When it comes to Generative AI (GenAI), IT teams are stepping up. They’re experimenting with models, building custom pipelines, standing up internal frameworks, and exploring ways to make this technology work for their organization.
In many ways, that’s exactly what they should be doing.
For IT leaders and AI engineers, the instinct to build is natural. It offers control, flexibility, and a chance to tailor the stack to the unique requirements of the enterprise. Whether the focus is data privacy, compliance, or simply future-proofing, creating an internal GenAI environment can feel like the obvious answer.
But here’s the reality: in many organizations, those well-intentioned internal builds are now slowing GenAI down.
Not because the teams aren’t skilled enough, or because the tools don’t work. But because the weight of managing complexity—across models, tooling, integrations, and infrastructure—is pulling attention away from what matters most: getting GenAI into production and delivering measurable business value.
It’s time to ask a hard question. Is your internal GenAI stack enabling the business to move forward, or is it becoming the roadblock everyone is waiting on?
The decision to build internally is almost always rooted in solid reasoning.
Enterprise IT teams foster a strong engineering culture and are used to managing complex systems. They understand the nuances of their environment better than any outside vendor. When it comes to sensitive data, complex, multi-system processes, or heavily regulated domains, maintaining control over infrastructure can feel essential.
We’ve heard all the justifications for keeping development in-house:
These are valid arguments, and in some cases, they genuinely justify a custom approach. However, they also come with trade-offs that need careful exploration.
Building your own GenAI environment means taking on a long list of responsibilities, many of which are still evolving as fast as the technology itself.
You’re not just choosing a model. You’re selecting retrieval frameworks, vector databases, prompt orchestration tools, and ways to fine-tune and version your outputs. You’re also managing deployment patterns, observability pipelines, security layers, performance monitoring, and usage costs. If you can do all that while fielding new business requests, responding to POC fatigue, and staying on top of a constantly shifting model landscape, then kudos to you and your team.
But that’s just to keep one or two apps afloat.
If your architecture is bespoke, every new use case may require new pipelines. Every different model you use introduces risk. Every system integration becomes a costly one-off exercise.
Before long, it’s not just AI experimentation that’s happening slowly; it’s the delivery of every project. Business teams are stuck waiting for another round of integration work. Your engineers get pulled into infrastructure instead of optimization, and those promising early demos never quite make it over the line into something usable.
What began as a strategy to move faster has become an unexpected bottleneck.
This tension isn’t theoretical. Our survey of 400 enterprise leaders found that while 85% had GenAI initiatives underway, only 30% had moved beyond pilots and put custom solutions into production. Those that had were significantly more likely to report achieving ROI and exceeding their expectations in the process.
The lesson here is simple: success with GenAI isn’t about technical completeness and ownership. It’s about getting real applications in the hands of users.
The companies seeing the most value aren’t always the ones with the cleverest engineering and most sophisticated architecture. They’re the ones who’ve figured out how to move fast, build reliably, and iterate in production.
That’s where IT can have the most significant impact, not by owning every component but by creating the conditions for GenAI to scale.
In this situation, platform thinking becomes useful.
Many teams worry that adopting a platform approach means giving up control or settling for “one-size-fits-all” tooling, but that’s a false trade-off. The right platform should offer flexibility and ultimately composability, which is the ability to plug in your own models, integrate your own systems, and maintain the governance, security, and observability you need, without reinventing the foundations every time.
At Vertesia, for example, we work with enterprise teams who want flexibility and want it now. Our platform supports multiple LLMs and inference providers—often in the same app or solution— including both commercial and open-source options. It handles areas such as prompt orchestration, RAG pipelines, model evaluation, and deployment without locking teams into a single stack or architecture.
More importantly, it allows internal teams to focus on high-value work such as optimizing model outputs, refining use cases, and enabling business adoption. The result is faster delivery, less technical debt, and clear alignment with the people asking for help.
No one’s suggesting that technical teams shouldn’t be deeply involved in how GenAI is built and deployed. Their leadership and skills are essential.
However, as the demand for AI solutions grows, the issue becomes one of scale. Are you spending your time on infrastructure or impact? Are you building to prove a capability or to deliver results?
The best use of your team’s time isn’t managing AI plumbing. It’s working with business stakeholders to solve real problems, defining repeatable patterns, and governing GenAI across the enterprise to ensure quality, security, and scalability.
Choosing a GenAI platform doesn’t mean choosing someone else’s roadmap. It means accelerating your own.
As organizations move past the hype and start expecting results, IT and AI leaders are under pressure to deliver quickly, responsibly, and at scale. That’s not easy, especially when you're also trying to architect a GenAI stack from scratch.
So don’t try to do it all yourself.
With the right foundation in place, your team can spend less time wiring systems together and more time enabling real outcomes. You can maintain the control you need, without shouldering all the complexity alone. You can move from exploration to execution without compromise.
The potential of GenAI is too important—and the expectations too high—for IT to carry the burden alone. Let’s give your team the tools to lead without slowing down.
To learn more about how to scale your GenAI initiatives, download this guide.