AI usage has only become more widespread over the years, and enterprises across different sectors have been leading full-scale integrations into their workflows. Once existing purely in labs, tools built on this technology are now becoming a must-have capability across more and more industries.
This evolution marks a shift in how companies adopt new technology. Executives no longer see AI as a potential future investment, one that may or may not fit in their operations, but as an immediate and necessary tool to improve internal efficiency, manage risk, and lead to new and advanced forms of productivity.
Nikita Kotsehub, a forward-deployed engineer who, at Palantir Technologies, has seen this new paradigm firsthand, and over the years he's been helping companies embed AI into their operations. Through hands-on deployment, communication with technical and administrative stakeholders, and making sure humans remain involved throughout the process, he aims to show how this technology can deliver quality, auditable results.
Establishing Practical AI in the Enterprise
In the early stages of enterprise adoption, company engineers would build AI prototypes, run them through controlled tests, and produce presentations that showcased impressive technical promise.
Yet these efforts struggle to go beyond isolated pilots. The technology existed in fragments, often used to sift through datasets or take care of mundane tasks, but these integrations into more encompassing operations remained out of reach for many.
The problems rarely came down to their technical backbone. Navigating a complex regulatory landscape, fragmented training data infrastructures, and a lack of internal know-how often meant that many tools couldn't make the leap from concept to production.
Nikita Kotsehub's work helped bridge that gap. As a forward-deployed engineer at Palantir, he worked directly with Fortune 100 companies to bring AI into environments with large regulatory standards and resistance to new technology. His approach focused not on building new tools in isolation, but on improving and updating existing processes.
Incorporating LLMs into Business Workflows
At the core of Kotsehub's enterprise transformation work is the deployment of large language models (LLMs). These systems can go through large sets of data, analyze the information within them, and create newly formed text, which makes them very useful in corporate settings based around heavy documentation, as they can parse lengthy contracts, extract structured (or unstructured) information, and draft summaries or recommendations at a speed faster than any individual worker.
At Palantir, Kotsehub's team built tools designed to allow these LLMs to parse policy and supplemental documents, automatically extracting the key information underwriters needed. This sought to reduce the time analysts spent searching through documents, allowing them to focus on higher-value decision-making instead of manual data gathering.
Kotsehub also made sure that the final system could guarantee outputs that were consistent and accurate. Since LLMs don't automatically verify their outputs, they can produce confident but ungrounded information, commonly called "hallucinations"—a risk amplified in sectors with heavy regulatory standards or legal requirements.
To counter that, he built review mechanisms where every AI-generated output was auditable and traceable to its source data. Human reviewers remained involved in each step of the process, verifying key decisions and checking that the outputs followed strict legal and internal standards.
The result was a workflow that preserved precision while delivering the efficiency gains that made AI viable at enterprise scale.
Scaling Lessons from Early Adoption
Through his deployments at Palantir, Kotsehub learned that expanding AI beyond pilot stages depends less on model sophistication and more on organizational structure. Early wins matter, but they only translate into long-term adoption when companies have the right people and processes in place to sustain them. His work often began with setting small, achievable goals (like time savings or accuracy gains) that, when successful, gave executives the confidence to invest further in this technology.
Kotsehub also highlights the need to establish a basic internal literacy to properly support those systems. He and his team interacted heavily with different stakeholders, both technical (engineers and data scientists) and administrative (governance stakeholders and executives), to set up a shared understanding that directly linked results to business objectives. That collaboration turned what might've been a series of disconnected initiatives into coordinated strategies built around consistent, realistic expectations on what this technology could achieve.
Kotsehub's projects also made sure all the surrounding governance and tooling were properly configured to meet regulatory requirements. This structure helped clients reassure stakeholders of these new systems as they began expanding across different departments.
"Scaling AI means proving ROI early and bringing the whole organization along," Kotsehub says. "Culture and governance matter just as much as the technology itself when you're trying to make AI work inside an enterprise."
Designing for the Human-in-the-Loop Future
Kotsehub is now concentrating on making sure the systems he and his team produce complement AI's capacity for automation with regular human oversight. His work on Palantir's enterprise platform centers on embedding "human-in-the-loop" mechanisms into regular operations. In practice, this means building tools where steps like customer communications always receive human review before being executed.
The next phase of incorporating AI in these settings, he believes, isn't about eliminating human control; it's about incorporating in an intelligent and organized manner so that oversight becomes a built-in part of the process from start to finish.
By keeping people within the feedback loop, organizations safeguard against the errors and reputational risks that can come from unchecked automation. These systems are designed to preserve the reliability, ethics, and accountability that enterprises demand while allowing AI to handle the intense demand modern operations face.
As Kotsehub notes, "Enterprises adopting AI must focus on trust and reliability, not just speed."
Making AI a Reliable Part of Enterprise Operations
The trajectory of AI in enterprise settings will only be successful when companies can balance their need to speed up their workflows with systems that make those processes accountable and traceable. Nikita Kotsehub's work shows that, if this technology is built to support clear business needs and has human oversight built into it, it can become a key part of a company's foundation and not just a mere experiment.
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