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.
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.
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.
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.
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.
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
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