In many SMEs, AI features do not break in an obvious way. They ship, they run, and they often deliver some value. The difficulty starts later, when behaviour needs to be adjusted and no one is quite sure who should make that call.

With traditional applications, responsibility is usually clear. Requirements change, code is updated, and teams know how to move forward. AI complicates this familiar flow. Behaviour can shift without an obvious release, outcomes are not fully predictable, and small adjustments can have wider effects than expected. When those changes start to matter, uncertainty around ownership tends to slow everything down.

Let's look at how behaviour decisions around AI differ from conventional software decisions, why they often end up in the wrong place in SMEs, and how responsibility can be assigned without adding heavy process.

Why AI behaviour decisions feel harder than expected

In a typical app, e.g. a C# system, behaviour is mostly the result of explicit logic. If something changes, it is usually possible to point to a specific modification and understand its impact. Testing and rollback follow established patterns.

AI features behave differently in practice. Outputs depend on models, prompts, data, and configuration that evolve over time. The system may still be technically healthy while producing results that feel off, inconsistent, or misaligned with expectations. This creates decisions that are not about correctness in the traditional sense, but about acceptability.

Those decisions tend to surface gradually. Users question results, edge cases accumulate, and confidence erodes. At that point, someone needs to decide whether behaviour should change and in which direction. Many SMEs realise too late that they never agreed on who that someone is.

How behaviour decisions default to the wrong place

When ownership is unclear, decisions tend to drift toward whoever is closest to the system. In many cases, that means developers adjusting prompts, thresholds, or routing logic simply because they have access and context.

This approach works for a short time. Over time, it creates tension. Developers find themselves making calls that affect product behaviour and risk tolerance without clear authority. Business stakeholders react to outcomes without visibility into constraints or trade-offs. Changes slow down, not because they are impossible, but because responsibility feels uncomfortable.

The result is rarely outright failure. More often, behaviour stabilises prematurely. The feature stays in use, but meaningful improvement becomes difficult.

Why developers cannot reasonably own these decisions alone

Developers are well placed to implement changes, but AI behaviour decisions usually extend beyond implementation. Choices about how conservative a system should be, how much uncertainty is acceptable, or how results should be presented to users are product and organisational questions.

When these decisions land implicitly on engineering teams, the safest response is often caution. Changes are delayed, experiments are avoided, and the system remains static. This is not a lack of initiative, but a rational response to unclear accountability.

Clear decision boundaries allow developers to focus on execution while reducing the personal risk associated with behaviour changes.

Why business owners also need technical framing

Placing all behaviour decisions on the business side does not solve the problem either. Without an understanding of technical constraints, requests quickly become contradictory. Expectations around accuracy, speed, and cost are difficult to balance without context.

This often leads to frustration on both sides. Technical teams feel pressured to deliver incompatible goals, while business stakeholders feel that progress is slower than it should be. What is missing is not authority, but a shared decision framework.

Effective ownership does not mean a single decision-maker but an agreement on who decides which kinds of questions.

A practical ownership model for SMEs

Rather than assigning “AI ownership” as a single role, SMEs tend to do better by separating decision types. This keeps responsibility clear without adding layers of approval.

Below is a decision ownership model that reflects how AI systems actually behave once they are in use.

* Example AI behaviour decision ownership model

Decisions led by business and management

• acceptable risk levels and failure tolerance
• how conservative or assertive AI behaviour should be
• how outputs are positioned and explained to users
• when behaviour changes require wider visibility

Decisions led by IT and engineering

• how behaviour changes are implemented technically
• monitoring, logging, and rollback mechanisms
• performance, scalability, and cost controls
• integration boundaries within the C# system

Decisions that require joint agreement

• whether observed behaviour warrants change
• trade-offs between quality, responsiveness, and cost
• prioritisation of improvements versus stability
• timing of behaviour updates relative to releases

Decisions that should never be implicit

• silent behaviour changes without visibility
• adjustments without rollback options
• changes made without a clear reason or owner

This model does not introduce new roles, it only makes existing responsibilities explicit, which reduces hesitation and rework.

Why this matters specifically for SMEs

In organisations of 50–100 employees, formal governance structures are usually light by necessity. That does not remove the need for clarity. AI introduces ongoing behaviour decisions that, if left implicit, slow teams down more than any formal process would.

Clear ownership tends to have the opposite effect. When teams know which decisions they are responsible for, iteration becomes safer and more predictable. Changes are discussed earlier, and behaviour evolves instead of freezing after the first release.

Free consultation with Blocshop

We usually get involved when AI features are already live and behaviour changes have become difficult to manage. The work is rarely about changing models, rather about clarifying where decisions are getting stuck, and how small structural adjustments can make iteration possible again.

If you are dealing with AI features where behaviour feels risky to change or decisions keep bouncing between teams, you can schedule a free consultation with Blocshop to identify where friction can be reduced.