Speed V’s Governance; Strategic Restructuring V’s Responsibility

The Leadership Tensions at the Heart of AI Transformation

Ask most senior leaders whether they feel on top of the AI transformation agenda and the honest answer is likely to be no. The scale of what is being asked is unlike anything in their experience. It is not one capability gap, but several converging at once. Each urgent, none clearly prioritised.

That is the difficulty with how AI transformation is often framed. The conversation tends to produce a list: AI fluency, governance, workforce redesign, commercial translation, systems thinking, speed, ethics. The implicit message is that all of it matters and all of it is needed now. For many executives, that feels less like clarity and more like overload.

The more useful question is not just what matters, but what matters most, and in what order.

Across leadership teams, a pattern is emerging. The organisations struggling to convert AI ambition into results are not those lacking investment or intent, but those unable to prioritise the tensions that sit at the heart of transformation. Two in particular stand out, because they consistently expose the gap between confidence and readiness.

Speed vs Governance:

Boards asked what they want from their leadership in an AI-augmented organisation are highly likely to prioritise speed, telling leadership to move faster; decide with less information; deploy ahead of competitors. In a market where AI capability is evolving faster than strategy cycles, the instinct to prioritise pace is understandable.

Investment patterns reflect this urgency.  Deloitte’s 2026 State of AI in the Enterprise report, drawing on over 3,000 senior leaders across 24 countries, found that 84% of organisations increased their AI budgets last year, with the dominant talent strategy being the acceleration of AI fluency across the workforce.

What the same data also shows is that the investment is not converting. Only one in four organisations have moved 40% or more of their AI pilots into production. Just 20% report high preparedness on talent. Revenue growth from AI remains an aspiration for 74% of organisations against a reality for just 20%. Fewer than half are making significant adjustments to their talent strategies, and more than a third are using AI at surface level with little or no change to existing processes.

It means money is going in, transformation is not coming out.

Moving quickly is not the same as moving effectively. The gap between the two is where executive reputations are currently being made or damaged.

This is where governance re-enters the conversation, however, often too late and misunderstood. The term itself still carries unhelpful connotations: compliance, overheads, constraint. As a result, it is frequently deprioritised in favour of visible momentum.

The evidence, however, points in the opposite direction. Organisations where senior leadership actively shapes AI governance consistently realise greater value than those that delegate it. Governance is not a brake on speed; it is the condition under which speed becomes safe, scalable, and defensible.

The regulatory environment has made this explicit. Frameworks such as the EU AI Act, alongside existing regimes like the UK’s Senior Managers and Certification Regime, are formalising accountability for AI outcomes. This is no longer abstract. If systems fail, whether through bias, data exposure, or flawed decision-making, the organisation is liable, and leadership is accountable. “The model did it” is not a defence that regulators or courts will accept.

Recent cases have reinforced this reality.

In February 2024, Air Canada was found liable after its AI chatbot gave a grieving customer incorrect information about bereavement fares. The airline argued the chatbot was a separate legal entity responsible for its own actions. The tribunal rejected this entirely. The case has since been cited across multiple jurisdictions as the moment the accountability gap in AI deployment became legally indefensible.

Contrast this with Robinhood’s approach to its AI-powered financial crimes investigation system, which built validation agents checking every output, full audit logs for regulatory explainability, and human oversight at every decision point. The result was a 20% efficiency gain in investigative workflows and a system that regulators can audit and leadership can defend.

The widely cited ruling by the airline chatbot providing incorrect customer information made clear that organisations cannot distance themselves from the actions of their AI systems. By contrast, organisations embedding oversight, auditability and human validation into AI decision-making are demonstrating that governance and performance are not in conflict, they are mutually reinforcing.

The leadership challenge, then, is not choosing between speed and governance. It is recognising that without governance, speed is fragile and often undermining.

Workforce restructuring vs responsibility.

If the speed-versus-governance dynamic is the most visible leadership tension in AI transformation, the workforce question is another that demands urgent and considered attention.  However, it is sometimes overlooked in the rush to drive efficiency savings through automation.

The economic logic for using AI to redesign operating models is clear. Automation, consolidation, and more AI-enabled roles can materially improve efficiency. On paper, the case is straightforward.  In practice, this is where financially rational decisions become leadership risks.

Organisations too often focus on those whose roles are removed or redefined, neglecting to mitigate the impact on those who remain. Organisations that restructure without a credible people narrative do not simply lose the people who leave, they can lose the confidence of those who remain.  With that, they may lose discretionary effort, institutional knowledge and the informal networks that transformation depends on.

The efficiency gain may be delivered, but the capability to build on it is often diminished.

This is where many transformation programmes quietly underperform. The structural change is achieved, but the conditions required for sustained performance are weakened in the process.

The capability required here is not empathy as a soft skill, it’s the ability to make difficult structural decisions with clarity and pace while maintaining the conditions under which high-performing people choose to stay and contribute. That combination is rarer than boards generally acknowledge and its absence is one of the less visible but more consequential reasons AI transformation programmes underdeliver.

There is a further dimension that receives less attention at board-level. The executives being asked to lead workforce redesign are themselves operating in an environment of considerable personal uncertainty. The roles being automated, consolidated or redefined are not exclusively below them in the hierarchy. For some, the capabilities that built their careers are among those the market is beginning to discount. This is a dynamic Rialto sees consistently in its work with senior leaders in transition – the difficulty of driving change with conviction when the ground beneath your own position is also shifting. Navigating it requires a degree of psychological clarity that technical upskilling alone does not provide.

This is not a reason to slow the pace of change. It is a reason to be deliberate about which leaders are positioned to drive it and what support the organisation is providing to those who are not yet there.

What This Means for Executive Leadership

The tension between speed and governance is often framed as a trade-off: move fast or govern well; compete or comply. Similarly, workforce transformation is framed as a structural exercise: redesign the model and execute.

The organisations that are translating AI investment into sustained value are not those choosing one side of these tensions. They are those whose leadership teams are resolving them, treating governance as an enabler of speed and workforce decisions as both structural and human challenges that must be addressed simultaneously.

PwC’s 2025 Responsible AI research found that 60% of executives said governance boosts ROI and efficiency while 55% reported improved customer experience and innovation as a direct result of responsible AI practices. Yet nearly half acknowledged that turning those principles into operational reality remained a challenge. The value of governance is appreciated, but many organisations are falling short when it comes to embedding it across functions and departments.

The organisations building resilience, innovation and enduring growth into their business models through AI transformation are those that understand which elements are load-bearing right now and need direct attention.

For most, that includes governance, workforce credibility and accountability for how restructuring decisions are made and experienced.

This is also where a more grounded view of executive readiness is needed. In ongoing work with senior leaders, and through current research into executive AI relevance, a consistent picture is emerging: confidence in certain areas, genuine gaps in others and a broader recognition that the demands are arriving faster than preparation.

The leadership task is to distinguish between what is urgent, what is foundational and where the risks of inaction are compounding in ways that are not yet visible on the surface.

A More Focused Question

For executives navigating this evolving landscape, the immediate question is whether they are prioritising the right tensions and addressing them in the right order.

The organisations that will look back on this period as a point of competitive advantage are unlikely to be those that moved fastest in isolation. They will be those where leadership teams made structural decisions at pace, embedded governance early and managed workforce transition without eroding the human foundations of performance.

One of the consistent challenges at executive level is the absence of an external reference point: a clear view of how peers are interpreting the same pressures, where they are placing emphasis, and where confidence diverges from actual readiness.

This is precisely the focus of current Rialto research into executive AI relevance. Through ongoing work with senior leaders, and a structured survey designed to capture how leadership teams are prioritising capability, risk, and investment, we are seeing an increasingly clear picture of where organisations are actually placing weight, and where the most material gaps sit.

The survey will provide a dataset which is missing in the current market. Findings will be shared in aggregated form with contributors, offering a more grounded view of how peers are navigating these same tensions, how they perceive and manage priorities. It will enable leaders to gain a clearer picture of how they fit into the broader landscape, both in terms of their own professional development and their organisational readiness.

For most, AI transformation is not constrained by awareness or ambition.  It is constrained by effective prioritisation in the face of the overwhelming pace of change and competing challenges.

At the centre of it all, the difference between progress and underperformance increasingly comes down to a single capability: the ability to decide what matters most and act on it first.

The survey remains open for a limited time and takes just five minutes. More details can be found here: Executive Relevance in the Age of AI.

“Harnessing machine learning can be transformational, but for it to be successful, enterprises need leadership from the top. This means understanding that when AI changes one part of the business, other parts must also change.” Erik Brynjolfsson, Stanford Institute for Human-Centered AI

Brynjolfsson is one of the world’s most cited economists on technology and productivity, a Stanford professor who has spent three decades studying what separates the few organisations that extract real value from transformative technology – which we will call the 6% club – from those that do not. He finds it an organisational issue: failure to consider the structural, governance and cultural changes needed to lead through AI transformation inevitably leads to under-achievement and disillusion.

Eighty-eight per cent of organisations globally now use AI in at least one business function, yet only around 6% qualify as genuine AI high performers – businesses attributing more than 5% of EBIT directly to AI and reporting significant value across the enterprise. The remaining 94% are somewhere between enthusiastic experimenter and quietly disillusioned pilot operator. Most have the tools. Very few have the results.

What the 6% are actually doing

These high performers do not have access to better technology. What distinguishes them is organisational. McKinsey found that high performers are 3.6 times more likely to be pursuing transformational, enterprise-level change through AI and nearly three times more likely to have fundamentally redesigned their workflows in the process. Bolting AI onto existing processes is a false economy that leads to wasted resources, lost opportunities and competitive drag. The 6% rebuild those processes around what AI can actually do.

They are also three times more likely to have senior leaders who actively own and champion AI, genuinely modelling its use and driving its integration into strategic decision-making. This is the strongest single predictor of enterprise-level AI impact in the data. When senior leadership treats AI as a technology upgrade, the organisation stalls. When they treat it as a strategic shift that requires them personally to change how they work, the organisation moves.

