For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale.
At least, that's the picture that emerged from a recent Forrester study commissioned by Tines. We asked more than 400 IT leaders from North America and Europe to share how their teams were thinking about AI and automation and what they felt was holding them back.
One idea that came up again and again was that progress often breaks down at the seams, where deployments in one part of the business don't carry over to others due to either missing or misaligned support structures. According to our findings, almost half (49%) of businesses lack a clear orchestration strategy, with most agreeing that this was making AI difficult to adopt and scale.
When we discuss orchestration, we usually mean how separate but often interconnected teams, tools, or business processes work together to enable more complex operations. Instead of adding new tools, orchestration links the existing ones your teams already use, serving as the execution layer that makes AI effective and scalable.
For this study, we wanted to understand how enterprises viewed orchestration as a leadership and coordination function, rather than just a technical exercise.
Where Orchestration Should Sit
Something that really struck us was the disconnect between where business orchestration should sit and how those efforts are seen. When asked whether they felt IT was best positioned to coordinate AI across workflows, systems and teams, 86% of respondents agreed. Further, 38% of respondents said IT should own and lead orchestration, and over a quarter saw IT playing a role either as a coordination hub between business functions or as an enabler of AI initiatives.
Yet, an equal share (38%) says those contributions are overlooked at the executive level.
A Snapshot of Fragmentation
That ambiguity shows up in other areas, too. Nearly half (49%) of IT leaders surveyed told us that conflicting objectives across IT, business, and data functions were the biggest obstacle to scaling AI efforts. Misaligned budgets, technical tooling and disconnected platforms (43%) compound these silos, making it even harder to scale AI solutions beyond isolated pilots.
In this kind of environment, it's not surprising that over half of IT leaders have made compliance and governance the foundation of their AI efforts — ahead of aspects like enhancing employee experience (44%), cutting IT costs (42%), or speeding up delivery (34%).
This is a logical response to the risks that come with fragmented AI adoption. But governance depends on orchestration to function at scale. Without it, workflows stay siloed and the connective tissue needed to apply governance consistently just isn't there.
How Orchestration Builds Confidence in AI
This brings us to another challenge: trust. 40% of IT leaders told us that employees didn't fully trust the outputs generated by AI tools.
It's a telling figure that reflects the on-the-ground reality of AI adoption. If people don't understand how AI is governed or how its outputs influence decision-making, they're unlikely to trust it, regardless of how powerful or sophisticated the model is.
Encouragingly, leaders increasingly recognize this problem. 73% of respondents said end-to-end transparency across AI workflows was essential to building confidence in AI outputs.
Orchestration supports this visibility by providing a clearer view of how decisions made in one part of the business affect outcomes in another, while at the same time aligning teams around shared tools and processes. That kind of transparency builds trust, not just by making AI activity legible across the organization, but by ensuring that everyone can see how their work contributes to wider business outcomes. More than a third (35%) of IT leaders told us aligning AI initiatives with enterprise strategy was a key priority, further underscoring the need to bring sense and structure to organizational roadmaps.
None of this is about slowing down innovation. In fact, our study showed that where orchestration is working, it's unlocking greater collaboration between teams and faster progress towards business goals.
From where I sit, orchestration is one of the clearest ways to turn AI ambition into real operational value. If IT has the mandate and the tools to lead, organizations stand a much better chance of scaling AI into something that delivers real business value — safely and sustainably.