Stop piloting AI and start scaling it
Digital transformation: The key is to establish a clear definition of success that reflects the realities of your organisation rather than adopting a generic view of AI value. Picture: iStock
A small number of practical decisions continue to distinguish organisations successfully scaling AI from those still confined to experimentation, writes , Partner Consulting, and , Managing Director – Applied Intelligence & Gen AI Lead, KPMG in Ireland
Across sectors including manufacturing, financial services, life sciences and retail, organisations are investing in AI-enabled tools and capabilities to improve performance, enhance customer experience and drive growth.
While enthusiasm remains high, many organisations continue to struggle to move beyond isolated pilot programmes. In our experience, success rarely depends on the technology alone. Instead, it is determined by several fundamental decisions around strategy, governance, operating models and value creation.
Productivity has become one of the dominant narratives surrounding AI adoption, largely driven by the way AI tools have been positioned in the market. However, productivity means different things to different businesses.
For manufacturers, it may involve reducing downtime, improving yield or lowering energy consumption. The key is to establish a clear definition of success that reflects the realities of your organisation rather than adopting a generic view of AI value.
Organisations that take the time to define measurable business outcomes are better positioned to identify where AI can have the greatest impact and how that impact should be assessed.
In recent years, organisations have understandably adopted a cautious approach to AI through proofs of concept and small-scale pilots. While this has been an important part of the learning process, the focus is now shifting towards scaled deployment and value realisation.
Pilots should not be seen as an end in themselves. From the outset, businesses should have a clear understanding of what success looks like, how solutions will be scaled and how measurable value will be delivered.
Without a defined pathway from experimentation to adoption, pilots risk becoming isolated exercises that generate interest but fail to influence the wider business.
One of the most effective ways to identify AI opportunities is through the lens of value streams, the end-to-end activities that create value for customers and stakeholders.
Focusing on value streams rather than individual technologies helps organisations identify where process improvements, better access to data and automation can have the greatest effect. It also provides greater clarity around ownership, governance and accountability.
When companies understand how value is created across their operations, technology investments become more targeted, strategic and outcome focused.
Successful AI adoption demands a review of the processes, policies and ways of working that have developed around existing systems.
Introducing AI often changes how decisions are made, how information flows across an organisation and how employees interact with technology. Simply automating existing processes without challenging whether those processes remain fit for purpose can limit the benefits that AI is intended to deliver.
Businesses should therefore view AI programmes as business transformation initiatives rather than technology projects alone.
Governance enables innovation As companies seek to balance innovation with risk management, governance has become increasingly important.
In the absence of formal frameworks, employees may adopt AI tools independently, creating risks around data security, compliance and intellectual property. What begins as experimentation can quickly result in inconsistent practices and limited oversight.
Establishing clear policies, governance structures and responsible AI principles enables businesses to innovate with confidence while maintaining appropriate controls. Effective governance should support adoption rather than constrain it.
As AI technologies continue to evolve, companies are faced with choices around whether to develop capabilities internally, acquire external solutions or adopt a combination of both.
In reality, the most effective strategies often involve elements of both approaches. Existing systems can continue to play an important role, while new AI-enabled capabilities are introduced where they can create the most value.
Partnering with organisations that bring specialist expertise can also help accelerate adoption, reduce implementation risk and provide insights into emerging capabilities.
Ultimately, technology choices will continue to change. What matters most is having a clear strategy, a strong understanding of business priorities and a disciplined approach to value creation.
The conversation around AI is evolving. The question is no longer whether organisations should explore AI, but how they can deploy it in a way that delivers tangible business outcomes.
Those that define success clearly, focus on value creation, strengthen governance and develop a pathway to scale will be best positioned to realise AI's potential. For many organisations, the greatest challenge is not adopting AI. It is moving beyond experimentation and embedding it into the fabric of the business.
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