The high performers apply the same capital discipline to AI investment as they would to a major acquisition: clear strategy aligned with organisational objectives, defined milestones and criteria for adjusting or closing underperforming initiatives. They manage AI investment across three horizons: foundational infrastructure (two to four year payback), near-term productivity (six to twelve months) and longer-term transformation (ongoing). They do not allow short-term return pressure to collapse everything into the second horizon at the expense of the first and third.

The Kyndryl Readiness Report, drawing on 3,700 senior leaders, found that 61% of CEOs now face intensified pressure to demonstrate AI returns compared with the prior year, while 53% of investors expect positive returns within six months or less. Responding to that pressure by sacrificing infrastructure and transformation investment to feed short-term results is one of the primary reasons organisations get trapped in pilot purgatory. Honest, clear communication from the outset – managing expectations, helping stakeholders understand realistic timescales and reimagining how success is measured – is itself a leadership responsibility. Equally, so is recognising when to kill a pilot that is not working, and to explain why.

The governance gap

Two-thirds of organisations remain in experimentation or piloting phase, lacking the operating model maturity to convert deployment into value. The most common single failure is the absence of clearly named executive ownership for AI outcomes across product, legal, risk and compliance. When nobody is explicitly accountable for what AI is doing across the organisation – which McKinsey found to be the norm – innovation slows, risk accumulates and resources are wasted.

Most organisations view governance as a constraint. The 6% experience it as a competitive advantage: the mechanism that builds stakeholder trust, enables faster decision-making within defined boundaries and provides the audit trail that allows boards to demonstrate responsible operation to regulators, investors and customers.

Regional AI regulatory frameworks add further complexity. The EU AI Act is now in phased application, with penalties reaching 7% of global annual turnover for high-risk non-compliance. The UK places the burden of interpretation directly on boards, making personal executive accountability the operative principle. In the US, enforcement is arriving through litigation rather than legislation, making documentation, testing and explainability the primary risk mitigation tools. Working across different regions demands flexible compliance models, but across all three regimes AI governance is a board-level responsibility and the expectation that it can be delegated to IT or legal functions is no longer sustainable.

What boards and leadership teams must actually do

Moving from the 94% to the 6% requires coordinated evolution across five interconnected dimensions. Here are five questions your board should be able to answer:

Who in your organisation is accountable if your AI produces a wrong outcome? In most organisations, nobody can answer that. Executive accountability means designating named individuals responsible for AI outcomes across every relevant function – product, legal, risk, compliance and people – with those owners demonstrating AI literacy in capital allocation decisions.

Are you asking how AI could transform how this work is done, or just how to make existing processes faster? Workflow redesign is the single most powerful lever in the McKinsey data. High performers decompose roles into task sets, identify which activities are best automated, which augmented and which require human judgement, and rebuild performance metrics around value delivered rather than activity completed. (See our previous insight, Redefining Work in an Human/Machine Era.)

Is your AI training a one-off event or embedded into how people work every day? McKinsey’s data shows that high performers embed at least 81 hours of annual AI training per employee into operations. Sixty-three per cent of employers globally identify capability gaps as their primary barrier to AI scaling, yet most continue to look externally for capabilities that reskilling could develop internally at lower cost and with less disruption.

Have you defined what failure looks like before you start? Capital discipline with kill-switch criteria means defining in advance, at the point of approving any AI initiative, when a pilot gets shut down rather than scaled. The organisations accumulating the most expensive AI failures are those that never established what insufficient progress looked like.

Can you explain to every stakeholder – employees, customers, regulators, investors – exactly how AI is influencing decisions that affect them? Stakeholder trust architecture is an operational requirement, not a PR exercise. In an environment where 51% of organisations report AI-related incidents, eroded trust is difficult to rebuild. High performers are more than twice as likely to have defined human-in-the-loop validation processes – 65% versus 23%.

Measuring returns beyond the financial

McKinsey found that function-level returns in software engineering, manufacturing and IT regularly reach 10-20% cost reductions, with marketing and product development seeing revenue uplift above 10% in leading deployments. But the ROI conversation in most boardrooms is still too narrow. Organisations measuring only financial return are missing both the value and the risk.

Two thirds of organisations in McKinsey’s survey report AI-driven improvements in innovation capacity, while 45% report improved customer satisfaction and 36% see strengthened competitive differentiation. These are leading indicators of future financial performance. Organisations tracking only EBIT impact miss the earlier signals that tell them whether their AI investment is building the capabilities that will compound into revenue.

Stakeholder trust is measurable and its erosion is one of the most expensive and least discussed AI risks. Customer trust in AI-mediated decisions, employee confidence in the organisation’s approach to workforce impact and investor trust in governance quality all affect the cost of capital, talent retention and customer lifetime value in ways that do not appear in short-term financial metrics. Regulatory standing carries an implicit financial value that almost no organisation currently quantifies, and boards that require AI investment proposals to include a regulatory exposure assessment alongside the financial case are making a sound capital allocation decision, not an over-cautious one.

Leadership seeking to help their organisations break into the top 6% can learn much from the earlier pioneers — both what to do, and what not to do.

JPMorgan Chase: lessons learned in an $18 billion experiment

JPMorgan Chase is the most thoroughly documented example of an organisation in the 6%. Its AI programme has more than 450 live use cases delivering between $1.5 billion and $2 billion in annual value. More than 200,000 employees use its proprietary LLM Suite platform daily and AI-attributed benefits have grown 30-40% year-on-year. AI coding assistants have lifted developer productivity by 10-20% across a technology workforce of 63,000, its Coach AI advisory tool contributed to a 20% increase in gross sales in asset and wealth management between 2023 and 2024, while fraud prevention and operational efficiencies saved a further $1.5 billion.

What explains it? Not the technology. JPMorgan uses many of the same foundation models available to every competitor. What distinguishes the bank is its governance architecture: a firmwide Chief Data Officer mandate aligning data platforms with model risk management, legal and security functions across every business line; rigorous ROI measurement at the individual initiative level; and a board-level treatment of AI as a core operating function. As JPMorgan’s own Chief Analytics Officer put it: “There is a value gap between what the technology is capable of and the ability to fully capture that in an enterprise.” Their answer to that gap has been structural and the returns reflect it.

The bank also acknowledges the risks candidly: recouping the $18 billion investment will take time, and the technology comes at human cost, with a projected 10% reduction in operations headcount. Organisations carry an ethical and societal responsibility to mitigate those potentially significant losses.

MD Anderson Cancer Center: a $62 million structural failure

In 2012, MD Anderson partnered with IBM to build an AI clinical decision support tool for oncologists. The goal was to democratise world-class cancer care, giving any oncologist anywhere access to the diagnostic intelligence of one of the world’s leading cancer institutions. Five years and $62 million later, the contract expired before the system had been used on a single real patient. Inquests found the failure organisational rather than technological: the system was incompatible with existing platforms, scope had ballooned, the original six-month delivery timeline had been extended twelve times and no one with clear authority had been accountable for keeping the project within workable boundaries. It failed where JPMorgan succeeded – in governance, data foundation, accountability and the integration of human and technical design.

The window is narrowing

The gap between the 6% and the 94% continues to widen because AI advantage compounds. The organisations that have redesigned their workflows, built their people’s capabilities and embedded governance into their operating models are iterating faster and learning more with every cycle. Their data gets richer, their models improve and the distance between them and the organisations still running disconnected pilots increases.

The structural work needed – governance architecture, operating model redesign, talent investment, cross-functional accountability – is neither glamorous nor fast. The 6% understood this earlier than most. They made different choices, at the leadership level, about what kind of organisation they were building. That, ultimately, is the only gap that matters.

This insight is edited from a section of the first Rialto AI Business Leaders Circle Strategic Briefing of 2026, a biannual benefit of membership, which also includes the opportunity to help shape the future of AI in UK business with a seat at the table of the All-Party Parliamentary Group for AI (APPG AI) alongside MPs and other leading figures across government, academia and investment.

You can find out more about joining here

Transformation is now the default condition for growth-focused organisations. Whether driven by rapid digital innovation, continuous AI integration and recalibration, competitive disruption, regulatory shifts or strategic reinvention, modern businesses operate in a near-permanent state of change. For executives, the challenge is maintaining momentum while protecting the wellbeing and capability of their teams. Mastering this balance has become a defining leadership competency.

According to McKinsey, 70% of large-scale transformation programmes fail to deliver their intended value, with behavioural barriers, including resistance, weak sponsorship and inadequate change infrastructure, accounting for much of the shortfall. Bain & Company reports an even more sobering picture: only one in eight transformations meet their original ambition, while most experience some level of value dilution – figures unchanged for two decades.

The human cost is equally stark. In a global survey by Emergn, half of employees reported “transformation fatigue”, and 45% said the associated stress had led to burnout. Crucially, half of those experiencing fatigue had considered leaving their organisation. Failure therefore carries consequences far beyond the project itself—it diminishes trust in leadership and weakens organisational cohesion..

When people are overwhelmed, engagement falls, performance drops and trust erodes. More than half of employees feel that too much change is happening simultaneously, and 71% say they are overwhelmed by the volume of change in their roles. Even those not yet at burnout often show signs of chronic stress, including reduced satisfaction, impaired judgement and lower productivity.

To counter this, organisations must find equilibrium: preventing overwhelm while sustaining progress. When transformation is thoughtfully designed, with realistic targets, clear communication, visible milestones and strategic resource allocation, teams feel supported and energised rather than depleted. Groups that experience collaborative, well-paced cycles of change with intentional peaks and periods of recovery are better able to sustain the relentless rhythm of modern organisational life.

What to Expect in 2026

Looking ahead to 2026, several transformation trends are likely to intensify, and with them, the risks of burnout.

1. Generative AI and Automated Decision Workflows

Organisations will increasingly use generative AI to underpin decision-making, customer experience and operational processes. While the potential for efficiency is considerable, these shifts require new behaviours, redesigned roles and significant capability uplift. Without strong change leadership, AI initiatives may create confusion, destabilise teams and deepen fatigue. Over half of the employees surveyed in the Emergn research said AI-driven initiatives were increasing transformation fatigue, a sign of companies putting digital transformation in before properly preparing workforces. See our previous insights on AI-powered workforces and Leading in an Era of Agentic Intelligence.

