AI: quality at scale

Business leaders are discovering two truths about AI at the same time: its potential to transform productivity is real, and integrating it meaningfully into work is harder than expected.  The gap between early enthusiasm and lasting value has become increasingly visible.  In fact, research from MIT has shown that many AI pilots fail to deliver sustained adoption or measurable impact.

 

A central reason is pace. In the rush to deploy AI everywhere, organisations often skip over the slower work of building capability, trust, and structure around it. To scale AI well, companies need more than access to powerful tools. They need a workforce equipped to use them effectively, and systems to ensure output quality remains high as usage grows. 

 

 

AI: a new way of working

It’s tempting to treat AI adoption like a standard software rollout: give everyone access, hold a training session, send a few tips on prompt-writing, and expect value to materialise. But AI isn’t another point solution. It’s a foundational shift in how information is discovered, work is structured, and decisions are made.

 

This means traditional change-management playbooks don’t fully apply. Rather than simply ‘installing’ AI, you need to develop capability and confidence around it. Fluency surfaces from hands-on experience, not instruction alone. People need room to test where AI accelerates work, where it introduces risk, and where human judgment remains essential. And they need to be able to do this regardless of their level of technical know-how or expertise.

 

Ideally, AI platforms should be as easy to use as your day-to-day web browser search. Quick to learn, easy to navigate, and capable of delivering comprehensive results out-of-the-box. It should also be entirely accessible and customizable through natural language, so that an onboarding support agent can derive just as much value from it as the engineers who built it.

 

This level of broad accessibility presents the best way to foster an AI-fluent culture. With everyone across the organisation capable of building and customising their own AI assistants, workers are less shoehorned into forcing AI into workflows that don’t make sense, but rather developing custom solutions of their own and integrating AI where it makes the most impact.

 

 

Trust relies on accountable results

The second part of the implementation equation is trust. Many organisations go through with hasty AI integrations that end up degrading the quality and pace of existing work due to consistently subpar responses. Once that happens, users lose trust in the tool, and implementation and adoption come to a grinding halt.

 

Responses from AI need two things to be trusted; accuracy and relevance. A report drafted by an agent on the top 3 performers on my team this quarter isn’t useful to me if it’s listed employees from an entirely different team. It’s also useless to me if it lists the top 3 performers from a different quarter. Responses like these are likely to frustrate users, and encourage them to forgo AI entirely the next time and do it the old-fashioned way.

 

Accelerating work doesn’t matter when you’re losing the content and context needed in order to deliver. That’s why, when sourcing information, AI needs to be tapped into the work you and your organisation do daily. It needs to understand who it’s creating the response for, the context behind the situation at hand, and where to source the most recent and relevant information across the likely hundreds of applications being used across each business. That level of deep knowledge and personalisation is essential for AI to produce meaningful results that users can trust and leverage to push the needle.

 

 

AI needs a fresh approach

The path to meaningful AI adoption isn’t about forcing technology into every workflow or racing to deploy the most tools the fastest. It’s about cultivating capability, building trust, and letting real value emerge through thoughtful experimentation and structure. When organisations treat AI as a new way of working, supported by context, guided by quality standards, and embraced by curious teams, they don’t just automate tasks; they elevate their people and accelerate how the business learns, decides, and operates.

 

The companies that thrive in this next era won’t be the ones who adopt AI loudly, but those who adopt it well: steadily, intelligently, and with a clear commitment to strengthening work, not just speeding it up.

 

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Juliet Bramwell is EMEA Vice President at Glean

 

Main image courtesy of iStockPhoto.com and Atomic62 Studio