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What Happens When the User is No Longer Human?
How software is selected is changing, and enterprise governance needs to catch up.
By Slavena Hristova, Director of Document AI, ABBYY
Enterprise software selection has always been an uneasy compromise between the people who own the architecture and the people who own the work. That tension has shifted into something more complex because business users are no longer just selecting tools outside the IT department’s view. They are building them, and the procurement model that most enterprises still rely on was not designed for this.
While working closely with organizations navigating this shift, what I see most often is not reckless behavior on the part of business teams – it is rational behavior inside a system that is moving faster than the governance structures meant to contain it. Understanding what is driving that gap, and how to close it, has become one of the more pressing strategic challenges for CIOs right now.
The build trap disguised as empowerment
The first shift happened when low-code tools and cloud delivery made software selection frictionless. Business users became “citizen developers”, empowered to move fast by creating apps, automation workflows, and business solutions. While “citizen developers” embraced the freedom from lengthy IT projects, many IT departments viewed the shift as a loss of control. Most enterprises acknowledged this, debated shadow IT policies, and moved on, because at the end of the day the low-code platforms themselves were still vetted against standard procurement policies and procedures.
The second shift is more consequential, and many organizations have yet to catch up. Large language models (LLMs) and accessible coding interfaces have made it possible for non-technical teams to build functional tools, not just select them. Analysts and operations leaders now prompt their way to custom workflows, document parsers, and data extraction pipelines that did not exist yesterday and will not appear in any vendor contract tomorrow.
The term “vibe coding” entered the conversation for a reason. It describes the pattern accurately: intuition-driven, iterative, and built without the design constraints that make software maintainable. A team with a pressing document processing problem, a deadline, and access to a capable LLM will produce a solution. It will work – until it doesn’t. When it fails, or when an auditor asks how a decision was made, or a regulator requests a data lineage trace, the answer will not be available.
This is the build-vs-buy trap at its most expensive. The cost extends well past the time spent building, into the rework, the compliance exposure, the inability to explain outputs, and the technical debt that accumulates until it becomes a crisis. Yet even that label undersells what’s happening: rather than a build decision that landed badly, this is an expensive, ungoverned compound of “build” and “shadow IT”.
What governance loses when building happens at speed
The governance failure here is that locally rational decisions, made quickly and in isolation, produce an incoherent enterprise architecture that nobody planned, and nobody fully controls.
Consider document-heavy processes within a bank as a concrete example. KYC onboarding, commercial lending, and trade finance each depend on accurate, validated, traceable document data. When each of those functions builds or adopts its own document pipeline, you get three sets of validation logic, three data models, and three different answers to the same regulatory question. Then when the bank needs to demonstrate consistent, explainable decisioning across products and regions, its fragmented pipelines cannot produce it because the architecture was never designed to converge.
The compliance cost of this fragmentation is not theoretical. Under frameworks like the EU AML Regulation, financial institutions must demonstrate ongoing, risk-based monitoring with consistent due diligence standards across customer segments and geographies. That requires a document layer that behaves consistently everywhere. Five bespoke pipelines built by five separate teams under deadline pressure will not satisfy that requirement, regardless of how well each one works in isolation.
The problem compounds when you try to fix it retroactively. Reconciling fragmented document workflows after the fact is slow, expensive, and often incomplete. It is far cheaper to define the governance boundary before the building starts than to audit and consolidate later.
Agentic automation will accelerate the problem, then reshape the solution
Here is where the trajectory becomes more interesting, and the stakes get higher.
AI agents are entering enterprise environments. They are not chatbots responding to prompts. They invoke, evaluate, and select capabilities at runtime, within defined constraints, to automate decisions rather than just perform tasks. A document-intensive process that currently requires a human to collect, classify, extract, validate, and route information is a natural target for agentic automation. Agentic workflows are being deployed at a pace that traditional 12-to-18 month procurement cycles can’t keep up with. Teams no longer wait months to prove value: a working integration, a called API, a selected tool, can happen within a sprint, and the expectation of what “fast” looks like has moved with it.