2. ESG and Sustainability Imperatives

Environmental, social, and governance (ESG) imperatives will continue to reshape strategy, requiring greener supply chains, more transparent operations and more rigorous reporting. These changes demand both operational discipline and meaningful cultural evolution, not merely compliance.

3. Recalibration of Hybrid-Remote Operating Models

Hybrid work has moved from experimentation to optimisation. Organisations will further refine operating models, role expectations, productivity metrics and team structures.  Many are encouraging increased in-office presence to reduce silos, strengthen collaboration and intergenerational learning and mentoring. This will create ongoing organisational adjustment, particularly across globally distributed teams.

4. Ecosystem Partnerships and Platform Models

More organisations will build strategic ecosystems or platform businesses, partnering with technology firms, start-ups and new entrants. These transformations demand new governance new capabilities and new trust mechanisms, adding further layers of complexity.

Collectively, these forces mean 2026 is not simply another year of “large scale project’ transformation, it is likely to be defined by continuous, multi-dimensional transformation.

The Human Toll: Why People Burn Out

At the heart of transformation fatigue, executives must consider this psychological truth: humans have a strong preference for stability. Change disrupts mental models, routines and meaning. Sustained disruption accumulates into cognitive overload, diminishing engagement and increasing resistance

Middle managers can be particularly vulnerable. They translate strategic ambition into operational reality without always having the authority, time or clarity to shape the journey. When they become overstretched, entire transformation programmes stall.

Poor sequencing further compounds the strain. Anthosa Research shows that when organisations run more than seven major initiatives concurrently, failure rates climb to 83%. Prosci’s research highlights that frontline functions, Operations, Customer service, Sales, HR, experience the greatest change saturation.

Another common error is overburdening the same high-performers. “Star Players” are too often asked to carry disproportionate weight, leading to burnout and capability loss, while other talent remains underutilised. When organisations fail to manage human resources and capabilities deliberately and strategically, transformation efforts can stall. When too many initiatives run in parallel without deliberate resource management, engagement collapses and leadership sponsorship weakens.

Leading With Resilience: Key Principles for Executives

Senior leaders can protect teams, and themselves, from burnout by grounding transformation in a set of disciplined, evidence-based practices.

1. Diagnose deeply before acting

Begin with a rigorous diagnostic to understand organisational readiness, historical change load and pressure points before moving on to a bold vision.  Leading companies (Ford, Adobe, T-Mobile, Virgin Australia) explicitly manage organisational energy from the outset, recognising that it is often the true governor of transformation pace.

2. Pace change intelligently

Accelerating too fast is a common mistake. McKinsey finds that organisations adopting structured, sequenced transformation actions can more than double their success rates. Build in hybrid phases where old and new systems run in parallel, giving people space to adapt.

3. Communicate relentlessly and with purpose

Ambiguity is the enemy of transformation.  Teams need repeated clarity on the rationale, process, expectations and available support. Recent research shows that only 53% of managers and 40% of employees understood the transformation underway—despite 68% of leaders believing they had communicated clearly.

Employees expect senior leaders to articulate the vision, but rely on line managers to translate it into personal relevance. Both layers must be aligned.

4. Empower people and build ownership

One of the most effective ways to reduce burnout is to involve people meaningfully. When people have a voice and help shape change, they’re more invested and better able to absorb the disruption. Create structured forums where concerns can be voiced without fear. High-trust environments result in employees being 2.6 times more capable of absorbing change.

5. Manage capacity

As a leader, you must be ruthless about prioritisation. Transformation pressure naturally invites competing demands. Decide what must pause while new ways of working emerge. It is essential to be able to deprioritise “business as usual” when transformation peaks and communicate these choices clearly.

6. Celebrate early wins.

Small victories help sustain energy and provide tangible proof of progress. Recognising teams publicly for achieving milestones fuels morale and provides a narrative of collective achievement. Research by Bain shows that companies using aspirations rather than benchmarks to set goals (and celebrating progress toward those aspirations) maintained organisational energy more effectively.

7. Lead without neglecting yourself

You set the tone, so you must also guard your own resilience. That means setting boundaries, protecting time for rest, and crucially, building a network of support. As pressures mount, consider executive coaching or peer-group reflection to maintain perspective and prevent burnout.

The Role of Coaching and Reflection

Transformation leadership is highly demanding. Executives who engage in structured reflection whether through executive coaching, peer groups or mentorship, tend to lead with greater clarity and endurance.

One of the biggest mistakes executives can make in times of intense pressure is to cut out any activities they see as luxury and invest all their energy and time into the project as deadlines loom and inevitable complications arise.

The most confident, assured and effective leaders recognise the value of stepping back to allow both downtime – during which creativity can thrive, ideas can percolate and problem-solving can be more effective – and time for honest appraisal with a trusted and knowledgeable sounding board/mirror.

The latter will provide confidential space to test tricky decisions, process doubts and sustain strategic discipline. A coach helps you recognise when you’re pushing too hard or losing balance, and supports building a leadership practice that is resilient over a career, not just a single project or even position.

Research with successful transformation leaders (including CTOs at Dell Technologies, Desjardins, International Paper and global insurers) consistently finds that external perspective helps leaders maintain the energy required for multi-year change journeys. These leaders emphasise that energy needs to be cultivated and managed deliberately. Coaching provides structure for that discipline.

Practical Habits to Build Resilience

A few regular practices can materially improve transformation endurance:

Weekly priority reset: At the start of each week, pick three transformation-critical outcomes. Everything else is secondary. Successful transformations build change into the company’s operating rhythm rather than treating it as separate from normal business.

Frequent feedback loops: Hold fortnightly check-ins with key stakeholders and use them to engage with teams; gauge morale, anxieties, confidence and buy-in. This helps leaders spot the early signs of change fatigue: shorter tempers, physical exhaustion, increasing absence, falling energy and enthusiasm, rising anxiety and resistance, both active and passive. Praise individuals and teams when it is due but avoid singling them out for blame. Where things have gone wrong, explore what can be learned and invite feedback on how they can be improved.

Share progress through visual symbols: Use dashboards, graphs and other visual artefacts to mark smaller wins and track progress. Seeing movement and momentum builds hope and endurance. This is particularly important at the transformation midpoint, when energy is most likely to dip.

Built-in recovery: After major phases, intentionally pause for consolidation, learning and a reset. Encourage teams to reflect on what went well and how challenges were met. Companies achieving successful transformations treat change as continuous but rhythmic, with periods of intensity followed by consolidation.

Leading for the Long Game

Transformation is no longer episodic.  It is a permanent feature of corporate life that is not delivered by intensity but by endurance. Leaders who guide their organisations through meaningful change without burning out their teams understand that pace, rhythm, and energy are strategic assets. They resist the lure of heroics, building ways of working that enable people to contribute at a high level without running on empty.

Leading for the long game means treating change as an ongoing capability, something that must be fuelled, protected, and renewed over time. This requires strategic clarity, psychological insight, disciplined prioritisation and the humility to recognise human limits. It calls for an operating rhythm that creates space for focus rather than overload, setting goals that stretch without overwhelming, and the deliberate management of organisational energy with the same seriousness applied to budgets and timelines.

The long-term value of getting this balance right is immense: resilient teams, meaningful capability uplift and the organisational stamina to transform again when the environment shifts.

If you are leading transformation now or planning one for 2026, this is the moment to invest in thoughtful design, purposeful communication, coaching and reflection. These are not ancillary, they are foundational to sustainable, repeatable success.

In this second part of our three-part series on upskilling for the AI era, we explore the distinct AI skills needed by today’s executives and how they fit into any ongoing programme of professional development.

Whether making a personal executive transition, receiving executive outplacement or driving organisational transformation, AI literacy is now an essential skill that should be considered as part of any development or change initiative. Executives who integrate AI mastery into a continuous learning agenda, spanning both personal and organisational transformation, will remain competitive and relevant in a rapidly evolving landscape.

As highlighted in our previous insight on how executives can stay ahead of the AI curve, of the $30 billion spent on AI globally, only 5% is seeing a return on investment. T his may be partly due to metrics and measurements not catching up with what success looks like, but progress is too often also impeded by executives’ glacial response as the technology accelerates exponentially in real time.

As former Cisco CEO John Chambers observed, half of executives “won’t have the skills to adjust to this new innovation economy driven by AI because they were trained to move at the speed of a five-year cycle as opposed to a 12-month cycle.”

Senior leaders therefore need to continuously reinvent themselves to stay aligned with the pace of technological evolution.

Building the right AI competencies

Below, we look at specific AI skills sets for executives who face distinct requirements when building AI competency. This guide provides an overview of core AI skills executives should consider acquiring and examines how training can be incorporated into broader leadership development strategies.

Skill 1: AI Strategy, Appraisal and Value Framing

Why it matters: Executives must identify where AI creates measurable return, build business cases and sequence pilots into scaled capability, recalibrating and updating according to technological advances which may otherwise outrun specific projects and lead to shareholder value erosion through misaligned investments or missed opportunities. Leaders who map use cases to financial outcomes gain competitive advantage.
Related competencies: Strategic foresight, scenario planning, critical and creative thinking.

Skill 2: AI Governance, Risk and Compliance

Why it matters: Boards and C-suites are prioritising governance, auditability and regulatory readiness amid a fragmented regulatory landscape, where inadequate oversight can expose organisations to severe fines or reputational damage from incidents such as bias scandals. Governance is a rising board agenda item, helping attract top talent through ethical practices and building resilience by managing the inherent complexities of scaling AI, while fostering ESG alignment and stakeholder trust.
Related competencies: Stakeholder collaboration, ethical decision-making, resilience.

Skill 3: Data Literacy and Decision Science

Why it matters: Executives who interpret model outputs, ask the right questions of data teams and set measurable KPIs are more effective sponsors of AI projects. This skill facilitates literacy in relation to decision frameworks, enabling navigation of volatile markets and bridging analytical gaps for informed sponsorship, particularly when aligning with UK initiatives around data protection and digital information that demand robust, privacy-conscious handling.
Related competencies: Data governance, analytical and critical thinking, cultural sensitivity.

