Seven steps to transformative AI adoption

Seven steps to transformative AI adoption

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For all the talk of mass adoption of Artificial Intelligence in business, the reality is more nuanced. Outside of the tech sector and world’s most innovative and wealthy institutions, AI-inspired disruption and revolution has been held back by skills shortages, hesitancy around organisational knowledge, workforce fears, ethical considerations, funding, regulatory, governance and compliance fears.

However, most analysts agree that 2024 will be the year that hype is translated into action: research, recruitment, investment, experimentation and integration.  A recent Reuters survey of more than 4,000 professionals, primarily board level and senior leadership, found that 76% of their organisations were either using generative AI or planning to use it in the next 12 months.

Meanwhile a MIT Technology Review Insights survey of 300 global leaders published just this month found that most expect to double the purposes and functions where they deploy Generative AI in 2024, particularly in customer experience, strategic analysis and product innovation.

All the research suggests the minority that are not looking at AI solutions as a matter of organisational priority risk falling behind.

The good news is that this is the perfect window in which to benefit from being an early adopter while learning from the outliers, the earliest pioneers and developers who have paved the way for the next phase of the AI revolution.

So, how can senior leadership best collaborate to build a roadmap and supportive framework to achieve effective and safe organisational implementation of Generative AI and related frontier technologies, such as large language models, big data analytics, machine learning, robotics and natural language processing?

Here is our seven-step action plan to investigate and implement some of the core opportunities presented by rapidly advancing frontier technologies – while ensuring due diligence – to achieve maximum advantage with minimal risk, drag and organisational disruption.

Step 1: Senior leadership to collaborate, including department heads, human resources, board members and IT to build understanding around how new technologies could benefit the organisation. 

The first step is to ensure you have the talent, skills and leadership to identify opportunities and risks. This may mean restructuring the board, introducing executives with appropriate expertise such as Chief Information Officer or Chief Data Officer or creating a committee to oversee an organisational review through the dual lenses of technology and change management.

Senior leadership from every function should be familiar with the primary AI tools including ChatGPT, Gemini and specialist end-to-end platforms making waves in their sector, reading blogs and articles, listening to Podcasts, speaking to colleagues and associates or being briefed by internal or external experts.

All board members and senior leaders should be able to contribute to planning and implementing AI policies and infrastructure in line with the strategic objectives of their department and beyond, and possess or train up in the skills required to drive AI-led transformation such as resilience, emotional intelligence and critical thinking.

Leadership should oversee an audit of organisational data upon which successful AI-integration depends. If you have not already got one, you may wish to consider managing in a data department to audit and integrate data-driven decision making before attempting to usher in a wave of AI-based change.

Step 2: Commission a market scope. 

What are competitors doing well, doing badly – or not doing at all? Who is using what? And why? Which are actively promoting their AI credentials? Can you see their tangible benefits? Can you imagine your own organisation successfully replicating or superseding the successes while ironing out any potential issues. Look at the current market, pipeline technologies and end uses and horizon planning.

Step 3:  Avoid hype by starting with your pain points and unrealised growth potential and looking for solutions. 

Many companies have made the mistake of throwing money at popular headline platforms and then trying to make them fit.

Your team of experts and/or consultants should be able to make the case for every proposed investment and integration with projected ROI and other benefits such as improved customer and employee experience. Efficiency savings are only one of the potential opportunities to optimise your business. Think also about how AI and other technologies could help develop your business model and products or services, explore new markets, improve talent acquisition and management and logistics/procurement and supply chain processes.

4: Create a roadmap with actionable insights based on your thorough research and consultation. 

At what speed do you want or need to move? Where do you stand to make the greatest gains? What tensions must you navigate?  What are the risks and how can you minimise and mitigate them? (see below) Do you have the data to support integration and optimisation? What infrastructural, hardware and talent adjustments do you need to make?

Start with simple processes that will augment your existing operations to ensure smooth entry and employee confidence. For example, data management and analytics and automation of simple, repetitive tasks to free up employees for more meaningful work, quality assurance and monitoring and continuous, personalised employee training.

How your roadmap looks will depend on many factors including:

• Your sector. Health, customer services and marketing are among the industries and functions investing most heavily in technological investment – and expecting to see the highest growth. Logistics, public sector, education and energy companies have been more hesitant. You may find the road to successful integration better laid out if you are in a sector that is already embracing disruptive technologies – though that also means that if you are not one of those leading the way, you need to start catching up before it is too late. Conversely, those in the sectors with the lowest investment may have the hardest climb yet most to gain.
• The size of your business. The Reuters research found larger businesses were far more enthusiastic tech investors than SMEs. Effective adoption should ultimately reduce costs and improve profit margins but are you financially stable enough to justify and sustain mass investment now or would incremental changes suit your organisation better?
• Your business model. Do you have the agility, adaptability and flexibility to do things differently with technology? If not, how can you build that in?

