AI Is Saving Hours at the Desk and Creating Work in the Boardroom

The same trend, seen from two altitudes

I spend most of my time running businesses, and that shapes how I look at AI. The technology is useful to me when it changes a real workflow: answering a lead faster, drafting a routine message, organizing information a team actually uses. Over the past two years, that kind of adoption has moved quickly. People are handing repetitive tasks to AI agents and getting real hours back. At the same time, boards are starting to treat AI as a material risk. The share of S&P 500 companies disclosing AI risk reached 72% in 2025, up from 12% in 2023. These two trends look separate. They are the same trend. The adoption happening at the task level is what creates the obligation at the oversight level.

The task-level gains are real

AI handles specific, repetitive work well. Summarizing material. Drafting routine communication. Sorting records. Answering common customer questions. Adapting one piece of content for several platforms. Research on workflow automation shows AI for repetitive tasks can free up 20 to 40 percent of employee time. The wins come from narrow jobs with clear inputs and outputs. The failures come from vague assignments where nobody defined what good looks like. Human review still belongs in the loop. Sensitive decisions, customer commitments, financial numbers, and anything with legal exposure need a person who owns the output.

Adoption raises the stakes for governance

Every workflow you hand to AI increases the amount of your operation that runs on systems your leadership team must understand, supervise, and answer for. The total number of AI risks disclosed by S&P 500 companies jumped fivefold to 408 in 2025. Reputational risk tops the list. One analysis found that 97% of S&P 500 companies mention AI in their annual filing, while only about 3% document a governed process for managing AI risk: a named policy, standard, or committee tied to a specific control. Risk you have named but have not assigned is risk you have chosen to carry. Somebody owns it by default, usually the CEO.

Why a single AI expert on the board falls short

The instinct at many companies is to hire one director with AI credentials and point the AI questions at that seat. Company filings show fewer than 3% of directors have AI expertise, and only 23% of executives rate their board as highly fluent in AI. The single-expert model still misreads how AI enters a business. AI runs through hiring, pricing, customer service, credit decisions, and operations. Oversight has to be distributed across the full board, the same way financial literacy is. In one study, 71% of directors said their boards need stronger AI skills for effective oversight.

• What business problem does this AI system address, and what is the baseline we measured before deploying it?
• Who owns the output when the system is wrong?
• Where does human review sit in the workflow, and who decided that?
• What happens to customer trust if this system fails publicly?

The management layer decides whether the gains compound

There is a third layer between the individual task and the board: the daily management routines of the company. If your team's administrative output doubles because of AI, and your meetings, performance reviews, and accountability structures stay exactly as they were, you are evaluating people on activity that machines now handle. At my companies, we run on EOS. When we put a tool into a workflow, it gets a measurable job. We define the problem, establish the baseline, and check whether the result improved. If it did, we document the new process and train to it. If it did not, we pull it. "The system did it" is never an acceptable answer to a customer.

• Meetings: if AI prepares the summary, spend the meeting on decisions, exceptions, and ownership.
• Performance reviews: measure judgment, customer outcomes, and follow-through.
• Accountability: name the human owner for every AI-touched workflow.

A practical path

You do not need a transformation program to start. Inventory where AI already touches the operation, including the unofficial uses. Give every AI workflow a measurable job and a human owner. Put human review where the consequences are: customer commitments, money, safety, and legal exposure. Build board-wide AI fluency instead of outsourcing it to one seat. Update one management routine this quarter and measure whether decisions got faster or better. The operating standard I apply to everything applies here without modification. Set a clear standard. Build the system to support it. Measure whether it works. Keep improving.

Read the original

This essay was published on X on October 21, 2026. The full version, with the chart, is also on Brad's site.

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