Skill 4: Generative AI Literacy and Prompt Design

Why it matters: Executives need practical fluency with generative tools so they can assess vendor claims, pilot real workflows and set safe guardrails, unlocking productivity gains while mitigating risks such as hallucinations leading to flawed decisions or unintended outputs. Amid the rise of multimodal trends, this becomes essential for integrating tools like enterprise Copilots and scaling pilots without misuse, in line with UK recommendations for safe adoption that emphasise responsible experimentation and organisational safeguards.
Related competencies: Strategic foresight, ethical decision-making, change management.

Skill 5: People Leadership for Augmented Work

(Part three of this series will examine workforce upskilling.)
Why it matters: Adoption failures arise when leaders treat AI as a technology or tooling problem rather than one of people and process change, overlooking the human elements of redeployment and upskilling that can enhance team creativity and improve retention in blended workforces. This fosters resilience in hybrid AI-human environments, addressing the transformative shifts in job roles and skills needs, and ties into broader workforce strategies. Leadership skills supporting redeployment and upskilling are flagged in employer surveys as essential.
Related competencies: Strategic workforce foresight, stakeholder collaboration and influence.

Skill 6: Responsible AI and Ethics

Why it matters: Bias mitigation, explainability and responsible deployment are areas where executives must make trade-offs between speed and trust. Courses increasingly include practical governance frameworks to support these decisions.
Related competencies: Ethical judgement and integrity, strategic foresight and systems thinking.

From learning to leadership practice

Developing the above competencies requires structured and intentional learning. The next step is therefore understanding how executives can build and apply them effectively. While AI learning opportunities are widely available, their effectiveness depends on context and application. As with learning a new language, the greatest value comes not from theory alone but from practical use and cultural understanding.

A range of flexible programmes now support executives in building these capabilities. Some offer on-demand, video-based content with downloadable certification (e.g. LinkedIn Learning, Microsoft, DeepLearning.AI). Others blend live instruction with self-guided modules or in-person engagement.

However, without strategic framing, such courses may lack the nuance required to translate learning into leadership impact. Incorporating executive coaching or providing structured professional development can help align AI learning with transition goals, business transformation objectives, and broader leadership capabilities such as ethics and human-first implementation.

Learning formats: matching goals and learning style

A wide spectrum of AI learning options is available to meet different executive needs, schedules, and learning preferences. To optimise the benefits of AI education, Rialto consultants recommend beginning with compact, high-quality micro-courses for immediate familiarity, followed by targeted intensive programmes aligned to sector or functional priorities. Ongoing micro-learning and peer discussion groups can then sustain progress.

Bite-size and micro-learning courses provide rapid, low-cost access to foundational AI literacy, typically requiring a commitment of four to twenty hours. They are particularly effective for boards and senior teams seeking immediate fluency, offering practical exposure to areas such as prompt engineering and vendor assessment. These short, modular courses, available from providers such as DeepLearning.AI and LinkedIn Learning, make learning highly accessible and inclusive. However, they generally offer limited depth in areas like governance, data architecture, and strategic trade-offs, and they tend to provide fewer networking opportunities or weaker credentials. As a result, they are best suited for establishing baseline literacy, developing tool-specific competence, or supplementing more intensive development initiatives.

For leaders seeking deeper engagement, intensive executive AI programmes offer a more comprehensive approach, often spanning three to eight weeks. These programmes address advanced themes such as AI governance, data architecture, vendor strategy, and organisational change management, while also enabling participants to build peer networks with other senior leaders. Providers such as MIT Sloan, Harvard Business School, Oxford, and Wharton offer faculty-led experiences with access to implementation playbooks and sector-specific case studies. Although these programmes require a higher time and financial investment, they provide the strategic depth and board-level perspective essential for developing AI maturity across organisations and for positioning executives for future leadership transitions.

Sustaining relevance through responsible AI Leadership

As AI continues to redefine the leadership landscape, executives who commit to continuous, structured learning will be best placed to lead responsibly, transform their organisations, and remain relevant through disruption. AI fluency is not an isolated technical skill; it is now a cornerstone of strategic foresight, ethical leadership, and cultural adaptability. Embedding AI capability within broader professional and organisational development enables leaders to make informed, values-driven decisions that build resilience and trust in a rapidly evolving economy.

Rialto supports this journey through its programme of complimentary invitation-only events  exploring AI and leadership topics. With 85 consultants operating globally, Rialto helps executives strengthen leadership capability, navigate transition, and align AI learning with strategic transformation goals.

Executives can also contact our research department for examples of leading AI learning programmes and providers—including Harvard Business School, LinkedIn, Deloitte, and others—that Rialto clients have successfully undertaken. To learn more, email research@rialtoconsultancy.com.

It is difficult to believe that just two years ago, only data scientists had really heard of Generative AI, the form of artificial intelligence that can create content, images and code; summarise vast amounts of data and extract insights according to prompts; identify patterns and illuminate concepts, stimulating creativity and filling vast gaps in our knowledge.

Below is The Rialto guide to the five stages of AI maturity – and actionable steps to help executives guide their organisations safely and strategically to a place of seamless integration and augmentation delivering growth, efficiency, improved services and products and innovation.

The arrival of the first open source ChatGPT in November 2022 led to a more dramatic transformation of the business landscape than any previous innovation and continues to evolve at a dizzying pace, offering unprecedented opportunities for growth and development.

For executives and senior leaders this has presented a new set of challenges – how to guide your organisation through this revolution at the right time, at the right speed, with the right platforms and end uses. Too fast and you risk destabilising your business model and workforce, buying into the hype, overspending on under-developed products and creating an expensive mess. Too late and you risk falling too far behind to catch up, allowing competitors to gain a defining edge.

At Rialto, we support executives to understand the five stages of AI maturity, creating a personalised roadmap aligned to their organisation’s strategic objectives, budget and culture. Those stages go from scepticism/ nascent awareness – the tentative first steps – to maturity, where AI is integrated into all relevant parts of the business, staff are trained and on board, business development and growth are driven by data and insights and a robust governance and ethics framework ensures Gen AI and other emerging technologies are being used safely.

Here is a brief outline of that map, the percentage of organisations at each stage of maturity in 2024* and some of the actions the Rialto team encourage executive teams to take at each stage.

Stage 1: Nascent awareness/scepticism. 26% in 2024

Organisations at this stage may be eschewing AI altogether or understand its potentially profound impact on growth and operations but lack a clear plan or strategy to move forward. Perhaps AI champions are meeting scepticism or fear from key leadership and stakeholders. According to various surveys, the percentage of organisations at this stage was anything between 43% and 65% in 2023, compared to the 26% figure quoted above in 2024 showing the speed at which organisations are moving on. To avoid losing ground and potentially destabilising your business’s future, our team recommend the following steps are taken as a wait and see approach in 2025 will be considered a high risk strategy.

Leadership Action

Prioritise Education and Vision: Leadership must start by educating themselves and their teams about AI’s capabilities and potential. Rialto works with c-suite leaders to support them through this vital first stage with confidence and ensure no wrong turns are taken.  Time is now at a premium, the journey into an AI-driven future must be clearly mapped out with strategic objectives at the fore to catch up with the field without rushing into critical mistakes.

Prepare your workforce: Bring in AI experts and facilitate open, two-way discussions across departments to ensure a smooth, carefully choreographed entry. Invest in training to increase awareness and understanding among employees. Invite feedback and act on it – only 17% of companies do so at this stage. Ensure your biggest asset, your people, are at the forefront of this journey throughout. Collaboration, confidence and co-operation are essential.

Evaluate the Market and Competition: Leaders should analyse competitors to identify how AI is shaping their industry landscape and assess their own position.  Rialto has partnered with a number of executive teams to build their business case for AI investment by highlighting both risks and opportunities to help ensure buy-in from all stakeholders.

Formulate a High-Level AI Vision: Leadership should articulate a clear vision of what AI could potentially achieve for the organisation. This doesn’t have to be fully fleshed out but should set the stage for future AI initiatives.

Assess Current Data Assets: Leadership should work with data teams to audit available data and build systems that can collect and analyse clean, relevant data aligned to strategic objectives as this will be key to future AI success.

Identify Low-Hanging Fruit. What are the best and least complicated first case uses? Seek repetitive, time-consuming, administrative tasks that could be streamlined; customer service portals that could be automated. You may seek external support as well as working with c-suite and data teams to build new AI-led systems that will show instant results with minimal risk to build confidence and demonstrate value.

Stage 2: Experimental/Activation. 41% in 2024

Two fifths of organisations assess themselves to be in this stage, experimenting with AI technologies to address specific opportunities or challenges. This phase is entrepreneurial and opportunistic with a focus on learning, testing agility of existing business models and data. More conservative, risk-averse companies might feel most comfortable at this stage now, trialling and preparing their organisations for more wide scale transformation in the next two to five years.

Leadership Action

Set Up Pilot/Proof-of-Concept Programmes: Identify and define high-potential, simple to run end uses and trial small, measurable projects within a controlled environment. Emphasis should be on analysing results and interaction with systems and employees, iterating and identifying areas which need to be adapted and developed before bigger, more risky projects are explored.

Foster a Culture of Experimentation: Leadership needs to encourage a mindset where small-scale failures are viewed as learning opportunities. Our project teams often find successful organisations treat their AI pilots as experimentation cycles rather than one-time projects. Ensure all learning is documented, analysed and applied to future projects and scaling up.

Identify Key Metrics for Success: To that end, establish clear KPIs to continually evaluate the success of these AI experiments, whether improved efficiency, growth, cost reduction or customer or employee satisfaction. Iterate based on feedback. Report to stakeholders, demonstrating transparency, feasibility and value.

Develop Cross-Functional Teams: Include IT, data scientists if you have them, business unit leaders, and compliance officers in pilot teams to ensure pilots are practical, scalable, and compliant. Smaller companies may wish to hire consultants.