Step 5: Explore vendor options.  

While big tech vendors – including Amazon, IBM, Google and Microsoft – are responsible for some of the biggest-selling AI tools and platforms, the development of generative AI and cloud computing has spawned a plethora of smaller, niche end-to-end platforms and tools which may be more relevant and financially viable to your business. It is worth bringing together a team of expertise or contracting consultants to explore all options and create a detailed plan before committing. Experiment with smaller tools and analyse the impact before extending across different functions. You may wish to integrate an end-to-end platform, such as Salesforce, Genesys or Amazon Web Service and bolt on tools as you go, or it may be beneficial to you to work with a software/AI developer to design an in-house system based on your own unique needs and business model.  Experiment with different models and tools and look out for free trials.

Depending on the size and scale of your AI-based integration, will you need data scientists and other specialists to integrate and manage your new technologies? Or can you buy in software that comes with human support to provide the expertise?

There are simple ways to immediately augment existing systems and workflows with add-on GenAI applications such as Microsoft’s Copilot which has integrated ChatGPT with its Bing Search engine to assist creative content, visual and text-based, improving productivity and inspiring innovation. Once again, training is key to ensure maximum gain from the licensable tool and minimising risks associated with all AI uses.

Research has found that many employees are using Copilot  and other GENAI tools at work independently, and while their initiative is to be applauded, it is essential that organisations take the lead in putting in place company policies around use of GenAI, setting out who should and should not be using it and for what purposes, alongside training to ensure responsible and ethical usage. (See step 7)

Step 6: Open a dialogue with stakeholders. 

Who needs to sign off investment and change management? Do you have investors to convince? Once you understand what you are trying to do, it is imperative that you communicate your intentions, purpose and methods very clearly to appropriate stakeholders. It is equally important to provide a safe space for honest feedback, to listen and nurture collaboration, constant adjustment and refinement.

This may only be board members, senior leadership, investors and partners at the earliest stage. However, the entire affected workforce must be brought on board before change is rolled out. Surveys show many employees are intimidated by technology and fear it will take their jobs. Before any staff are expected to work alongside AI and other new technologies, c-suite, senior leadership, team managers, human resources and internal comms need to work together to reassure, educate and train staff.

Technologies, data, analytics and machine learning are only as good as the humans piloting, feeding, analysing and monitoring them.

Step 7: Ensure ethical and regulatory guardrails are in place and test them thoroughly before going live. 

Experimental technology is by its very nature high risk and fallible.

You will have heard of generative AI hallucinating – the way it creates content by predicting next words in a sentence leaving it susceptible to total fabrication (though recent reports show Google’s Gemini is now winning the accuracy race over ChatGPT). It is imperative that all output is verified by humans, for tone and potential bias as well as factual errors.

Consider also ethical issues around transparent and accountable data management, intellectual property rights, regulations laws and customs across geographical and cultural regions and borders. Many companies have set up committees or teams to monitor compliance and ethics. There are technological solutions to all these issues but, as always, they need human managers.

Of course, technology-driven transformation and related change management are a process, not an event. The platforms, tools and applications are changing day by day, as are regulations in different parts of the world. Once integrated, AI-based solutions will need constant updating and refining which is why one eye should always be kept on the horizon. Impact must constantly be measured, evaluated against KPIs and adjustments made accordingly.

There is no finish line. It’s no use being an early adopting hare if you’re caught napping as the more cautious tortoises plod past you.

Having the soft skills embedded throughout your workforce to step up to the challenge of constant change and adaptation is as important as importing or training up staff in the technical expertise needed to implement effective transformation.

AI and related technologies will continue to impact almost every function in every sector, whether through automation or augmentation. After the initial phase of instinctive fear of the new, progressive leadership is increasingly appreciating the virtuous cycle of positive transformation afforded by AI proficiency.

Rialto has a team of experts who can support individuals, teams and organisations of any size in any sector through the seven steps to successful AI investment and integration, from market research to change management and skills benchmarking, and beyond.

Contact us for additional insights related to accelerating AI adoption and/or benchmarking your firm’s readiness to adopt AI compared to peers in your industry globally.