As we progress through 2026, the pressure on ISV CEOs has shifted. It is no longer enough to present an AI roadmap – enterprise customers now demand implementations that are production-ready, auditable, and inherently trustworthy.
The prevailing sentiment from analysts and CIOs is that the market is increasingly sceptical of bold AI promises. Success now requires hard evidence, rigorous trials, credible references, and success criteria they can measure.
For the remainder of the year, the market will favour vendors capable of closing the gap between confidence and delivery. To achieve this, leaders must avoid the common architectural traps that cause AI initiatives to stall once they encounter real-world customer environments.
The Reality of the "Readiness Gap"
There is a massive disconnect in the market right now. Our research shows that while confidence is high – 91% of ISVs believe they are AI-ready – the actual implementation phase is proving to be much harder than many expected.
The data tells a fairly blunt story:
- 97% of ISVs are hitting significant roadblocks when trying to bake AI into real enterprise environments
- Nearly half (48%) say their customers are already frustrated because the AI outputs are not accurate enough
- Over half (52%) of ISVs admit that their customers’ fragmented data architectures are the main reason AI adoption has stalled
These are not just technical "glitches”, they are direct drivers of implementation costs and customer churn. In a market that’s consolidating as fast as ours, your roadmap decisions are not only about product features, they are also about your company’s growth and long-term survival.
A Reality Check for your Roadmap
If you want to keep your strategy on track, it is advisable to subject your current plan to a quick "reality check." These conversations need to happen right now between your product, data, and commercial leaders.
Start with data - because that’s where trust lives. Are your AI models built on data that is compliant and auditable? Can you explain exactly where an output came from and who is accountable for it? While 41% of ISVs acknowledge governance as a top requirement for their enterprise customers, only 31% are prioritising it. That is a huge gap, and it is creating friction.
Will it hold up in the real world? It is easy to make AI work in a sandbox. It is a lot harder to make it scale when data is fragmented and policies change. You have to assume from day one that your customer’s data environment will be complex. If your architecture is not flexible enough to handle that reality, your solution will not survive the first week of implementation.
Address the "accuracy crisis" at its source. If half your customers are frustrated with low accuracy, you have to look deeper than the AI model itself. Our research highlights that 37% of these issues stem from poor data quality or availability. The simple truth is you can’t fix an accuracy problem if the data foundation it relies on is broken.
Make your integration repeatable. Integration should not be a bespoke, one-off project every single time. To keep your margins up and speed up time-to-value, you need stable interfaces and repeatable patterns that allow you to plug into customer environments without the usual friction.
The Bottom Line
Enterprise customers are looking for assurance. They need to know your AI tools are built on a solid, scalable foundation. Reliability, compliance, and performance at scale are no longer "nice-to-haves" – they are the core product requirements for 2026.
At InterSystems, we specialise in strengthening the data foundations that make AI work. Whether you are modernising legacy systems or launching an entirely new product, we help you deliver solutions that scale, comply, and perform.
If your AI implementations are hitting walls, it is time to look at the foundation. Let’s talk about how to get your strategy into production.
In a nutshell
- Understand your customers AI needs: make sure your roadmap matches what enterprise customers need and expect in terms of reliability
- Focus on governance: don't wait for customers to ask for it – build it in now
- Fix the foundation: if you do not strengthen your data, you will never solve the accuracy and trust issues holding you back