The role of the CIO and enterprise architecture function is evolving away from approving individual tool selections, toward defining the guardrails within which autonomous systems can make those selections themselves. Governance must evolve beyond control to setting spending boundaries, security and compliance requirements, data residency rules, and integration standards, then giving AI agents the flexibility to choose the most appropriate tool within those guardrails.
This is no longer a distant scenario. Highly regulated industries and sensitive use cases will require strict, centralized tool selection for the foreseeable future to satisfy compliance mandates. Yet, for most other business operations, the future of procurement lies in defining a flexible governance framework within which autonomous agents can operate. Organizations that establish these guardrails today will benefit from agentic scale rather than suffer from its complexity.
Niche solutions versus use-case-agnostic platforms: The tradeoff that matters more than it used to
The shift toward agent-led tool selection surfaces a tradeoff that enterprise buyers have always faced but now need to think about more carefully: niche point solutions versus broader, use-case-agnostic platforms.
A niche solution built specifically for one vertical or one common use case can be excellent at that task. A specialist tool for invoice extraction or passport verification, for example, may outperform a general-purpose platform in pure accuracy metrics within that narrow domain. But when an agent, or a business team, or an expanding automation program needs to move from one use case to five, a portfolio of niche tools creates its own burden.
Integration complexity multiplies. Security reviews repeat. Training and configuration effort accumulates across each new vendor relationship. And when an auditor asks for a unified view of how documents were processed across onboarding, lending, and trade finance, a collection of point solutions rarely produces a coherent answer.
A use-case-agnostic Document AI platform addresses this through economies of scale that compound over time. The integration work done once for KYC applies directly to lending. The validation logic configured for bank statements transfers to trade finance documents with modification rather than reconstruction. The audit trail is consistent because the underlying platform is consistent. Ramp-up from one use case to the next becomes a configuration exercise rather than a new procurement.
For organizations moving toward agentic automation, the platform question becomes even more critical. An agent operating across onboarding, compliance, and operations needs a document layer it can call reliably, with predictable outputs, consistent data models, and traceable decisions. A mosaic of point solutions cannot provide that. A governed, API-first Document AI platform can.
The strategic answer is not to avoid niche solutions entirely. In some contexts, they are the right choice. But the default evaluation question should shift: can this platform grow with us across use cases, and will the governance overhead stay manageable as it does?
Enterprise governance as the destination, not the constraint
Enterprise governance depends on four areas that CIOs need visibility into before agents begin selecting tools independently: spend, control, observability, and compliance.
Starting with spend, CIOs need a defined budget envelope and cost ceiling per workflow, so agentic scale doesn’t translate directly into runaway AI spend. This is the fastest-growing line-item CIOs are being asked to justify right now, and it is also the easiest one to lose control of once agents, not people, are initiating usage. Next is control. CIOs need to be sure they have the ability to enforce which data gets processed, how models behave, and when automation must defer to a human. This is where observability comes into play. Observability into your automation workflow makes sure you can trace an output back to the logic that produced it, so a failure can be diagnosed rather than reconstructed after the fact. Lastly is compliance, the ability to audit, explain, and defend an automated decision – non-negotiable once decisions are made at machine speed and scale.
For CIOs, the practical implication is a shift in how they think about their role in software selection. The goal is no longer to approve or block every tool decision. It is to define these four boundary conditions, then let the organization, and eventually its agents, move quickly and safely inside them.
The instinct to centralize every decision slows the business and pushes build behavior further into ungoverned territory. And the instinct to decentralize without constraints produces fragmentation, compliance risk, and cost that stays invisible until it becomes unavoidable. The space between those two failure modes is where sustainable governance lives.
Define the guardrails. Choose the platforms that operate well within them at scale. Let the business, and eventually the agents, move fast inside those boundaries. That is how software gets chosen well in the environment we are already operating in, and it is the only model that will hold as agentic automation continues to accelerate.
Slavena Hristova is the Director of Document AI at ABBYY. She helps innovation leaders understand how to improve document-centric processes and increase value to customers with purpose-built AI. Slavena offers deep expertise in the areas of AI for documents, text recognition, and OCR, information and document management.
With over 20 years of experience leading ABBYY’s document understanding, Slavena focuses on industries where documents fuel the business such as transportation and logistics, healthcare, insurance, and financial services.
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