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 

THE DECISION LAYER Series - Article 1

When I look at where AI is heading, I constantly feel a tension between two distinct mindsets. The entrepreneur in me wants AI agents that move fast, experiment, and break through bottlenecks. But the enterprise operator in me knows that a corporate board demands a named, accountable person for every single outcome.

Most people think you have to pick a side. I don't think you do. The winning design doesn't force a compromise, it splits the job: agents draft, humans release.

Consider how an AI agent handles group financial consolidation—the system that stitches dozens of corporate entities into a single set of board-ready numbers. It’s a perfect scenario to think through because the downside is so stark. A single misconfiguration in entity mapping or elimination entries, and your board ends up signing off on incorrect financials.

When we architect agentic solutions like this at SDG Group, the hardest part of the design process isn't picking the LLM or tweaking prompt templates. It comes down to a single line in the architecture spec:

The agent never touches the live system.

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

 When you first write that rule down, it feels almost counterintuitive, are we holding the AI back? But that strict boundary is precisely what makes the entire system trustworthy. In my view, it is the most underrated design decision in enterprise AI today. 

Everyone is turning the wrong dial

In most conversations about agentic AI, autonomy is treated like a volume knob. Turn it up, and you get more speed and value; turn it down, and you get more safety. The implicit goal becomes pushing that knob as far to the right as the risk team will tolerate.

That mental model is precisely why so many pilots stall in committee.

Look at the predictions coming out of Gartner: they project 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. Yet at the same time, 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, June 25, 2025).

How can both forecasts be true? Because the appetite for speed is genuine, but leaders lack a framework that lets a CFO, an auditor, or a regulator safely 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 must handle the heavy lifting—the research, data pulling, drafting, configuring, and reconciliations. Keeping a human manually involved in every single keystroke destroys the economic value of AI.
• Enterprises are right that an individual must own the result. "The algorithm made a mistake" is not a defense 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.

Beyond Finance: Applying the Pattern

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.

Addressing the Speed Objection

“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.