The leadership question changes when AI moves from producing an answer to taking an action. A weak summary can be corrected. A supplier commitment, an altered customer record or an instruction sent downstream can create consequences before anyone notices.
Technical access is not decision authority. I believe leadership must decide what AI may do, when it must stop, and who remains answerable—not leave those choices hidden inside a prompt.
For Saudi enterprise leaders, this is a practical management question, not a distant debate about artificial intelligence. Connecting a model to business systems means connecting it to existing permissions, obligations and relationships.
SDAIA’s AI Ethics Principles, September 2023 edition provide a reference point. They address accountability across the AI lifecycle, intervention by authorised users, and human oversight for decisions whose effects are irreversible or difficult to reverse. This is a reading of that dated edition, not a claim about the latest rules or legal compliance. The leadership challenge is enduring: responsibility must remain identifiable, even when execution becomes automated.
Define authority before autonomy
Before discussing an agent’s capabilities, I would write down its decision rights.
What may it read? What may it recommend? What may it change without approval? Which actions require a named person’s authorisation? Which actions are prohibited altogether?
These are different permissions. Combining them under a broad instruction such as “handle procurement” hides the decisions that matter.
An approval threshold should also reflect more than transaction value. A modest purchase involving sensitive information or an unfamiliar supplier may deserve closer scrutiny than a larger, routine order. Context matters more than a single convenient number.
Make oversight meaningful
“Human in the loop” can sound reassuring while describing very little.
A reviewer needs enough information to understand the proposed action, enough time to challenge it, and genuine authority to refuse. They should see the relevant evidence, uncertainty and exceptions, not just a polished recommendation with an approval button.
The system must also stop when approval is unavailable. Otherwise, oversight is an aspiration rather than a control.
Consider a hypothetical Saudi enterprise using AI to support purchase requests. The first workflow might check completeness, retrieve approved supplier information and prepare a recommendation. It would not create suppliers, amend bank details or release payment.
An authorised procurement colleague would review exceptions and approve the next step. Missing evidence, conflicting records or an unexpected request would trigger escalation, not improvisation. This is an illustrative design, not a claim about a Siyada Tech deployment.
Start with the smallest useful workflow
For me, Siyada Tech’s boutique approach should mean precision about the problem and its boundaries, rather than a general argument about team size.
The starting point is the smallest workflow that creates useful value while keeping authority understandable. One process, a clear owner, explicit permissions and a way to measure failure can teach more than an impressive demonstration spanning several departments.
Anthropic’s guidance on effective agents makes a related engineering argument: start with the simplest solution, adding complexity only when necessary. Sometimes the right answer is a predefined workflow. Sometimes it is assistance without autonomous action. This is provider guidance, not independent proof that any design will succeed.
Keep accountability after launch
Leadership does not end at approval to deploy.
Someone must own the workflow’s outcomes, review incidents, monitor exceptions and decide whether its permissions should expand, contract or be withdrawn. Logs should help reconstruct what happened, but a record is not a substitute for an accountable owner.
Anthropic’s early framework for trustworthy agents also emphasises human control and visibility. I see a practical connection to Vision 2030’s capability-building ambition: using more powerful tools should strengthen institutional judgment, not make responsibility disappear.
I would ask five questions before authorising a live workflow:
- Who owns the business outcome?
- What can the system never do?
- When must it stop and ask?
- Can the reviewer meaningfully disagree?
- How will we detect and contain a mistake?
These questions do not promise legal compliance or eliminate risk. They make delegation explicit.
I want AI to extend organisational capability without making responsibility harder to locate. The strongest leadership decision may be to grant more autonomy. It may equally be to draw a firm boundary.
Either way, that decision belongs to us. If you are exploring this question, start with one workflow, not a promise of autonomy everywhere.