Building success out of AI hype

Cathal McCarthy at Kore.ai describes what AI leaders are betting on to avoid failure

 

The race for first-mover advantage in any industry is rarely without incident. As more and more enterprises scramble to jump on the advantages AI can bring, we’ve witnessed a period of intense urgency – arguably not seen since the dotcom boom – driven by generative AI breakthroughs, competitive pressure and the fear of being left behind.

 

But as the dust settles on this AI gold rush, a few things are becoming abundantly clear. Much of the creative activity witnessed during this frenetic period happened in isolated pockets, with teams launching AI pilots wherever they could, but often with little coordination. 

 

As a result, what’s left behind is a trail of disconnected tools and incomplete experiments, many of which are unlikely to see the light of day. But there’s also something that is, perhaps, even more troubling. Three-quarters of those surveyed in a report by Boston Consulting Group (BCG) talk of these projects not in terms of glowing success but of ‘silent failure’. 

 

 

AI islands linked with ‘silent failure’

The BCG report talks of AI systems that appear to work on the surface but quietly underdeliver on value, accuracy and trust. 

 

And while ‘silent failure’ can happen in any environment, the report suggests that it thrives in fragmented ones. That’s because when AI is deployed in isolation – without shared governance or enterprise-wide visibility – failures are harder to spot, more difficult to fix and easier to ignore. As a result, disconnected tools create blind spots, making it difficult to track outcomes, ensure compliance or scale success.

 

That’s why many leaders are now shifting course. The urge to experiment is giving way to a more strategic phase, one built on unified platforms, central oversight and scalable foundations. Or to put it another way, enterprises are now looking at ways to connect their AI  islands and improve the overall business impact and return on their investments.

 

 

From disjointed tools to unified platforms

This means investing in integrated platforms that bring together data, models, workflows, and governance within a single system. More than 70% of those organisations surveyed have already evaluated the benefits of a unified AI platform, and of those, 84% are either exploring or actively transitioning to one. As a signal of intent, it’s pretty clear to me that change is happening. 

 

One of the driving forces behind this shift is cost. The inefficiencies associated with a piecemeal approach are starting to mount up. In fact, 81% said the cost of developing new use cases had become too high under the current fragmented model.

 

 

Platform-based AI delivers a firmer foundation

Then there’s governance. As regulatory scrutiny intensifies, enterprises are rethinking how they manage risk, so much so, that 70% of respondents said governance is now a top concern. 

 

In a fragmented setup, oversight is hard to maintain. In a platform environment, on the other hand, it’s built in. Features built on a responsible AI framework like role-based access controls, audit logs and embedded policy enforcement, help organisations apply consistent standards across all AI activity from pilot to production.

 

And finally, there’s the matter of speed. While early deployments often took months to build and customise, a platform approach means that enterprises can now move far more quickly. Platforms come complete with shared components, reusable models and centralised orchestration.

 

Indeed, companies like Nvidia have already made that transition, consolidating dozens of standalone chatbots into a single platform that’s more scalable, more accessible and far easier to govern. In short, platforms aren’t just helping enterprises address the fragmented approach of the last couple of years, they’re laying the foundation for what comes next.

 

 

Turning trials to transformation

Of course, moving to a platform model doesn’t mean abandoning specialist tools entirely. Many enterprises are taking a hybrid approach, building sensitive components in-house while buying off-the-shelf systems to speed up deployment, with nearly 80% of survey respondents saying they prefer this blended strategy. 

 

The key is interoperability: ensuring everything, whether bought or built, works within a unified governance and orchestration layer. Another challenge is data readiness. Worryingly, only one in ten organisations consider their data to be AI-ready. And yet, without clean, current, accurate and fair data, even the most well-architected platform will struggle to deliver results. 

 

So where does that leave us? The early AI rush – like the dotcom boom before it – gave organisations permission and time to experiment. Like any hype cycle, it’s an important phase of rapid development and learning. While the early adopters have seen success in pockets, there are no tangible results at the organization level. Connecting any AI islands isn’t just a technical clean-up job. It’s a strategic reset to maximise AI’s return on investment.  

 

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Cathal McCarthy is Chief Strategy Officer at Kore.ai

 

Main image courtesy of iStockPhoto.com and Lemon_tm