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Businesses move beyond AI hype by designing workflows around five foundations: a clearly defined business outcome, an explicit risk tolerance, interoperable systems, decision-relevant context, and governance that sends high-risk actions to a person. Those are recommendations from Michael Clark, director of product management at Google Cloud, reported in AI Business’s September 7, 2026 article about his appearance on the Targeting AI podcast. They are attributed guidance—not a Google-wide policy, a product guarantee, or a measured outcome study.
The practical implication is straightforward: an impressive model is only one component. An enterprise agent must be able to obtain the right information, work across the systems where business data and actions live, show what it is doing, and stop for approval when the consequences exceed the organization’s tolerance for risk.
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What “beyond the hype” means in an enterprise
Enterprise AI is often discussed as if selecting a more capable model will automatically produce a useful business process. Clark’s argument is different. Reliability comes from the surrounding operating design: trust, transparency, interoperability, and a disciplined definition of what the workflow is supposed to accomplish.
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That distinction matters because an agent can generate a plausible answer while still being unable to complete the job. A customer-service agent might understand a complaint but lack access to the order system, billing record, entitlement database, or dispatch tool needed to resolve it. A finance agent might see a transaction but not have the controls required to place a hold. In both cases, model quality is secondary to the workflow’s connections, context, permissions, and review rules.
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Clark summarized two of the architectural requirements this way: “Interoperability and openness are two really key concepts, and transparency as well.” Treat that statement as implementation advice attributed to him, not as evidence that every enterprise AI deployment already meets those standards.
1. Define the outcome before choosing an agent
An AI initiative should begin with a business result that can be observed in the existing process. “Use an agent” is not an outcome. “Resolve a defined class of customer requests without transferring the case” or “prepare a claims packet for an employee’s approval” describes a target that can be tested against the current workflow.
Clark advises businesses to understand their outcomes clearly and to identify their risk tolerance. Those two decisions determine what the agent may do autonomously and what must remain subject to approval.
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Write the outcome in operational terms
- Name the user and process. Identify who initiates the work, which team owns it, and where the current process begins and ends.
- Define a completed task. Specify the record that should be created or changed, the customer or employee communication that should be sent, and the evidence that proves completion.
- Set boundaries. List the request types, jurisdictions, data classes, monetary values, or account states that are in scope. An agent should have a deliberate “not supported” path.
- Choose an initial baseline. Document how the process works today, including handoffs, waiting points, rework, and the systems employees consult. This gives the pilot something concrete to compare with.
Turn risk tolerance into an approval rule
Risk tolerance is not a general statement such as “keep humans involved.” It is a threshold tied to an action. Reading a public knowledge-base article, drafting a response, changing a shipping address, issuing a refund, and closing an account have different consequences. Classify actions by potential harm, reversibility, legal or contractual exposure, and financial impact.
| Action class | Typical control decision | Evidence to retain |
|---|---|---|
| Informational or draft output | The agent may prepare an answer or recommendation, with a person responsible for publication when accuracy matters. | Source records, prompt or task context, generated draft, and final editor. |
| Reversible low-impact change | Allow automation only after validating identity, authorization, and required fields. | Inputs, policy checks, tool call, result, and rollback path. |
| High-impact or difficult-to-reverse action | Require a human approval step before execution. | Agent rationale, supporting records, approver identity, timestamp, and final action. |
The table is a practical interpretation of Clark’s risk-based human-oversight recommendation, not a published scoring framework. Your compliance, legal, security, and process owners should set the actual thresholds.
2. Make interoperability a design requirement
Interoperability determines whether an agent can move from conversation to resolution. Business information is usually distributed across customer-relationship systems, order and billing platforms, identity services, ticketing tools, file stores, and specialist applications. If the agent cannot reach the authoritative system—or cannot write back through an approved interface—it can only describe the next step to a person.
Clark pointed to call centers as an example: “In a lot of call centers, people do things very manually, not using APIs or not using other things when they’re solving customer problems.” He said the opportunity is to give agents access to what is happening elsewhere in the business and enable them to take appropriate actions.
Map the workflow’s systems and actions
- Inventory sources of truth. For every field the agent may use, identify the authoritative system, owner, freshness expectation, and retention rule.
- Separate read and write capabilities. An agent may need to read an order record but require a stronger permission to cancel it. Expose only the operations the workflow needs.
- Prefer documented interfaces. Use stable APIs or other supported integration methods instead of screen scraping or undocumented shortcuts. Record versioning and error behavior.
- Design failure paths. If a service is unavailable, data conflicts, or an authorization check fails, the agent should explain the blockage and route the case to a named queue rather than improvising.
- Test cross-system consistency. Verify that an action in one system produces the expected state in dependent systems and that duplicate requests are handled safely.
“Open” does not mean unrestricted access. It means that systems expose understandable, governable ways to exchange information and invoke actions. Identity, least-privilege permissions, rate limits, audit records, and contract testing remain necessary.
3. Supply context that matches the decision
An agent’s context is the information it can use for a particular decision, not merely the text in a chat window. The useful context may be structured records, policy documents, recent events, sensor feeds, or a human conversation. Clark described a broad range: “We see context being audio and video, even in some cases, things like lidar and other kinds of information, financial streaming data and other kinds of things to help agents make decisions daily.”