Invest in AI Talent: As experimentation ramps up, recruiting AI specialists or upskilling existing employees will become crucial. Maintain open dialogue with existing staff and look for opportunities to upskill to foster trust.

Stage 3: Foundation Building, Investing in Infrastructure and Data, Wider Experimentation. 2024: 14%

This is where we start to enter the more advanced stages inhabited by earlier adopters.   These may tend to be enterprise and innovative and/or tech companies which have a clear understanding of how AI can benefit their operation and have defined processes for implementation across the organisation.  Leadership understands the need for robust infrastructure, data governance and AI talent.  According to a Gartner report, 80% of AI projects will fail to scale by 2025 if companies don’t build a solid AI infrastructure.

Leadership Action:

Introducing wider AI projects: Those low-hanging fruit identified in stage one should now be going live and being closely monitored for governance, security, quality, impact and ROI. Our clients have tended to scale up first in operational optimisation, customer support and marketing analytics and content creation. If your organisation does not have its own AI ecosystem in place, with a c-suite leader taking accountability and data leads in relevant teams, it might be an idea to bring in external consultants to maintain dedicated oversight and advise throughout this process.

Data Strategy & Infrastructure Investment: Leadership should prioritise building a scalable data infrastructure. This includes investing in cloud computing, data lakes and the tools necessary for managing large datasets. Test platforms for compatibility, robust compliance, cybersecurity, customer service and ease of use before scaling up.

Focus on Data Governance: As AI thrives on data, it’s imperative that leaders ensure that data collection, storage, and usage adhere to regulatory standards. Data governance frameworks must be implemented to guarantee AI models are ethical, secure, and transparent.

Recruit Specialised Talent: In this stage, it’s essential to have the right expertise to scale up and optimise AI use. Leadership should seek to build teams of data engineers, machine learning experts and AI project managers or bring in consultants.

Consider Establishing a Data Governance Committee: Form a committee to ensure data privacy, quality, and compliance are central to your AI operations. Ensure accountability and transparency.

Promote Data Literacy: As AI permeates every level of the business, leadership should invest in data literacy programs to ensure employees at all levels understand how to share relevant, clean data with the knowledge base and interact with and interpret AI outputs.

Stage 4: Strategic Scaling Stage, Deploying AI Across Functions, Optimisation. 12%

Just one in eight are at this stage and those that are here and beyond – having reached it carefully and in alignment with strategic objectives – are reporting promising results with efficiency savings, growth and vastly improved products and services.  At stage 4, AI moves from pilots to full-scale deployment across multiple business units. Agility is built into business models to continuously adjust and adapt. The workforce should be growing in confidence, with AI integrated into their routines and actions.

Leadership Action:

Consider Investing in AI Platforms and replacing some legacy systems: Seek advice on which platforms to use and look at what competitors are doing with them. You may wish to invest in a single Gen-AI powered CRM such as Amazon AWS, Salesforce or Hubspot, and bolt-on other application and tools – or have your data scientists build your own using open access Generative AI models. Continuously monitor ROI and build a relationship with the provider to ensure constant adaptation and improvement or consider alternatives.

Align AI with Business Goals: Always start with objectives, not the technology, to avoid buying into hype. New models and platforms and iterations are coming on to the market daily. In this second wave of adoption, consider prioritising high-impact areas such as supply chain management, customer personalisation and fraud detection.

Consider Creating an AI Centre of Excellence (CoE) and/or Data Governance Committee: Establish a centralised AI committee or CoE that drives AI strategy, oversees technology deployment, and ensures best practices across the organisation. An ecosystem should now be in place with c-suite responsibility and accountability and company policies, guardrails and training for anyone in knowledge-based or customer facing roles in risk and compliance issues such as data security, copyright infringement, GDPR, inaccuracy and bias.

Leverage Data: Use analytics to gain insights, drive decision-making and continuously improve your offering, operations and forward planning.

Stage 5: Maturity, 7%.

Organisations at Maturity stage will find that AI has become a core component of the organisation’s DNA, integrated into the very fabric of the company, driving every aspect of decision-making and enabling continuous innovation. According to a study by Accenture, the few businesses in this stage outperform their competitors by three times in terms of profitability. In the most extreme of AI streamlining exercises, Meta boosted net income by 201% and saw a 178% stock surge by focusing on AI operational efficiency. However this came at a cost of 21,000 jobs. Companies that have successfully reached the fifth stage of maturity find themselves in a virtuous cycle of continuous improvement while employees understand the value of data and are guided by AI copilots in everything they do. AI augments every task, function and team. Employees are 1.5 times more likely to view AI as a helpful colleague. This shift in perspective leads to increased AI usage, enthusiasm, and productivity gains. Impact is assessed and ROI and value are demonstrable and measurable.  Robust guardrails are in place to minimise and mitigate risks.

Leadership Action:

Foster Continuous AI Innovation: There is no time to rest on laurels. Other organisations are catching up and technology is constantly evolving. Leadership must keep pushing the envelope by encouraging teams to innovate continuously. This may involve AI-powered R&D initiatives or the adoption of cutting-edge technologies such as AI-generated content, AI-driven customer experiences and autonomous systems.

Evolve Organisational Structures: Leaders should ensure that the organisation is agile enough to respond to the fast-paced changes in AI technology. This may involve restructuring teams, constantly upskilling or creating new or hybrid roles.

AI in Strategic Decision-Making: Make AI a critical player in c-level strategic decisions, using AI-driven insights to predict market trends, customer needs, enhance supply chain and internal operational efficiencies.

Stay Ahead of AI Trends and New Tech Offerings: Leadership must stay abreast of the latest AI advancements such as generative AI, reinforcement learning, or edge AI, and adapt them to their current and future business models. Rialto supports c-suite leaders at this stage to maintain a bigger picture perspective, stay focused on future and personal/professional development.

Benchmark Against Industry Leaders: Continuously evaluate your AI maturity against the best-in-class organisations to identify new areas for AI-driven growth.

Maintain virtuous cycle of improvement: Ensure data analytics feed into continuous development and growth. Maintain a constant state of innovative evolution. Sustain and expand capabilities and use cases.

Ethical Considerations: Never lose sight of the governance and ethical risks and responsibilities. Build in continuous reviews and ensure continuing compliance with changing regulations in different territories.

Benchmarking your organisation against the above five stages will provide a clear indication of where you and your company lie on the path to AI maturity and the steps you may need to be considering accordingly. It’s clear that organisations are moving through this process at an ever-faster pace, reflecting the growing importance of AI in today’s business environment.  Yet with 41% of organisations now in the experimental stage and only 7% in full AI maturity, there remains a significant opportunity for competitive growth.

While cost reduction and efficiencies may be the primary goal of immature adopters, high performers are twice as likely to have shifted into a phase of innovation where they use Gen AI to create new businesses or offerings, expand into new sectors or regions armed with detailed insights and the confidence of likely success gained with reliable analytics extracted from vast lakes of data.

The shift from initial scepticism to full AI integration can happen faster than many expect with the right approach. Whether you’re just starting out or already experimenting with AI, having a clear roadmap and a focus on continuous innovation will enable your organisation to progress rapidly and stay ahead. The question isn’t whether you should move up the stages, but how quickly you can—and will—do so.

Whatever stage you are at, The Rialto team of experts can help guide you and your organisation to maturity at a pace that suits your culture, while ensuring a human-centric focus, bringing your people on this journey with you. Contact us for a free initial consultation if you would like to know more.

*Asana and Anthropic State of AI at Work study        

As we approach the final quarter of 2024, executives and board directors find themselves at a pivotal juncture. Q4 is not just the culmination of the year’s efforts but also a critical period that sets the stage for the year ahead.

So, as we crunch back up through the gears following the summer slowdown, now is the time for all top tier leaders to look forward, evaluate priorities and ensure all functions and teams are strategically aligned to ensure resilience and growth.

Here we look at what should be top of mind for CEOs as they steer their organisations through the final stretch of 2024.

Almost two thirds of CEOs singled out growth as their top business priority for 2024 in a Gartner survey, the highest level for a decade and up from a half the previous year. This indicated a renewed confidence in the economy after years of uncertainty, stagnation and hesitancy.

The green shoots have been slower to emerge than many had hoped, however there are now definite signs that the most challenging times are behind us, barring any further disruptions such as an escalation of conflict in the Middle East or Eastern Europe.

How can senior leadership then optimise the last months of the year to set their organisations up to seize opportunities as we navigate the interplay of different factors that paint numerous possible futures for the year to come. This includes continued shifts in the global economic environment, how businesses organise themselves across sectors and the continued evolution of digital technologies, their adoption by consumers and the impact on business operating models.

Re-evaluate and Adjust Strategic Goals

This is the perfect time to reassess your company’s strategic goals set at the beginning of the year. Are they still relevant in the current market environment? What has changed in your sector, organisation or the wider economy in that time? Have new opportunities emerged around disruptive technologies, sustainability, for example, or have new markets come to the fore?

Key Actions: Conduct a strategic review with your executive team, focusing on KPIs, market dynamics, and potential pivots. If necessary, recalibrate your strategies to ensure alignment with the latest market realities.

Strengthen Financial Management

Q4 is traditionally a time for financial housekeeping. What are the potential economic challenges on the horizon and how can you best prepare? In what direction are market trends pushing you? Do you need to think about adjusting spending priorities to capitalise on any emerging opportunities and shore up any under-resourced, strategically important areas? Have inflationary pressures changed since your targets were set at the end of 2023? Do your end of year plans still reflect energy prices, transport, wages and any investment needed to bring your organisation up to date with evolving technologies?

Key Actions: Work closely with your CFO to monitor and optimise cash flow, reduce unnecessary expenses and ensure your company is in robust financial health. Review pricing models to ensure they reflect current market conditions and protect margins. Has Generative AI changed your sector and do you need to adjust spending priorities to stay ahead of the curve?

