The Entrepreneur Wants Speed. The Enterprise Wants Control. Agentic AI Needs Both.

Written by Mostafa Elkourechy,  Director of AI & Consulting Operations, SDG Group Middle East 

Entrepreneurs want AI agents that move fast. Enterprises want a named person accountable for every decision. The winning design doesn't pick a side. Agents draft, humans release.

Picture an AI agent that sets up a group's financial consolidation, the machinery that turns dozens of entities into one set of group numbers. It makes a good example because the stakes are obvious. One wrong configuration and the board signs off on the wrong numbers.

When we design agentic solutions like this at SDG Group, the hardest decision is rarely the model or the prompts. It is one line in the design:

The agent never touches the live system.

It can read everything and propose everything. It can't execute anything.

That line is what makes the whole solution trustworthy. I believe it is the most underrated design decision in enterprise AI.

Everyone is turning up the wrong dial

Most conversations about agentic AI treat autonomy like a volume knob. Turn it up and you get more value. Turn it down and you get more safety. The goal, the thinking goes, is to push the knob as far right as the risk team will allow.

That framing is why so many pilots stall.

Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. The same Gartner research expects at least 15% of day-to-day work decisions to be made autonomously through agentic AI by 2028, up from 0% in 2024 (Gartner press release, 25 June 2025).

Both predictions can be true at once. The demand is real. What's missing is a design that lets a CFO, a regulator or a board say yes.

Asking “how much should the agent do?” doesn't get you there. A better question is:

Where exactly does the agent's work end and a human's accountability begin?

The entrepreneur–enterprise dilemma

I spend my days between two cultures. As an operator at a consulting firm, I work with enterprises where nothing moves without an approval trail. As an entrepreneur, I've built ventures where speed beats process every time.

Each culture gets agentic AI half right.

Entrepreneurs are right that agents should do the heavy lifting: the research, the drafting, the configuring, the reconciling. Keeping a human in the loop for every keystroke kills the economics.

Enterprises are right that someone must own the outcome. “The AI did it” is not an answer any auditor, regulator or shareholder will accept.

The mistake is treating these as a trade-off. They are two layers of the same system.

The rule: Agents Draft, Humans Release

This is the operating rule we design around. It applies to any function where a decision carries consequences.

1. Separate thinking from doing. The agent's job is to think: analyze, propose, draft, configure on paper. A separate, deliberately boring mechanism does the doing. It is a controlled workflow, a template or a script that runs only what was approved. The intelligent part never holds the keys to production, and the part holding the keys has no intelligence to improvise with.

2. Every output is a difference a human can read. An agent shouldn't hand over a black box. It should hand over a clear before-and-after: here is what changes, here is why, here is what it touches. If a manager can't review it in minutes, the agent hasn't finished its job.

3. Release is a named human act. Someone specific presses the button, and their name stays attached to it. That isn't bureaucracy. It is what turns an AI output into a business decision.

In other words, the entrepreneur gets the speed and the enterprise gets the control.

It works far beyond finance
The pattern is general because accountability is general:

In each row, the agent removes most of the effort. The human keeps all of the accountability.

“Won't this slow us down?”

It's the first objection I hear. The design is built to do the opposite, for three reasons.

• Reviewing is faster than doing. A controller who checks a clear, explained batch of entries spends less time than one who prepares them from scratch.
• Trust compounds. Every release is logged, so you build evidence. After enough clean cycles, low-risk categories can move to automatic release. You widen the agent's authority deliberately, category by category, on the record.
• Risk conversations get shorter. When the risk committee sees that the agent physically cannot execute, the debate moves from “should we allow AI?” to “which releases should this person own?” Leaders can answer that second question in one meeting.

This matters even more in our region. The UAE adopted its National Artificial Intelligence Strategy 2031 with the goal of becoming a global AI leader (UAE Cabinet). When adoption runs at that pace, a design that shows who released what, and when, lets you move fast without betting the license.

Three questions to ask before you approve the next AI agent

If you're sponsoring or evaluating an agentic AI initiative, put these to your team:

1. What can this agent never do? If the answer is “nothing, it's fully autonomous,” you've found your future cancellation.

2. What does the reviewer actually see? A readable before-and-after, or a result they have to trust blindly?

3. Whose name is on the release? If nobody's is, nobody owns the outcome, and the project will stall the first time something goes wrong.

The real competitive edge

The next few years won't be won by the organizations with the most autonomous agents. They'll be won by the ones that design the clearest line between what the machine proposes and what a person decides.

That line is what I call the decision layer. It is where the entrepreneur's speed and the enterprise's control stop being a trade-off.

If you're drawing that line in your own organization, in finance, operations or anywhere else, I'd like to compare notes. Reach out and let’s start this conversation.