Match each context source to a purpose
| Context type | Possible role in a workflow | Control questions |
|---|---|---|
| Audio or transcripts | Capture a customer’s stated problem, consent, or prior troubleshooting steps. | Was the recording lawfully collected? Can the agent distinguish the speaker and detect transcription uncertainty? |
| Video or images | Inspect visible damage, equipment status, or an interaction that cannot be represented in text alone. | What resolution and retention period are required? Which details are reliable enough to trigger an action? |
| Lidar and other sensor data | Describe physical conditions for maintenance, logistics, or safety decisions. | What is the sensor’s coverage, calibration, and failure mode? What happens when readings conflict? |
| Financial streaming data | Provide current signals for monitoring, reconciliation, or exception handling. | How is latency handled? Which events are authoritative, and how are corrections or duplicates processed? |
| Business records and policies | Ground a recommendation in account status, entitlements, contracts, and approved procedures. | Are the records current, access-controlled, and traceable to their source version? |
More context is not automatically better. Unrelated, stale, or contradictory information can make a decision less dependable. Define a context contract for each task: required fields, acceptable age, source priority, handling of missing values, and the signal that should trigger escalation.
4. Build transparency and trust into the workflow
Trust is an operating property that users can inspect. A person deciding whether to approve an action needs to know what the agent attempted, which records it used, which rules applied, and where uncertainty remains. A polished explanation that cannot be traced to evidence is not sufficient.
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- Log the request, identity, permissions, and relevant context references.
- Record each tool or system call, its parameters, response, and error state.
- Preserve the policy or instruction version used at the time.
- Show the proposed action separately from the executed action.
- Keep the human decision, reason for override, and timestamp.
These records support incident investigation, quality review, customer disputes, and controlled improvement. Access to logs should itself be restricted because they may contain sensitive information.
Use human review where consequences warrant it
Clark’s recommendation is explicit: “As a business, understand your outcomes really clearly and understand your risk tolerance,” and, after identifying that risk, “putting a human in the loop for anything that’s above that.” Human review should be a real control, not a button that automatically approves every suggestion.
Give reviewers the facts needed to make a decision, a meaningful amount of time, and the ability to modify or reject the proposal. Route ambiguous cases to specialists, and make sure the agent cannot bypass the approval queue through a different integration. For urgent operations, define an emergency procedure with named authority and a retrospective review.
A practical implementation sequence
The following sequence translates the guidance into a manageable enterprise project. It is a planning method, not a claim that the source article measured these steps.
- Select one bounded workflow. Choose a process with a clear owner, repeatable inputs, and an outcome that matters. Avoid starting with an enterprise-wide “copilot” objective.
- Document the current state. Capture systems, manual work, decisions, exceptions, data classifications, and existing approval points.
- Set the autonomy boundary. Label every proposed agent action as draft, recommend, execute-with-checks, or execute-only-after-approval. Have risk owners sign off.
- Prepare the context layer. Connect only the required sources, define freshness and precedence rules, and test missing or conflicting data.
- Implement governed tools. Give the agent narrowly scoped operations with authentication, authorization, validation, idempotency, and clear error responses.
- Add transparency and review. Build logs, evidence views, approval queues, escalation routes, and rollback procedures before opening the workflow to production users.
- Run a controlled pilot. Use representative cases, including edge cases and deliberate failures. Compare the agent’s proposals and actions with the documented baseline and have process owners review samples.
- Expand only after evidence. Increase volume, data types, or autonomy one dimension at a time. Reassess risk whenever the workflow, connected system, policy, or jurisdiction changes.
How to evaluate an enterprise AI approach
The article does not compare named vendors or products. If you are comparing internal designs or implementation proposals, use the following axes derived from Clark’s themes rather than a vendor scorecard.
Best Value
| Axis | Questions for a design review | Weak signal |
|---|---|---|
| Interoperability | Can the design read authoritative data and perform approved actions across the systems involved? | A demo works only with copied data or a single application. |
| Context quality | Does each decision receive the necessary, current information with source traceability? | The design assumes that a longer prompt fixes missing or stale records. |
| Transparency | Can a reviewer reconstruct what the agent saw, decided, and did? | There is an answer but no evidence trail or versioned policy. |
| Governance and trust | Are identity, permissions, privacy, retention, and incident response defined? | Controls are deferred until after deployment. |
| Outcome clarity | Is success tied to a business result and an existing process baseline? | The only target is model fluency or user excitement. |
| Risk and human approval | Are high-consequence actions blocked until an accountable person approves them? | “Human in the loop” means occasional sampling after the action. |
Common failure modes and practical corrections
The agent can answer but cannot resolve
This usually indicates an integration boundary, not a language problem. Identify the missing system operation, expose a supported and least-privilege interface, and provide a clear escalation when that operation is unavailable.
The agent receives too much untrusted context
More documents or streams can increase ambiguity and privacy exposure. Reduce the context to task-relevant sources, define precedence, and test stale, contradictory, and missing inputs.
Reviewers approve without understanding
If the approval screen hides evidence, the human becomes a rubber stamp. Present source records, proposed changes, policy checks, uncertainty, and a reason to reject or edit the action.
The project has no defensible definition of success
Return to the outcome statement. Select operating measures appropriate to the process—such as completion quality, escalation volume, handling time, rework, or error severity—and review them with the process owner. These are measurement choices for an implementation team, not results reported in Clark’s interview.
What the published evidence does—and does not—show
The September 7, 2026 AI Business article reports Clark’s remarks and does not provide quantified business outcomes, comparative tests, product specifications, or an independent evaluation of an enterprise agent. It therefore supports a set of design principles, not a prediction that following them will produce a particular return or accuracy level. Organizations should validate each workflow with their own data, controls, users, and risk owners.
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