Enhance AI Transformation Initiatives

AI – and particularly Generative AI – transformation is displacing digital transformation as the keyword CEOs mention most as the major driving force behind business growth. AI is moving from the “peak of inflated expectations” in the hype cycle but it is essential that adoption is measured, carefully considered from the point of added value and ROI, and avoids buying into hype. Technology-based initiatives must also be continuously evaluated and updated to keep pace with technological advancements. Q4 is a critical time to assess the progress of your AI transformation efforts and identify areas that require further investment or adjustment. Whether it’s AI integration, cybersecurity, or cloud computing, ensuring that your tech infrastructure is robust, aligned with strategic objectives and future-proof is essential.

Key Actions: Audit your AI transformation progress and invest in key technologies that can drive efficiency and innovation. Look at what is happening in your sector – what is the competition doing? What technological advances are on the near and far horizon? Maintain a dynamic strategy to fully optimise all opportunities. Ensure your company’s cybersecurity measures are up to date to mitigate any potential risks.

Focus on Customer Retention and Engagement

With budgets being finalised for the next year, ensuring that your current customer base is satisfied and engaged can significantly impact your financial projections. Are you doing enough to ensure customer loyalty and meeting their evolving needs. In today’s competitive market, with access to high-quality instant customer service and highly personalised marketing and interactions powered by predictive analytics and data-driven insights, B2B and B2C customers and clients expect more than ever. What is the competition doing? Are you allowing your offering to slip behind? What can you do to accelerate your data collection, analysis and application to sales and customer experience?

Key Actions: Implement targeted campaigns to boost customer engagement leveraging data analytics to personalise outreach and offers. Consider customer feedback loops to continuously improve the customer experience.

Prepare for 2025: Strategic Planning

While Q4 is about closing the year in a position of strength and growth, it’s also about setting healthy foundations for continuing growth next year. CEOs should lead the charge in developing strategic plans for 2025, considering emerging trends, potential disruptions and growth opportunities. This includes evaluating market expansion possibilities, identifying new revenue streams, and planning for talent acquisition or development to support future growth.

Key Actions: Begin the strategic planning process for 2025 involving key stakeholders across the organisation. Focus on building a roadmap that balances short-term objectives with long-term vision, ensuring your company is prepared for the challenges and opportunities of the coming year.

Sustainability and ESG Initiatives

Environmental, Social, and Governance (ESG) factors are becoming increasingly important to every sector. In Q4, CEOs should assess the progress of their sustainability initiatives and ensure they align with both regulatory requirements and stakeholder expectations. This quarter is also an opportunity to set more ambitious ESG goals for the coming year. Failing to meet requirements can be costly, both financially and in terms of reputational damage.

Key Action: Review your company’s ESG performance and make necessary adjustments to meet or preferably exceed industry standards. Engage with stakeholders to communicate your sustainability efforts and future commitments and ensure they are onboard and, where necessary, compliant.

Mitigate Supply Chain Risks

Supply chain disruptions have been a significant challenge for many companies in recent years. Q4 is a critical period for reviewing that your supply chain is resilient, giving you value for money and can withstand potential disruptions. This might involve diversifying suppliers, increasing inventory levels or investing in supply chain technologies to enhance visibility and responsiveness. Again, CEOs should be looking at potential AI solutions to maximise efficiency of supply chain and logistics.

Key Actions: Evaluate your supply chain risks and take proactive measures to mitigate them. Consider building stronger relationships with key suppliers and investing in technologies that provide greater supply chain transparency, reliability and efficiency.

By prioritising these key areas with their Executive Team and using Q4 to review and adjust strategies and objectives, CEOs can confidently steer their organisations to year end on a high, with strong foundations laid for further progress and growth in 2025. As the business landscape continues to evolve, staying agile and proactive will be key to navigating the complexities of the complex modern market.

Rialto has a team of experts in Generative AI and other emerging technologies who can develop an action plan specific to your business and strategic priorities. Get in touch for a free initial consultation.

Rialto Consultancy and Rainbird Technologies Announce Strategic Partnership to Deliver Transformative AI-Powered Solutions

Uniting AI-powered decision intelligence and expert-guided transformation to elevate business success.

London (UK), May 22nd, 2024 – Rialto Consultancy, a leading global provider of change management, organisational transformation and multi-level employee career transition solutions, and Rainbird Technologies, the pioneering Decision Intelligence platform, today announced a strategic partnership to bring groundbreaking AI capabilities to enterprises.

This alliance combines Rainbird’s advanced, explainable AI technology with Rialto’s deep expertise in guiding organisations through complex people-oriented change initiatives. Together, the two organisations will empower clients to harness the full potential of AI to drive innovation, improve decision-making, and unlock unprecedented business outcomes through a compelling joint solution focussed on considerate AI adoption and workforce upskilling and transition priorities.

“We are thrilled to partner with Rainbird and leverage their cutting-edge decision intelligence platform,” expressed Richard Chiumento, Rialto Director. “By integrating Rainbird’s AI-powered insights and automation into our comprehensive people focussed change management solutions, we can help our clients navigate transformation with greater speed, precision and transparency.”

The partnership will focus on a joint go-to-market strategy, enabling Rialto to seamlessly incorporate Rainbird’s solutions into its portfolio of services. Clients will benefit from a unified value proposition that combines Rialto’s expertise in areas such as talent development, change communication, global career transition employee upskilling, with Rainbird’s ability to digitise human expertise, automate complex decisions and provide auditable explanations for AI-driven outcomes.

“Rialto’s deep understanding of organisational change and their proven track record of driving successful transformations make them an ideal partner for Rainbird,” emphasised Rainbird CEO, James Duez. “Together, we will empower enterprises to embrace AI-powered decision-making that is aligned with their strategic objectives and values.”

The partnership will initially focus on joint go-to-market efforts in the United Kingdom, with plans to expand into additional global markets in the future. By leveraging each other’s strengths, Rialto and Rainbird are poised to redefine the way organisations navigate change and harness the full potential of AI.

About Rialto Consultancy

Rialto Consultancy is an award-winning global provider of change management and organisational transformation solutions. With a focus on talent development, change communication, global career transition and employee upskilling, Rialto partners with enterprises to guide them through complex change initiatives and unlock their full potential.

For more information, visit https://www.rialtoconsultancy.com/.

About Rainbird

Rainbird’s revolutionary Decision Intelligence platform is transforming enterprise decision-making with trust and explainability at scale. For over a decade, Rainbird’s AI platform has enabled organisations to digitise human expertise into enhanced, yet transparent, extended knowledge graphs. Their advanced reasoning automates complex judgments at scale while providing auditable rationales behind each outcome.

For further details, explore https://www.rainbird.ai.

Press Contact

Rialto Consultancy: Justine Smith, Press@Rialtoconsultancy.com

Rainbird Technologies Ltd: Sabu Samarnath, sabu.samarnath@rainbird.ai

“Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window.” Peter Drucker (a founding father of modern management theory)

If death and taxes are the only certainties upon which we can rely, the best we can do is consider and interrogate the likely developments of the near future and prepare our business models and personal career growth plans accordingly.

Here are some of the major themes that the Rialto team believe will influence the economy and global executive job markets in 2024.

1: Optimising AI.  

If 2023 was the year that early adopters got ahead and most doubters came round to the reality that generative AI is here to disrupt every part of our lives and many occupations, 2024 will see a more mature, pragmatic and strategic integration of frontier technologies into all aspects of work automation priorities.

It is imperative that executives prioritise the need to ensure senior leadership are bringing in the talent or hiring expertise to identify the most appropriate applications and introduce them across the business as seamlessly and effectively as possible.  By the end of 2024, the gulf between believers and dinosaurs will become increasingly evident.  The influence of generative AI is developing exponentially. Looking back at the pace of acceleration of adoption in 2023, it is almost impossible to imagine the disruptive force it could generate over the coming 12 months.

As McKinsey succinctly stated in its recent report, “What matters most? Eight CEO priorities for 2024”,  executives need to ask: “which parts of the business can benefit? how can applications be scaled from one to many? and how will new tools reshape their industry?”.

This is not just a paradigm shift, it is a revolution, with accelerated change now a constant; business models and mindsets need to adapt to be able to respond in real time to stay ahead of the competition.

Rialto can help you benchmark your personal readiness for the future of work and identify any skills gaps. We also offer a programme of live online events presented by experts in specific fields around the frontier technologies and their application in various functions and sectors.

2: Another year of economic uncertainty.

2023 proved to be a year of economic contradictions and we go into 2024 with the same equivocation. Despite all of the crosswinds which threatened to push the UK and major global markets into recession, including conflict in the Ukraine and the Middle East, double-digit inflation, record energy costs, high interest rates and the legacy of Covid driving a cost-of-living crisis, markets ended the year buoyant with the US indices reaching record highs.

With inflation falling rapidly, interest rates likely to follow later in the year, wages rising faster than prices, and promised tax cuts in the Spring, many forecasters are now predicting a grey market up to the UK national election, with stagnation rather than recession.

In the background, the risk of conflict spreading and causing energy prices to spike will continue to prompt caution among investors.

Executives and board members across most sectors will be emphasising the need for resilience, efficiency and frugality. Spending is likely to be targeted on processes, technologies and strategic priorities that will focus on savings to build up reserves and create agile business models to adapt to the fast pace of AI-driven change; leaders will look for creative, low risk ways to promote growth in an otherwise stale UK economy which continues to lag behind other G7 countries.

That could offer little in the way of relief to the lean executive job market which Rialto highlighted with exclusive data at the end of 2023. It showed a dramatic fall in publicly-advertised executive level vacancies on the year, highlighting the increasingly critical need for the most senior level job seekers to be able to access the hidden market, identify the opportunities meeting their requirements and upskill or retrain if necessary. Pivoting towards new roles created by the technological revolution could open further pathways to successful career transitions.  The focus is on creating a brand and skillset which are more relevant than ever to the new market demand curve for leadership talent.

3: Elections.

2024 is a year of elections, local, national and global.

Will it be a 1992 – the year Labour leader Neil Kinnock snatched defeat from the jaws of victory after taking a tumble on Brighton beach? Or another 1997-style landslide for Keir Starmer’s more centrist Labour party? Markets are always averse to uncertainty. How might that affect the economy?

PM Rishi Sunak has indicated he will call a General Election mid=way through the year.

