Scaling up Gen AI is, however, more than just a numbers game. Sure, we can boost the system’s capacity, but the real challenges—like keeping our AI accurate, seamlessly integrating different systems, fine-tuning and sharing our data across the enterprise and beyond —are what really matter. The secret lies in not tackling these issues separately. The real magic happens when these efforts are not isolated but interconnected to bring the entire enterprise together into a cohesive and effective whole.

From a collection of systems to a cohesive, connected whole

Solving individual challenges is still easy. Take the use of generative AI in customer service of a financial services firm, for instance. AI captures and summarizes every interaction between customer service agents and customers. This technology doesn’t just save time—it ensures no important detail slips through the cracks, making every customer interaction smooth and consistent.

But here’s where it gets really interesting. Gen AI goes beyond the basics and it’s like posting a personal financial advisor for every customer, one that knows their history, keeps an eye on the market trends, and understands exactly what financial products move their goals forward. This AI doesn’t simply log and respond to queries. It offers tailored advice, shaped perfectly to fit the customers’ financial landscape, changing as they change, learning as they grow.

To pull this off, Gen AI needs a rich blend of data, from detailed customer histories to the current market dynamics. Only then can Gen AI offer advice that’s not only right but also right for the customer.

This is AI at its best—turning complex data into simple, actionable, and highly personalized advice, making every customer feel like the only customer.

We have this technology available to us today, which can be incredibly on point. But 66% of leaders have yet to see significant ROI. Why?

To achieve this level of personalization, it’s not enough to solve challenges in isolation. We need to bring all these tech pieces together into a single, streamlined system—a connected enterprise platform. This is where everything clicks into place, making our technology not just functional but fantastic at what it does.

Cognitive pivot creates a world of difference

In the article “AI-First Essentials: Moving Toward an AI-First Future,” we investigate what it takes to build out generative AI effectively. Once the technical groundwork is laid, the bigger task is about guiding change and building trust. The true power of technology is not just in the systems we set up. It is in how people adopt and adapt to them.

Building AI Fluency Among Employees

Here’s a striking fact: for every dollar spent on developing a Gen AI model, about three dollars go into change management, largely due to training needs. Successful companies know that to keep these costs in check, it’s crucial to engage end-users right from the start. This involvement helps steer clear of common pitfalls like launching a chatbot that looks good on paper but falls short in practice because not enough thought went into the user interface.

Start with a team-oriented approach and bring in domain experts early. These experts make sure the AI doesn’t just work; it works well within the specific framework of your company and leverages your internal data effectively. Directly addressing organizational inertia by embedding a collaborative spirit from the get-go helps turn generative AI investments into real, lasting value.

Enhancing Customer Trust by Committing to Responsible AI

There’s huge potential to innovate with generative AI, yet many consumers still feel uneasy about the technology. In fact, a whopping 93% have ethical concerns, ranging from fears about deepfakes and losing the human touch to data privacy issues. It’s clear that good intentions aren’t enough. To build real trust, we need a solid plan that commits to ethics, fairness, and inclusivity from the start. Good intentions are not good enough. To build real trust, we need a solid plan that commits to ethics, fairness, and inclusivity from the start. We need to build our AI Responsibly to strengthen trust, making our AI solutions more palatable and welcomed by your customers.