Imagine an AI that drafts investment proposals, sifting through massive data repositories in an instant. Picture an intelligent agent streamlining employee onboarding. Or an AI extracting critical ESG data. Or hyper-personalized customer experiences at a huge scale. These aren't futuristic fantasies, they are benefits GenAI is already delivering to customers today.
In sectors like banking, financial services, and insurance (BFSI), where the stakes are among the highest in terms of regulatory scrutiny, data sensitivity, and reputational risk, the promise of GenAI is vast: accelerating investment due diligence, enhancing risk assessment, and driving unparalleled insights. IDC projects spending on GenAI to reach $202 billion by 2028. The market is eager, the potential huge.
But here's the thing: harnessing GenAI’s potential takes more than just impressive features. All of the required building blocks need to be tied together, orchestrated, into a cohesive, functional solution. Whether deployed on-premises, in the cloud, or in hybrid environments that get the best of both worlds, true enterprise GenAI demands a seamless integration of every element.
And that – successfully scaling up enterprise GenAI deployments across the organization – is a skill that takes years of painstaking effort, weeks, months, sometimes years in the trenches with demanding customers in harshly regulated industries, to refine.
The Feature Fallacy: Why More Is Not Always Better
Over the past few years, we’ve seen a cottage industry of GenAI providers emerge. Each comes with myriad features: advanced NLP, sophisticated content generation, seamless UIs – you name it, someone has done it. Industry giants have joined the fray. At first glance, it looks like a race to line up the longest list of functionalities.
And that’s not all. Foundational LLMs are now available to anyone, along with open-source alternatives that are quickly closing performance gaps. With that, the cost of building surface-level functionality is approaching zero. What used to take entire teams months to put together can now be assembled in a weekend.
But after a few years of experimenting with GenAI, organizations are waking up to the truth that simply bundling a collection of amazing features together doesn’t, by itself, create a functional enterprise solution, much less a strong differentiator. As pointed out in a recent article by Foundation Capital, when every company ships the same primitives, what you build is no longer your moat.
The Core Challenges: Where Enterprise AI Lives or Dies
The real confrontation with reality comes not in a pilot, but when you try to scale. Shiny features crumble against enterprise data: legacy formats, incomplete information, industry-specific terminology, idiosyncratic workflows. This is where most solutions fail.
In the BFSI sector, this reality is particularly obvious. Financial institutions operate under strict regulations, deal with massive volumes of highly sensitive data, and cannot tolerate errors. The core challenges are clear: privacy, security, accuracy, flexibility, and scalability. GenAI deployments are rarely greenfield operations; they have to integrate seamlessly into existing, often decades-old, infrastructure like OpenText, SharePoint, and AD/EntraID.
For most enterprise GenAI providers, checking all these boxes while delivering workable solutions on time and to budget, has proven to be an almost insurmountable challenge. As a result, very few GenAI providers have come close to achieving true enterprise-grade scale in these demanding environments.
Decades of Experience Taming Enterprise Data
At Squirro, our success in breaking through this pervasive struggle isn't recent luck. It's the result of a decade spent working tirelessly to solve the core challenge of enterprise-grade AI: accurate, secure, and permissions-enabled enterprise search and information retrieval.
Starting well before GenAI entered the mainstream, we were working side by side with central banks, government authorities, and leading financial service providers, helping them modernize their knowledge management and enterprise search solutions. These experiences refined our offering, ironing out wrinkles in search optimization, information retrieval, and, crucially, permission-enabled access control. We became experts in deeply embedding production-grade enterprise AI into the most complex enterprise environments.
Then, GenAI took off. Our mature information retrieval stack, already adept at finding the most relevant enterprise data, paired perfectly with large language models. This enabled advanced retrieval augmented generation (RAG), now an industry standard for enterprise GenAI.
The result? An offering that perfectly aligns with the BFSI sector's stringent requirements for privacy, security, and accuracy at scale, which has been adopted by institutions like the European Central Bank (ECB), Deutsche Bundesbank (DBB), Bank of England (BoE), Security Benefit, and Standard Chartered Bank
The Only Currency That Matters: Provable Results
The result of this journey is a future-proof, enterprise-grade GenAI platform that empowers banks, financial institutions, and other large, highly regulated organizations to harness the full potential of their data. While many vendors are still navigating the initial hype, we are already driving tangible value in the most demanding environments, working with the most sensitive customer data.
We are not just selling software; we are delivering outcomes. Our partnerships with leading central banks and financial institutions are not just logos on a slide; they are proof of our ability to navigate complexity, secure sensitive data, and deliver verifiable results at scale. We are among a small group of European vendors that Gartner has listed as an emerging leader in their prestigious Emerging Market Quadrants in Generative AI Engineering and AI Knowledge Management Apps/General Productivity.
The opportunity is vast. The $4.6 trillion that enterprises pour each year into outsourced services is poised for disruption. Every startup that masters deep integration and outcome insurance will carve off a slice of that multi-trillion-dollar frontier. The only currency that matters in this new era is the speed with which you can turn promises into provable results. And that's precisely what we do.
Find out why Gartner has included Squirro as a representative vendor in their latest Market Guide for Generative AI Platforms in Banking and Investment Services. Access your complimentary copy of Gartner's Market Guide here to gain deeper insights.