Will his promised tax cuts fuel increased consumer confidence and prompt a commerce-led recovery? – or spook markets concerned about further increases to record borrowing levels?

The most recent polls put Labour 19% ahead of the Conservatives which would give them the seats they need for an outright majority and a strong mandate for change.

However polls are notoriously inaccurate. Not only did they get it wrong in the last two US elections and our Brexit referendum, they can reduce turnout for the leading party from voters convinced the outcome is a foregone conclusion.

Rishi Sunak will betting on an economic turnaround as inflation and interest rates fall globally to carry him over the line but will his immigration all-in on the divisive Rwandan policy see him go bust and bring on a surprise early election?

And what will it mean if Labour do win? Starmer is keeping his cards close to his chest so little detail in his manifesto so far. He and shadow chancellor Rachel Reeves are, however, determined to prove Labour can take care of business and keep tight fiscal control with new powers for the Office of Budget Responsibility so don’t expect any big public spending contracts, though the construction industry would see a shot in the arm with a promise to build 300,000 new homes a year for five years.  They have promised is to secure billions in private funding to promote growth in the regions, focusing on the green energy economy for which a massive injection of cash would follow a Labour victory.  Along with AI, it’s the growth sector of the moment. Can Starmer pull a rabbit out of the hat and rejuvenate the former industrial heartlands?

In contrast, Sunak has backpedalled on renewables in a bid to put fresh air between him and Starmer, so uncertainty over related future financial and economic policies is likely to deter foreign investment at a crucial time for the sector, potentially leaving the UK too far behind to play catch up with China and the US who are going full steam ahead in the race to become the green superpower.

Whatever and whenever the result, executives seeking new positions and career transitions could do worse than to reskill and pivot towards these emerging technologies.

Business leaders also need to keep an eye on regional elections, including the London mayoral election which could have a big impact on the City, and global elections, including US Presidential elections which will have an important bearing on Western relations with the crucial Chinese market.

4: Laser-focused strategic growth.

As leaders and executives seek to navigate stormy waters at the beginning of the year,  laser-focused strategic growth will be more important than ever.

Budgets remain tight, investment in new technological infrastructures will remain a priority; now is a good time to revisit and update McKinsey & Company’s 2022 10 rules for intelligent growth.

1. Put competitive advantage first. Find your winning formula first then scale up.
2. Make the trend your friend. Stay on top of emerging markets; recognise when the trend is waning and change.
3. Don’t be a laggard. If you’re ahead, keep moving to stay ahead, no treading water. Be more Apple, less Blackberry.
4. Turbocharge your core. How can you build strength into the core that will support new growth? Technology? Product development?
5. Look beyond the core. Expand organically using natural connections and progression.
6. Grow where you know. Optimise your local advantage, knowledge, connections etc.
7. Be a local hero. Win strong loyalty and brand awareness in your locality.
8. Go global if you can beat local. But be sure your product will transfer to new markets first
9. Acquire programmatically. Plan organic growth alongside new ventures. Think about the emerging sectors such as AI and green technologies.
10.  Shrink to grow. Prune dead wood and re-seed for stronger growth,

In this time of uncertainty, it is more important than ever that all leadership teams are constantly looking for opportunities for growth and development with minimal risk, whether micro or macro, into new markets, new sectors or improving on core business performance. Entrepreneurial curiosity needs to be built in to the workforce with strong two-way communications to open opportunities for all employees to contribute ideas to optimise performance at every level.

5: Filling skills gaps.

Technology is disrupting the way we do business at all levels but we need the right people at the wheel to prevent a kamikaze ride into the future.

Generative AI and other frontier technologies are bursting with almost limitless potential but are also fraught with tensions and risks that need a human touch. The most advanced business leaders are starting to recognise that abstract skills such as empathy, curiosity and adaptability are becoming more valuable to the fast-changing, AI-led economic landscape than traditional qualifications such as superior formal education. Emotional intelligence, creativity and strategic thinking are among the skills that cannot (yet) be replaced by technology.

The UK’s education system has been slow to catch on to the need for these skills to power the country’s future economic success. Even young people graduating from universities and sixth forms now have not been prepared for this brave new world.

Senior executives need to demonstrate these skills in their own leadership but also build them into the workforce through strategic recruitment and continuous upskilling and training. HR leaders should be bringing in AI applications to analyse and meet current and future skills requirements particular to the growth strategies of the business.

6: Minding mental health.

As the New Year blows in on the back of grey skies, driving rain and warnings of another gloomy economic 12 months ahead with further job layoffs and a lingering cost-of-living crisis, maintaining a feel good factor in the workplace is a challenge facing leadership in all sectors.

According to Gallop’s State of the Global Workplace 2023 report, almost six in 10 employees considered themselves quiet quitters – psychologically disengaged from work without a sense of meaning or purpose. It claimed that active and psychological disengagement costs the global economy $8.8 trillion a year or 9% of global GDP.

By genuinely committing to caring for the wellbeing and welfare of staff, companies could potentially make huge savings with minimal outlay at a time when critical skills shortages and economic uncertainty are threatening to undermine growth in every sector.

Investment in cultivating relational intelligence, empathy and introducing mental health toolkits, evaluation and support will pay dividends. That may mean hiring consultants or using AI programs to deliver focused training, continuous assessments and easily-accessed support services.

Employees who are being made to return to the office after the homeworking of Covid times and adjust to constant technology-driven changes in their daily operations may be at risk of burnout and disillusionment. Open, two-way communication, flexibility and understanding can all help cushion employees and make them feel safe and valued.

The CIPD claims flexible working can reduce staff turnover by up to 87%.

Some companies are going further, offering sensory spaces, menopause support and introducing wearable devices to track mental health, sleep patterns and AI programmes to analyse sentiment around workplace developments. HR directors should be actively analysing workforce engagement and identifying risks to reduce attrition rates and costs and maintain a healthy workforce to boost engagement and drive performance.

On a more personal level, executives and leaders have had to take on more responsibilities and cope with more change than at any time in history. Those in position should be aware of their own stress levels and look for ways of managing competing challenges, whether by re-evaluating leadership teams to maximise opportunities for delegation of duties or building in moments of decompression, rest and mindfulness throughout the working week.

For those seeking a career transition or out of work and actively looking for a new position, share your journey with a trusted relative, friend or consultant. It is too easy to get caught in a cycle of hope and despondency as opportunities come and go. Take time to reflect on any rejections and seek advice from someone who may have a more objective perspective and be able to help turn challenges into positive development.

In the World Economic Forum’s 2023 Future of Jobs report, 49% of those surveyed across industries anticipate AI to be a catalyst for job creation while 23% also expect it to drive job displacement. This displacement does not just apply to automation making certain roles redundant but also entails reshaping certain functions and shifting the skills needed by the professionals working alongside AI.

When thinking about the skills needed for future success, leaders need to consider both their own capabilities and the skillsets their team will need to possess to deliver impact while working alongside the digital workforce. With Generative AI and other forms of advanced technology taking over specific tasks and functions, what gaps may humans need to fill? How can man and machine work together in tandem to drive business growth? Our team have identified the following five skills for senior executives to focus their upskilling and reskilling efforts on:

1. AI Understanding: To work alongside AI effectively, one must possess a solid understanding of its capabilities, limitations, and functions. For senior leaders, it is imperative to understand what value AI can bring to your business, to be clear on the ‘how’ and ‘why’ your organisation plans to adopt it, and to ensure the rest of your team shares that same vision and understanding. Having a grasp of the scope of AI’s capabilities will make it much simpler to assign actions and owners, all while knowing that AI will not be a total replacement for all tasks and functions. While intelligent and impressive, this technology is not yet autonomous and will need a human at the helm prompting it into action and overseeing its outputs. Executives and their team members alike should treat AI as an assistant rather than a leader, learning how to coexist beside this technology rather than fixating on the losses it may create or overinflating expectations about its capabilities.
2. Analysis and Critical Thinking: One key area that AI excels at over human intelligence is its ability to process vast amounts of data in real-time and provide valuable on-demand business intelligence to business decision makers. This will include insights into markets, competitors, customers, supply chain productivity, and employee performance. While AI can compile this information into clean and digestible formats, artificial intelligence is not always able to assign relevance, perspective, or meaning to the insights it produces. It is therefore essential for leaders to hone their analytical and critical thinking skills to provide relevance to the information generated by AI and relate it back to the business’s objectives and strategy.
3. Bias Detection and Ethical Consideration: Of course, AI outputs should not be inherently and wholeheartedly trusted, as this technology has been proven to occasionally have ‘hallucinations’ and produce inaccurate outputs. Applying critical thinking and analysis to AI outputs can not only help mitigate the risks of misinformation but also help catch potential ethical errors. To be clear, AI is not an inherently unethical or biased technology, but with misuse or improper training can generate harmful outcomes. AI does not possess a human’s judgement to determine between right and wrong, and therefore leaders and their teams must be conscious of the dangers and potential harm of adopting this technology into business practices. This includes becoming conscious of the data sources AI algorithms are trained on, the potential harm of using AI for tasks such as recruitment or talent management, and the privacy concerns involved in sourcing and using information.
4. Emotional Intelligence: Adopting AI will be a major change for senior executives and their teams and is likely to cause discomfort and potential friction. Some members of the team may be eager to evolve, while others may feel threatened, intimidated, or anxious about the introduction of AI into their day-to-day activities. Therefore, it is of critical importance for senior executives to lead with empathy and understanding throughout the entire digital transformation process. Offer support and reassurance wherever possible. Understand your team’s perspectives and take their feedback on board.
5. Communication: To help ease concerns and make the transition to an AI-enabled workforce more effective, senior leaders need to become skilled and tactful communicators. Ensure that all goals, objectives, and expectations are shared clearly across every level of the business. Make it clear why the organisation is adopting technologies, how and when it plans to do so, and what this will look like in practice. Assign clear actions and owners with defined expectations and responsibilities. At the same time, it is just as important to listen as it is to speak. Open the feedback loop for questions, concerns, and suggestions. Making the entire team active participants in your business’s AI journey will help the process and man-machine partnership function much smoother.

