How technology leaders are moving from AI novelty to generate business value

While the media headlines may focus on AI's potential to disrupt, the reality is that AI is already quietly transforming businesses. 

From optimising supply chains and personalising customer interactions to accelerating drug discovery and detecting fraud, AI is delivering tangible results across industries. More than 85% of advanced adopters are already reducing operating costs with AI, and organisations are finding new applications and use cases every week. 

The transformative power of AI, as demonstrated with Microsoft Copilot, is reshaping businesses at speed. At our recently held technology conference, AI Con, we showcased our AI-powered panellist, Clay, demonstrating the potential of AI to contribute in a meaningful way in real-time discussions. Innovations like Clay could be used as an interactive coach or trainer, rapidly accelerating the implementation of changes in organisations. 

Despite its potential, many organisations struggle to effectively translate the C-suite’s AI vision into feasible opportunities; data silos, legacy systems and a lack of skilled talent can hinder progress and prevent companies from realising the full benefits of AI. Additionally, the pace of change is accelerating. Agentic AI, with its ability to act autonomously and learn independently, promises to be even more disruptive. This new era of AI demands a more strategic and considered approach to adoption. 

Without a proper strategy, AI remains a solution in search of a problem. 

A structured framework for AI adoption

Kainos has supported organisations to understand AI's capabilities, risks, and strategic planning, helping them move forward safely. We have distilled the learnings from our experience into an action plan that implements AI operations and delivers business value. 

Step 1: Align AI initiatives with business strategy

First and foremost a vision, supported by a SWOT analysis, about the strategic impact of AI on the organisation needs to be developed. Working with C-level stakeholders, technology leaders should identify and formulate a vision that articulates the importance of AI to the organisation and its stance to AI adoption in addressing business goals. Link these opportunities to specific business goals, considering strengths and weaknesses. 

Before implementing AI, identify where it can truly add value – rather than just adding AI for its own sake. Ask these essential questions: 

• Where can AI solve existing business challenges? Evaluate previous attempts at identifying AI opportunities. Consider the risk, impact, needs, and feasibility of the use cases identified. 
• What will the real benefits and value-add be to my organisation? By identifying the expected and potential benefits of AI through key operational metrics, organisations can allocate costs and resources more effectively to maximise value and ROI. This approach ensures that AI projects use baseline metrics to measure success against the overall business objectives. 

Having a business-aligned AI vision and set of AI initiatives is a good start, however without the right capability, your AI vision remains theoretical. To deliver the AI opportunities identified, you should also incorporate into your planning development an AI operating model, which identifies the enabling capabilities needed in terms of technology, data, organisation, AI literacy, engineering and governance. 

Crucially, this operating model should also encompass your cloud strategy. Cloud computing and hyperscalers like Microsoft Azure offer significant advantages for organisations embarking on their AI journey. 

Based on our experience working with organisations embarking on their AI journey, we frequently uncover the following maturity challenges that influence an organisation's ability to adopt AI: 

Understanding your own maturity, is crucial to successfully navigate the early stages of AI adoption and unlock the transformative potential of this technology. 

Step 3: Create your AI roadmap and execute 

It is crucial to coordinate the identification of AI opportunities and the planning of an AI operating model’s goals due to; 

• Value creation depends on operational maturity: You can't just have great AI ideas; you need the right foundation to execute them. This means having the right skills, data infrastructure, and processes in place.  
• Different AI initiatives have different needs: A simple AI project might just need existing tools and a little training. But a complex project might require significant investment in new technology, data management, and change management. 

Understanding the operational needs of different AI initiatives allows you to prioritise them effectively. You can start with projects that fit your current maturity level and build towards more complex initiatives as your capabilities grow. 

2. AI adoption as “Quick Wins” which avoids the glare of a big budget and business case. While some interesting outcomes of experiments may be delivered, it is of no consequence as no new capability has been put in place. This results in significant duplication of effort and a tangle of bespoke technology architectures that are costly to build, manage, and maintain.  

Instead, Kainos’ approach to AI adoption, identifies one or two business goals aligned AI initiatives that prove value and are operationalised at pace. This builds early capability and credibility equally.  

Based on our approach; 

We find organisations get faster value from this balanced approach. By starting small and scaling incrementally, your capability builds, your teams learn and adapt which makes the transition to AI enablement smoother and better managed.   

Step 4: Measure success against expected benefits 

The ability to capture the full economic potential of AI innovations is a core differentiator between those who succeed and those organisations who remain developing proofs of concept.  We see that many organisations struggle with effectively mapping the benefits of AI initiatives for these reasons: 

Ultimately, mapping the benefits of AI requires a strategic approach that aligns AI initiatives with business goals, establishes clear metrics, and tracks progress over time. It also requires a commitment to communicating the value of AI to stakeholders and securing their ongoing support.  

Organisations that have successfully created value with AI have managed to go beyond the phase of experimenting. What sets them apart is they have built an AI strategy that is coupled with their business strategy. Ergo, transitioning from AI novelty to business value requires an AI strategy.  

As CIOs and CDOs, it’s crucial to focus on high-quality data, clear governance with guardrails, robust security, and adaptable AI roadmaps. By addressing these areas, organisations can overcome common challenges and fully exploit AI to drive significant measured business value. From our experience, the Kainos approach to AI adoption builds capability through fast delivery is transformative. It enables businesses to incrementally add sustainable capability that impacts operations, enhances customer experiences, fosters innovation and ultimately outpaces the competition.  

Our AI launchpad is designed to help organisations conceive and realise a way forward with their AI journey without falling into the trap of low value projects. We help your organisation understand the capabilities required to realise your vision and offer real-time next steps to propel your organisation into the future with AI.