AI has moved from being a technical curiosity to a boardroom priority in an incredibly short space of time. Senior leaders are now getting comfortable discussing generative tools, predictive models and autonomous agents, while teams are busily building pilots and trying out new tools.
But for many companies, there’s a tension lurking beneath the AI excitement. The truth is that there’s often a big gap between what senior leaders hope and believe AI can do for them and what the organisation is ready to support.
The ambition / reality gap
Most leadership teams have a big vision for AI. Maybe it’s about smarter customer engagement; automated decision-making; more efficient operations or intelligent systems that support employees rather than slowing them down. But when you look under the surface of the ambition, common challenges appear:
- Data is fragmented or inconsistent.
- Proofs-of-concept have been built, but few have scaled.
- Ownership of AI initiatives is unclear.
- Governance is still evolving.
There’s certainly no lack of great ideas for how they can use AI, they just often lack the foundations to deliver valuable AI initiatives consistently and at scale.
Closing this gap means treating AI as a business transformation initiative, not just a technology project. That means linking every use case to a clear outcome and being honest about the readiness of your data, your processes and indeed your teams.
AI will amplify your data, for better or worse
AI is a powerful amplifier. If your data is well-structured, reliable and accessible, it will unleash amazing value. But if your data is patchy and poorly governed, AI will simply magnify those weaknesses. Many organisations discover deep-seated data issues only when their AI models start producing unexpected results.
Rather than seeing this as a setback, it can be reframed as an opportunity. AI initiatives can provide the catalyst to improve your data quality and governance, which will pay dividends in the longer term. To see more rapid results, you might just need to prioritise use cases where data is strong enough to succeed, while steadily improving the wider data landscape.
Who owns AI in your organisation?
Another recurring question is ownership. Does responsibility for AI sit with IT? Or with an innovation team, or Individual business units? Without clarity on this point, AI initiatives – however exciting – will eventually stall as decision-making breaks down and the attention drifts towards newer and more exciting ideas.
Effective organisations tend to adopt a shared model:
- Executive leadership sets direction and appetite for risk.
- Central teams define guardrails, standards and platforms.
- Business units own outcomes and value realisation.
- Cross-functional teams deliver solutions collaboratively.
Just as important is the cultural side. AI can be a disruptive force within the business: it challenges roles and established ways of working. Leaders need to create space for experimentation while being transparent about how responsibilities may evolve.
From proof-of-concept to real impact
There is no shortage of AI pilots in happening. The real challenge is moving from a promising demo to a production-ready product that can scale nicely and make an impact on day-to-day operations.
Scaling requires integration with core systems, process redesign, training and measurable ROI. It’s a ‘business task’ and a ‘people task’ as much as a ‘technology task’.
This is where delivery approach matters. Traditional development cycles can be slow, inflexible and resource heavy. At the same time, unstructured experimentation can lead to fragmented solutions built on ungoverned tools.
Modern delivery models offer a more balanced path. Using a consistent low-code platform like OutSystems, and building modular, agentic applications make it easier to iterate quickly while maintaining oversight and integration. This approach allows teams to test, refine and scale solutions without starting from scratch each time, and to build a ‘centre of excellence’ around a core toolset.
Rethinking processes, not just automating them
One common mistake is using AI to replicate existing processes more quickly. If a workflow is fragmented or inefficient, automating it may save time, but it won’t really make a step-change to your outcomes.
The real value often comes from reimagining how work should flow in an AI-enabled environment. Agentic applications, for example, can coordinate tasks across systems and support decision-making in new ways. But that requires stepping back and asking: if we designed this process today, what would it look like? Transformation is as much about ‘design thinking’ as it is about AI and algorithms.
Should you act now or wait?
With AI evolving so quickly, it is tempting to wait for the market to stabilise. No one wants to invest in tools that feel outdated before you’ve even had a chance to get them to market.
At the same time, delaying entirely carries its own risk. Competitors may build capability and confidence while your organisation dithers. And with the AI surge continuing, perception matters, both internally and externally.
A pragmatic approach is to act deliberately rather than reactively. Focus on high-value use cases with clear metrics. Build internal capability and choose flexible platforms and delivery methods that can evolve as technology advances.
‘Readiness’ is less about having all the answers and more about building the capacity to adapt.
So, are you ready?
AI readiness is not defined by how many pilots you’ve launched or how many tools you’ve trialed. It is defined by clarity of purpose, quality of data, strength of governance and your ability to deliver change at pace.
The companies that successfully differentiate using AI will be the ones that combine ambition with pragmatism, innovation with discipline, and technology with thoughtful process redesign.
The good news is that there aren’t many companies that are ticking all of these boxes just yet, so the AI opportunity is very much there for the taking. In fact, it’s the perfect time to get started.
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