# Agentic AI for Executives: Four Decisions Only You Can Make

Delivery decisions belong to delivery. These four do not, because each trades a business risk against a business return, and the trade is yours.

7 min read · Beginner · Lesson 9 of 9 in By role · Updated 2026-10-02 · By Akash Das

Canonical: https://akash-coded.github.io/aws-bedrock-agentcore-strands/learn/agentic-ai-for-executives/

> **In short.** An executive leading agentic AI makes four decisions nobody below can make. They are **which work is
> genuinely AI work**, **what agents may do without a person**, **what counts as evidence**, and
> **what gets funded past the first cycle**. Four questions asked every cycle keep them true, take
> about ten minutes and defeat most of the ways these programmes fail.

![The four executive decisions in order, closing on the four questions asked every cycle](https://akash-coded.github.io/aws-bedrock-agentcore-strands/assets/learn/map-agentic-ai-for-executives.light.webp)

**In this lesson** you'll learn:

- the four agentic AI decisions that belong to leadership, and what to ask for each;
- the four questions that keep them true, and the maturity check that replaces tool counts;
- seven signals worth escalating, and a first thirty days that needs no budget approval.

## Sound familiar?

- The organisation has an agent-first directive and a portfolio of pilots, and none has reached production.
- The board pack shows one accuracy number and a count of AI tools adopted.
- The largest AI cost line arrived as a surprise, from finance.

None of these is a delivery failure. Each is a leadership decision being made by default, somewhere
below the level that should be making it.

## The four decisions, step by step

### Decision 1 · Which work is genuinely AI work

Ask it of the roadmap, not of one feature, and expect most of it to come back as rules. Three questions
in order (is there a genuine judgement call, is the volume high enough, is a wrong answer recoverable)
and the first "no" ends it. A team under an agent-first directive will build agents for things a rule
does better, and will not tell you, because you asked for agents. **Ask for** an AI-fit record per
candidate, with the rejected alternative. **Healthy answer:** two or three of your top five are rules.

![In a field a small machine with one eye waves its arms at two crows. Beside it a worker is planting a scarecrow, which would do the same job.](https://akash-coded.github.io/aws-bedrock-agentcore-strands/assets/learn/sketch-a-scarecrow-would-do.light.webp)

*Ask for agents and you get agents, even where a rule does the job better. Expect two or three of your top five to be rules.*

### Decision 2 · What an agent may do without a person

Per action, never per product, following what a mistake costs and whether it can be undone: acts alone,
acts monitored, acts inside a veto window, needs a named approver every time, or is not delegated at
all. **Ask for** the autonomy record, then the follow-up that matters: *show me the cap*. If someone
opens a prompt file rather than a code file, you have found the gap that produces incidents.

### Decision 3 · What you will accept as evidence

The decision that most often goes by default. Accept a demo and you will be shown demos; accept a single
accuracy figure and the risky slice will hide inside the average. **Ask for** a score per slice against a
derived bar, with its lower bound, plus a shadow run against the people doing the work today.

### Decision 4 · What you will fund past the first cycle

A first cycle that saves time and costs more is normal. Funding past it should depend on a trajectory (the review load falling, the re-run count falling with it) not on a promise. **Ask for** two numbers,
every cycle, from the team: the time saved and the money spent, on one line.

## The four questions, every cycle

| Ask | Of what | A good answer |
| --- | --- | --- |
| Which of these are rules? | The roadmap | Two or three of the top five, on record |
| What may it do without a person, and who decided? | Each action | Someone opens a tool's signature when you ask to see the cap |
| What are the two numbers? | Every cycle | Saving and spend, with review hours and re-runs beside them |
| What level are we, and what is the next control? | The team | A number out of six, and the first missing control by name |

The last question replaces the most misleading metric in the field, tools adopted, with a count of six
controls a team either has or does not. [The maturity model](https://akash-coded.github.io/aws-bedrock-agentcore-strands/learn/ai-delivery-maturity-model/)

## Seven signals worth escalating

A single accuracy number in a board pack · a cap that lives in a prompt · a launch date agreed before a
shadow run · tool adoption reported as maturity · a postmortem that produced a name · a cost number that
arrived from finance · a dashboard that stopped moving while work continues. None is a failure; each is a
decision being made by default.

