Why Your Department Heads Need to Drive AI Requirements, Not the IT Department

Imagine a Problem Statement workshop led by the IT department (not by a business lead, not with any business users present). They dutifully write down everything the business expert says over the course of the workshop.

They document all the desired features, issues, and concerns the business expert has about the problem. The IT department may shape the conversation with their own concerns and technical limitations, but they are there to facilitate the process. The problem is that the expert they talk to is themselves.

The IT-Led Model is Failing on the Numbers

In other words, generative AI frequently fails to produce tangible value not because the vendor posited an unsound method, or the algorithms broke down, but because too much of the engineering starts in a vacuum of intelligent requirements. This is where good-old-fashioned software development tenets ought to jump to your generative AI’s rescue.

IT Can’t See What it Can’t See

IT teams can provide security, hosting, and integration support. However, they are not the ones ingrained in the everyday decisions that ensure a process runs smoothly. For instance, a claims adjuster is aware of the exceptions that require human intervention without fail.

A finance analyst is aware of the line items that are typically flagged, even if no explanations are documented in any official policy. These “unwritten” rules are what govern a department and reside in the minds of the staff members performing the tasks rather than a technical architecture.

When IT, devoid of these insights, drafts requirements, it often leans towards features using a language like “develop a chatbot with retrieval-augmented generation and a response time of five seconds”. While this provides a precise technical overview, it does little to ensure functional adoption of the solution.

What should be detailed instead is something in the lines of “reduce claims processing time from eight to one day” or “decrease the time required for a quote from 48 hours to a few hours”. These requirements are easier for a department to take ownership of, quantify, and rally behind to their leadership, as they are grounded in business objectives.

From Domain Knowledge to Executable Requirements

Most heads of department understand the details of the process but do not have a formal approach to transform this knowledge into something an IT department would implement. They understand their workflow very well.

They know where the bottle-necks are, they know how they can deliver “good” production, they know which KPIs matter for the business, but putting this all together in a form of a requirements document that does not fall apart after the first contact with engineering is a completely different skill.

This knowledge translation gap is where a lot of AI projects silently die and this gap is exactly what ai strategy consulting services are built for: giving an executive a structured, lightweight approach instead of a 60 pages technical specification he was never even trained to produce.

This does not mean that IT is out of the picture. IT will still own the LLM infrastructure, the integration architecture, and the governance that makes everything compliant with the EU AI Act and GDPR. In regulated industries, there is still a model risk manager with a documented, responsible business owner signing off how a model behaves in production.

IT will be more of a facilitator than a guardian, but a lot of the same responsibilities will remain. They just stop being the department that writes a wish list for a workflow they are not responsible for running.

Shadow AI is a Symptom, Not a Personality Flaw

Employees often bypass the established route not because they feel like taking chances, but for the very reason that the established route is too slow or fails to capture their immediate intent. Every unofficial tool or application an employee subscribes to represents an organizational demand that has gone unheard in the official circumstances.

This is the actual cost of bring-your-own-AI, which isn’t just compromised data (bad enough); it’s the evidence that your data capture mechanism for AI needs is flawed.

It is not a question of making department heads the official conduit for AI demand decisions. Rather, when people realize that going through the proper prior authorization process is a better way to get an AI tool implemented than by copying and pasting their best customer directory onto some unsanctioned web tool, you will see people opting for the prior authorization process. If they don’t, then one-page ethics guidance documents and instructions on cautionary tales aren’t going to be of much help.

Ownership Drives Adoption, and Adoption is the Whole Game

Many AI governance frameworks overlook one thing: the return on investment comes from using the model, not its accuracy. A tool with 95% accuracy that nobody in the department opens is worth less than a 75% accurate tool that is integrated within existing workflows. If department heads have not pre-set the key performance indicators, then developing their business processes availability map will determine where AI can actually help, and IT will have to guess where it might do so.

They must be the owners throughout the life cycle of the model. The same person who developed the success metric is the one who’s going to push model acceptance in their department, because they are the ones who are held accountable.

C-level sponsorship is also very important. If the CEO/COO only endorses the CIO’s strategy of AI, then the department head is not authorized to negotiate the specs if they have to be a bit different from what works in their department. The department head must be told by the C-level sponsorship: this is your requirements document, your KPI, your accountability. IT will develop it. You are the owner of the results.

The department that gets the most out of their AI investments is not the one with the most advanced technical specs, but the one where the workflow users designed their own requirements, and IT developed exactly what was asked of them.