In conclusion, as we stand at the crossroads of a transformative AI-driven era, senior executives must recognise that their role in shaping the future workforce goes beyond merely adapting to technology. It involves a profound shift in perspective, from viewing AI as a tool to seeing it as a collaborator in business innovation. These pivotal skills are the pillars on which executives can build a bridge between human ingenuity and artificial intelligence and are not just keys to embracing AI; they are the compass guiding us towards a more agile, empathetic, and prosperous future of work, where man and machine together drive the evolution of business and society.

On average, an adult makes approximately 35,000 conscious decisions every day. Some of these choices are as simple as ‘tea or coffee,’ while others have much higher stakes. For business leaders, that number is likely much higher and many of those decisions hold much greater weight. Day-to-day, senior executives are tasked with making choices that impact their business, their people, their customers and – in certain cases – wider society.

Each individual leader will have their own approach to decision making, with some preferring to seek the advice of trusted peers while others rely on their own intuition. In fact, research has found that more than 40% of CEOs make decisions based on gut feelings. But in our increasingly digital age, businesses and their leadership have a powerful weapon in their arsenal that hold incredible value for making smarter, more effective decisions.

Understanding Data-Driven Decision Making

‘Data’ is not unique to the digital age. Before the somewhat recent wave of digitisation and the subsequent migrations to cloud storage, businesses kept physical records locked in filing cabinets or stored in boxes. These methods were not necessarily the most convenient or secure but served their purpose of telling the story of the business via facts and figures.

Data looks rather different in the digital age. With our shift towards smart devices, social media, and e-commerce, businesses today have access to more data than they realise or utilise. The volume of online activity makes it difficult to pinpoint exact figures, but estimates suggest that 2.5 quintillion bytes of data are created each day. Every interaction, every web search, every sale, and every activity between the organisations and its audiences creates a data trail that helps the business to gain a better grip on its standing in the marketplace and among its customers and competition.

The process of using this information to guide the business strategy and validate courses of action is commonly known as Data-Driven Decision Making (DDDM). Organisations may do this by analysing macro trends and research from credible third parties, conducting their own surveys and focus groups, or running tests to generate original insights on specific products or business challenges. These and other DDDM practices have been used for centuries. However, an innately modern phenomenon is occurring wherein an increasing number of companies have begun using advanced technologies such as artificial intelligence (AI) to analyse the wealth of digital data produced by the everyday digital activities of the business.

Combined, these methods provide deeper insights into the activities of the business, its people, and the markets in which it operates.

Why Use DDDM?

According to a PwC survey of more than 1,000 senior executives, highly data-driven organisations are three times more likely to report significant improvements in decision-making. It is easy to understand why.

In the wake of the pandemic and its aftereffects, it has become more important than ever for businesses to develop the right strategy and prioritise actions that drive impact. The challenges in the marketplace have made it imperative for leaders to make wise choices regarding their products, customer experiences, operations, personnel, suppliers, and more. However, the stresses of navigating the tumult amid pressures to deliver business impact can often cloud judgement and create space for irrationality.

Becoming data-driven can help to keep the business on track by creating a stable model for decision-making that can withstand both troubling times and ideal operating conditions. Much of its value can be attributed to the fact that data is inherently objective. At some point or other, all of us will have heard the phrase, “Numbers don’t lie.” Data offers a similar infallibility. While it is possible for biases to creep into data collection methods and taint the outcomes, overall, data lacks the subjectivity and ‘blind spot’ thinking that intuition-based and other decision-making methods possess. When collected properly, data paints a picture of the way things are rather than presenting individuals or the business through the lens of how you perceive or wish them to be. It may not always be what we want to hear, but data will tell us everything we need to know to grow and evolve.

Because of its ability to benchmark the current position of the business, data makes it possible to better understand the potential impacts of any subsequent decisions and track progress along the way. Data can lend credibility to gut instinct or help steer leaders away from paths that may not deliver the desired impact. This is crucial in times of turmoil when every decision carries extra pressure, and resources may be increasingly valuable. Data analytics and insight generation can often highlight issues that may require immediate attention, areas for improvement, develop risk metrics or potential cost savings. On their own, these insights may seem small, but can help inform a wider strategy that pilots the business towards a more favourable position.

Since data is both logical and objective, it is much easier for business leaders to become more confident in their decision making over time. This confidence will be key for generating buy-in for any strategic initiatives and earning trust for the leadership team. Staff, customers, and other stakeholders want the business to be led by leaders who have proven their competence and their ability to make good judgements. Prioritising data in decision making increases the likelihood of achieving the best possible outcomes much more often, thus increasing the credibility of the leadership team in the eyes of their audiences, as well as the leaders’ own sense of conviction.

Top DDDM Challenges

This is not always as easy as it may seem. In the most recent NewVantage Partners annual survey, which tracks the progress of corporate data initiatives, just 26.5% of organisations reported having become data driven. The biggest challenge seems to be a people issue. 91.9% of executives in the survey cited cultural obstacles as the greatest barrier to becoming data driven. Crafting a successful data culture requires shaping collective beliefs and behaviours to unite all levels and areas of the business over a shared mission to lead with insight.

As with any major organisational change, there needs to be effort invested into communicating objectives, creating alignment, and ensuring the right values and priorities are embedded into the organisation’s practices. Leaders may experience pushback or resistance and will have to work through these changes collaboratively with their people. Data is a fluid asset that flows throughout the business and transcends organisational boundaries. Therefore, it can at times become difficult to assign clear ownership to it, which increases the complexity of managing the business’s valuable information. Communication is critical for assigning responsibility and creating the necessary alignment across teams.

The nature and sheer volume of the data itself presents obstacles as well. The majority of this information is unorganised with experts predicting that by 2025, 80% of global data will be unstructured. This form of data is more difficult to analyse, quantify, and search through. Common examples include email communications, photos and videos, social media posts, websites, and open-ended survey questions. When you consider how many of these items are generated each day, the burden of data analysis becomes much heavier. That is why many businesses looking to become more data driven have begun rapidly adopting advanced technological tools that are capable of assigning meaning and gleaning insights from this mess of information.

How data is collected, managed, and shared creates a major challenge both internally and externally. Customers are not naïve to the fact that the organisations they do business with collect and use their data. Over time, consumers and businesses reached an unspoken social contract in which customers agree to surrender their data in exchange for better products, services, and experiences. But as part of this agreement, it is also expected that the business will use and store this data in a way that safeguards their customers. In recent years, we have seen companies including British Airways, Yahoo, Marriott Hotels, and various social media platforms experience major backlash when this trust is breached. We have also seen the introduction of specific laws, such as GDPR, designed to provide additional protections to consumers in the data age. Navigating the ethical and regulatory considerations of fair data use is a challenge every business needs to take very seriously.

Becoming Data-Driven

But how can leaders overcome these obstacles and put DDDM into practice successfully? At Rialto, we consult with C-suite executives, Non-Executive Directors, HR Directors, Board members, and other senior leaders on strategies to enhance their capabilities and keep pace with the evolving marketplace. Our experts are advising senior leaders to develop a greater focus on the following:

• Maintain an Open Mind: The first step to becoming more data-driven is to be willing to take it on board. Data will not always tell you what you want to hear or confirm the beliefs you may have, which can be uncomfortable. This discomfort may be especially strong for leaders who have historically relied on gut instinct in their decision making. To reap the benefits of data, you need to think of it as an ally. Leaning into your organisation’s data can make you and your business more efficient, more effective, more strategic, and more targeted than ever before.
• Take a Proactive Approach: DDDM is most often reactive in nature. An insight is presented by the data which in turns triggers a decision to either remedy it or follow in the direction it leads. While this is often fine, sometimes the insight is gleaned too late for the subsequent action to make a real impact. Therefore, leaders should aim to use data proactively to become more strategic. Data does a great job of presenting what is, but it is also very useful for assessing what could It is possible to leverage insights in a way that enable the business to test potential courses of action, predict trends, or identify budding problems before they worsen. Learning to use your data in this way will help you navigate the present while setting your organisation up for the future.
• Keep Data at Your Core: Of course, for DDDM to be effective, it needs to be consistent. Your organisation’s data needs to be at the core of all decisions, not just the larger or more strategic ones. When deciding anything, leaders should reflect on the data rather than reverting to gut instinct or previous behaviours. Make it standard practice to tie all decisions back to the data to support your thinking. Use all any data sources available whether it is your digital data, research your organisation conducts itself, or simply the latest macro trends and stats. Over time, referring to the data and applying relevance to your decision making will become a habit that can support more analytical ways of thinking.
• Understand Where DDDM is Headed: While AI and other technologies are not the only way to assess or collect data, these tools are unrivalled for the depth and efficiency they can produce. Therefore, DDDM is relying more heavily on the insights created and presented by advanced technologies. AI is capable of analysing all the organisation’s digital data constantly in real time, a feat no human worker could replicate. This technology can also process and make sense of millions of data points in a matter of seconds. It would take a human worker months of nonstop work to get through this volume of information, and by the time they finish, it is likely that the trends and market conditions will have changed. To keep abreast of ever-changing consumer habits and economic fluxes, businesses will increasingly rely on digital DDDM tactics moving forward. Understanding this now will help to prepare for this inevitable shift.
• Upskill as Needed: That said, it is critical for leaders to have the right digital capabilities for navigating the future of DDDM. Given where DDDM practices are headed, a baseline understanding of AI will be of value to any leader possessing decision-making responsibilities. To support data-driven mindsets, leaders should also look to increase their analytical thinking capabilities. Being able to make sense of patterns, spot anomalies, and derive meaning from charts and figures is a crucial aspect of becoming data forward. The ability to translate raw figures into business relevance and commercial thinking will also serve you well. Additionally, honing softer skills like communication and collaboration are crucial for creating data driven cultures. The most effective data-driven leaders are those who empower their teams to become active contributors the business’s growth. Focus on improving these areas to get the most of your DDDM activity.

If you would like support with strengthening your capabilities through Leadership Development executive coaching or creating a data-driven culture within your organisation via our Business Transformation services, please get in touch with our team.