## Your first thirty days

1. **Ask the four questions** at the next review, then say nothing for ten seconds.
2. **Ask to be shown one cap** the team says is enforced.
3. **Take a baseline**, person-days per story, on whatever starts next. It cannot be recovered later.
4. **Run the six-control check** with the team, ticking only what can be shown in ten minutes.
5. **Name who owns governance.** If the answer is "we all do", nobody does.
6. **Fund the gateway**: a fortnight of platform work that turns every cost conversation into a diagnosis.

None of this needs a reorganisation, a new function or a vendor commitment.
[Rolling out agentic delivery](https://akash-coded.github.io/aws-bedrock-agentcore-strands/learn/rolling-out-agentic-delivery/)

## Why it matters

Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027, for escalating costs,
unclear business value or inadequate risk controls. All three are leadership decisions left to default:
what counts as value, what the agent may do, and what counts as evidence.

## Try it

Your CTO proposes an "AI-first" target: 50% of new features to include an AI agent by year end. **Which of
the four decisions does this pre-empt, and what would you ask for instead?**

<details><summary>Show the answer</summary>

**Decision 1: which work is genuinely AI work.** A quota for agents rewards building them where a rule
would be better, cheaper and provable, and teams will comply without telling you. Ask instead for an
AI-fit record across the roadmap, with the rejected alternatives, and set the target on outcomes (the
two numbers) rather than on the number of agents.

</details>

## Key takeaways

1. **Four decisions are yours**: what is AI work, what agents may do alone, what counts as evidence, what gets funded.
2. **Four questions, every cycle**, and a maturity count of six controls instead of tools adopted.
3. **Thirty days, no budget**: ask the questions, see one cap, take a baseline, name the governance owner.

## FAQ

### What should executives know about agentic AI?

That most of the portfolio is not AI work; that "does it work" is a number per kind of case, derived from
what a mistake costs; that autonomy is decided per action and enforced in code, not in prompts; and that a
first cycle can save time and cost more, which is survivable only if the team reports both numbers.

### What questions should a board ask about AI agents?

Which of these are rules? What may it do without a person, and who decided? What are the two numbers,
the saving and the spend? What maturity level are we, and what is the next control?

### How do you measure AI maturity in an organisation?

By controls, not tools: a context file the agents read, a spec with a bar and an owner for every item, a
harness that blocks merges per slice, caps in tool signatures, a trace that redacts, and production
evidence with drift watched. The level is how many of the six a team can show.

### Should we mandate that teams use AI agents?

Mandate outcomes and evidence, not agents. A quota for agents produces agents for work a rule would do
better; a requirement for an AI-fit record and two numbers per programme produces the right mix.

## Apply it in your role

| If you are… | Do this | The AI-augmented shortcut |
| --- | --- | --- |
| **A forward-deployed engineer** | When a customer's executive joins, bring the four decisions, not the architecture: what is AI work, what it may do alone, what counts as evidence, what gets funded. | Ask a model to prepare a one-page brief on the four decisions for this customer. |
| **A product manager or FDPM** | Answer the four questions before they are asked: which are rules, who decided the autonomy, the two numbers, the maturity level and the next control. | Have a model rehearse the four questions with you, playing a sceptical executive. |
| **A GenAI or agentic AI engineer** | Make "show me the cap" answerable in ten seconds: a link to the tool signature and its test. | Ask a coding agent to generate a caps page from the code: each consequential action, its cap and its test. |

**Across the enterprise.** The executive team asks the four questions of the whole portfolio each quarter,
and the maturity count per team replaces the count of tools adopted.

**The ten-minute workflow.** Rehearse the review before it happens:

```text
Play a sceptical chief executive reviewing our AI programme. Ask me the four questions one at a time:
which of these are rules; what may it do without a person, and who decided; what are the two numbers;
what maturity level are we, and what is the next control. Push back on any answer that lacks a
number or a named owner.
```

## Sources and credits

| Idea | Origin | Source |
| --- | --- | --- |
| The four decisions, four questions, seven signals and first thirty days | **Original**: this manual | [For leadership](https://akash-coded.github.io/aws-bedrock-agentcore-strands/protocol/) · [Role: Sponsor](https://github.com/akash-coded/aws-bedrock-agentcore-strands/wiki/Role-Sponsor) |
| The six-control maturity check | **Original**: this manual | [Gates and Governance](https://github.com/akash-coded/aws-bedrock-agentcore-strands/wiki/Gates-and-Governance#maturity-control-not-tool-count) |
| Over 40% of agentic AI projects cancelled by 2027, and why | **Borrowed** | Gartner (2025). [Press release, 25 June](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) |
| Why a single number gets pushed | **Borrowed** | Goodhart, C. (1975). Goodhart's law |