In a tough market, raising productivity is a key way of boosting financial performance and teasing out those marginal gains. It’s something that the private enterprise leaders I speak to are highly focused on achieving – and the number one route they’re taking is to increase investment in technology including AI.
We saw this clearly in our recent KPE Pulse Survey, where technology was by far the dominant investment area, cited by 66% of leaders. This placed it well ahead of the second highest area for capital spend (workforce & skills, 37%).
In our digital age, leveraging technology makes strong sense and I am encouraged by the clarity of focus and intention. However, it is crucial to recognise that spend on technology on its own will not bring about the uplifts leaders are chasing.
A particular focus, of course, is AI – still a very new and emerging field. But in fact we only need to look at the lessons of traditional IT investment to show us that there is far more to success than simply wielding the cheque book.
Euan West
Head of UK Regions and UK & EMA Head of KPMG Private Enterprise, Head of Markets & Growth
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A combination of success factors
In any IT transformation or upgrade, a number of elements need to come together to make good on the investment. It starts with C-suite sponsorship and tone from the top. Leaders need to send a clear message about why the investment is important and, crucially, how it will benefit staff. They need to set out the vision that excites, motivates and engages the workforce. This establishes the context and ambition – the North Star.
Then it is about robust and rigorous project management. A capable PMO is needed who can oversee the programme, ensuring that the stages, sequencing and dependencies of the project are optimally configured. A good PMO also manages the day-to-day communication around the project, keeping stakeholders engaged and informed.
A critically important component is change management. This is where, in fact, most projects either succeed or fail. There is no point installing a shiny new technology asset if no one uses it or understands what to do. Therefore, managing the change through communication, education, training and technical support is key. Only if staff feel comfortable and confident in the new technology will they fully adopt it and help the business realise those productivity gains.
Adopting AI as a continuous process
All of these factors apply to the deployment of AI, albeit there are some nuances. The most obvious point is that AI is not a one-off event like the installation of, say, a new finance system. Using AI is more of a continuous shift in the way of working that will itself evolve and change as new models, capabilities and functionalities become available.
This means that successful adoption of AI requires a mindset shift, not just a one-off adjustment. Get people excited about the potential AI holds, give them time to experiment with the tools that are available to them as part of the company’s existing software suite (like Copilot or ChatGPT). Set out the vision for AI more strategically in the business: beyond these foundational AI productivity tools, where else and how are you envisioning AI could make an impact?
Other best practices apply, like tone from the top and executive sponsorship. Establish a cross-functional AI working party under a trusted individual’s clear lead (the ‘PMO’). Formulate an AI policy (what can and can’t AI be used for?) along with a governance framework that establishes quality assurance, review and approval principles for AI outputs. Identify specific use cases for AI in your business and get some proof of concepts moving.
Different stages of the journey
Many private enterprises are already some way along this journey and are seeing positive results in terms of use cases that add genuine value, even if few have yet achieved anything like whole-of-enterprise transformation. Others are further back – really only dabbling around the edges as individuals in the business experiment for themselves with publicly available tools.
My message to private enterprises is to lean into AI. If you haven’t already, start approaching it in a systematic and structured way, with a clear view of where it could support your wider business priorities. Data should sit at the heart of that thinking from the outset, rather than being treated as an afterthought. Its quality, accessibility and governance are likely to influence how effectively different AI models, tools and use cases can be explored, so these foundations need to form part of the wider conversation.
Converting time into productivity
However – to return to my original question – will AI solve the productivity conundrum? Again, just like broader technology, the answer must be that on its own it won’t. It will be a combination of the factors I have discussed that really bring out the value, together with a workforce that is motivated to find efficiency improvements and make them stick.
This is what those further along the road are already finding: where AI helps staff save time and get things done faster, it’s crucial that the capacity freed up is actually put to valuable use on something else. Many people need some element of incentivisation to do this – so think about what that is.
We are seeing some encouraging signs in the market as businesses invest in technology and AI. The ones that go the furthest, fastest, will be those that don’t just spend the money – they bring their people with them, support them in the change, and engineer a collaborative cultural shift.
