beyond AI hype: building the right foundations for lasting impact.

Businesses are rushing to integrate AI, under pressure to unlock instant value.  But simply deploying AI or isolated point solutions on top of broken systems, processes or structures, won’t work. Without the right foundations in place, AI initiatives will get stuck in POC mode, failing to scale.

To succeed, companies must first establish a solid technology, data, and automation foundation that enables AI to do its job & optimise business performance. As referenced in our last piece, McKinsey’s research shows 70% of AI initiatives fail due to poor foundations, lack of integration, and resistance to adoption. In this article we’ll focus on the critical technology foundations needed to move from AI experimentation to enterprise wide impact. 

Why AI needs the right technology foundation

Many businesses are implementing ‘AI’ under the belief that simply integrating new AI models & tools will fix, streamline & optimise business operations. However, these capabilities only reach their potential when applied to structured, scalable, and efficient business environments.  The critical factor here isn’t more tools - it’s about getting your ecosystem ready for AI - laying the groundwork for a fit for purpose technology foundation.  A foundation ready to consume, interact with, & accelerate emerging AI capabilities.  Preparing the right tech stack, data architecture & automation approach holds the key to scaled, sustained AI success.

Lay the foundations for scalable, adaptive AI through your tech stack:

• Balance legacy & future - AI capabilities must integrate with existing systems while remaining agile for future advancements.  This is often overlooked, leading to implementation challenges when inflexible legacy architectures meet rapidly evolving capabilities.

• Composable-first design - A modular, composable approach ensures seamless connectivity across AI tools, automation platforms and business applications. This flexibility allows businesses to swap components, scale efficiently, and futureproof their AI investments.

• AI ops & orchestration – E2E AI lifecycle management is essential to ensure scalability, monitoring, and continuous improvement of AI models across the enterprise. Without this, initiatives risk becoming outdated or unmanageable.

The scalability gap - Gartner found that 4 out of 5 organisations struggle to scale AI initiatives due to fragmented technology infrastructure. Businesses that invest in flexible, scalable tech stacks see 60% higher AI adoption success rates.

Data architecture: the fuel for scaling

• From big data to smart data - AI doesn’t just need more data - it needs clean, structured, and relevant data that enhances decision making and automation.  Without quality data pipelines, AI models underperform.  LLM’s on their own do not differentiate you from your competition - your data and domain knowledge combined with the capabilities of AI is the critical factor in your success. 

• Bridging legacy data silos - Most organisations have disparate data sources that AI struggles to unify. Breaking down these silos ensures AI can leverage complete, high-quality datasets to drive insight and automation.  We know this is daunting – pick it off incrementally.  It’s more important than ever.

• Pragmatic insights - Businesses must pragmatically & incrementally determine when real-time AI-driven decision making is necessary, in order to balance costs, speed, and computational efficiency.  Sometimes, a batch process will suffice.

The data challenge - Amazon research indicates that data silos account for a 40% reduction in AI efficiency, reinforcing why data unification is critical for AI-driven value creation – but this has been a key transformation problem for years.  The solution?  Adopt an iterative approach to fixing data silos in line with your rollout plans & use cases.

Scale enterprise impact through intelligent automation & agentics

• Beyond rule-based automation - AI-driven intelligent automation can optimise E2E workflows dynamically, rather than just automating individual tasks.  Businesses must first evaluate their entire process landscape to ensure automation delivers holistic efficiencies – rather than simply moving a problem or bottleneck from one place to another.

• Agentic AI & decision-making – Whilst the term ‘agent’ is a buzzword at the minute – they are coming - so it needs adequate preparation & investment.  Businesses must acknowledge that as this area emerges organizations will develop AI systems that adapt, learn, and orchestrate business processes autonomously.  Consumers will also leverage this - introducing an age of ‘business-to-agent’ processes & transactions.  For these areas to land successfully & safely businesses will require a high level of process maturity, governance, and risk assessment to prevent unintended consequences.

• Human + AI collaboration – To scale AI it is critical that humans are in the loop.  Rather than replacing human expertise, AI will enhance and augment human decision-making & outputs. Organisations that successfully upskill their workforce to guide their AI capabilities will be better positioned to unlock its full value in a safe, reliable & trusted manner.

AI-powered efficiency gains - Accenture research found that companies using AI-powered automation achieve 30-50% faster operational efficiency gains, reducing costs and increasing productivity.

One of the key hurdles in scaling AI initiatives & delivering sustainable outcomes, is in the tech readiness of the organisation.  Our technology, data & automation blueprint is built upon the fundamentals of change.  Companies must assess & audit where their tech and data capabilities are really at - before designing & scaling their AI investments.  The data from Bain is clear - companies that start with AI readiness assessments outperform competitors by 45% when it comes to scaling & adoption of AI initiatives.

Where to start: 

• Alignment – We cant stress this enough – don’t do anything until there is a consistent understanding across the business as to what you’re trying to achieve.

• Reality - Understand the reality of your data, system readiness, gaps, and inefficiencies before designing.  

• Roadmap – Identify technology, data & areas for automation that are a priority for foundational change, alongside elements that can drive quick wins and efficiencies. 

• Design – Ensure you’re designing for interoperability & flexibility in context of your current estate, while adapting to emerging technologies, tools & capabilities.

• Readiness – It’s critical to get your technology & data estate AI-ready.  Use AI-driven automation to prepare data pipelines, documentation, and API integrations.

• Start small – Keep it simple.  Ensure that focus is on measurement, incrementality & proving out your process in readiness for scaling through your operating model & workforce.

What not to do: 

• Treat AI as a technology project – This is a business change programme.  It will impact every business function, not just IT.   Siloed AI projects will fail to deliver.

• Bolt AI onto legacy processes – Don’t restrict value by forcing transformative capabilities into an outdated, legacy operating model.

• Assume AI is a cure-all for inefficiencies – If workflows are chaotic, data is messy, or decision-making is inconsistent, AI may actually amplify these problems rather than solve them.   There are steps in the journey - AI is not the solution for all problems.

• Fail to plan for scaling costs - Computational costs can spiral if commercial governance isn’t in place.  Google found nearly half of AI investments were wasted due to poor design, governance & implementation planning.  

The bedrock of successful AI initiatives are built in the technology, data & automation foundations of an organisation. Scaling those initiatives into a sustainable, value generating change is predicated on how deeply you integrate & embed the change within the organisation - rethinking processes, reshaping structures, and transforming the workforce.  In our next article we’ll focus on the shift to AI-first operating models, and how businesses must evolve structures, workflows, and their culture to fully capture AI’s potential across the enterprise.

The time to build AI enabled businesses is now – let’s start.