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08/28/2026

Decision delays create schedule pressure long before a milestone appears late.

While an approval remains open, different teams respond in different ways. Some pause. Some make assumptions so they can keep moving. Others prepare multiple versions of the same work.

The longer the decision remains unresolved, the greater the chance of rework, conflicting directions, and less time for downstream activities.

Every open program decision should identify:

• The decision that must be made
• The person with the authority to make it
• The information still needed
• The date an answer is required
• The escalation path if that date is missed

It should also identify which tasks, teams, or deliverables are waiting for the answer. That detail separates an inconvenient delay from a decision that is putting the schedule at risk.

At the next program review, sort open decisions by downstream impact and required date. Address the ones affecting multiple teams or near-term milestones first.

08/27/2026

An AI guardrail can be perfectly clear and still fail in practice.

“Use only approved tools” sounds straightforward until an employee needs a capability the approved tool does not provide, a contractor lacks access to the company account, or a new integration appears after the tool has been reviewed.

“Do not enter sensitive information” raises its own questions. Employees need to know what the organization considers sensitive, how the rule applies to real documents, and what to do if information is shared by mistake.

The gap is not necessarily the policy. It is the ex*****on structure around it.

Turning an AI guardrail into an operating control requires:

• An owner responsible for implementation
• Specific steps built into the relevant workflows
• Communication and training based on actual work
• A way to confirm that the control is being followed
• An escalation path for exceptions and incidents

This is program management work. It connects policy to people, processes, systems, and oversight.

A useful implementation test is to walk one guardrail through three situations:

1. Normal use: Can an employee follow it without stopping to interpret the policy?
2. An exception: Is it clear who can approve a different approach?
3. An incident: Does the employee know how to report it and what happens next?

If any step depends on the assumption that “people will know what to do,” the guardrail is not yet operational.

08/21/2026

A paid AI subscription can still leave a company with very little control over its data.

Price and product tier are easy shortcuts when evaluating security. Business versions may offer different administrative settings, data commitments, retention options, or contractual terms. The receipt itself, however, does not answer the operational questions that determine the organization’s actual exposure.

A useful review should trace the real data path:

• Who owns the account?
• What information enters the tool?
• What can the provider retain, and what deletion options exist?
• Which company systems can the tool read or change?
• What administrative controls and activity records are available?
• Who reviews consequential output?

Two employees can use the same AI product and create very different levels of risk. One may work through a company-managed account with restricted integrations. Another may connect a personal account to email and cloud storage.

The product name is the same. The organization’s visibility and control are not.

Security depends on the account, configuration, permissions, integrations, and workflow surrounding the tool. Procurement matters, but it cannot make those decisions for the business.

Before approving or renewing an AI tool, which of these questions can your organization answer confidently?

AI policies can be technically correct and still be too vague to guide an employee through a real decision.Can this info...
08/20/2026

AI policies can be technically correct and still be too vague to guide an employee through a real decision.

Can this information enter the tool? Is a personal account acceptable? What can a connected application access? Who reviews the result?

Small and midsized businesses do not need an enterprise-scale governance program to begin answering those questions. They need practical guardrails that match the way people actually work.

This carousel outlines six places to start, from discovering current AI use and setting data boundaries to securing accounts, requiring human review, and preparing for incidents.

Save it, share it with your team, and use it to start a more specific conversation about AI use inside your organization.

Link in comments to learn more and read the full ClearPath article.

08/19/2026

In case you missed it, the latest issue of our monthly LinkedIn newsletter, ClearPath, is now available.

This month’s article examines shadow AI, including how everyday AI tools can create gaps in data visibility and what leaders can do to establish practical guardrails.

“Your Team Is Already Using AI. Do You Know Where Your Data Is Going?”

Click the link in the comments to read it.

AI may already be inside your organization—even if you never approved it. Sometimes it arrives as a personal login used ...
08/13/2026

AI may already be inside your organization—even if you never approved it.

Sometimes it arrives as a personal login used to summarize a document. Sometimes it is a meeting assistant, a browser extension, or an application connected to company files.

The work may be useful, and in many cases it is. The challenge is that the organization may still have no clear view of what information entered the tool, what permissions were granted, or who is accountable for the result.

That is the focus of the new ClearPath issue, now available:

“Your Team Is Already Using AI. Do You Know Where Your Data Is Going?”

Inside, we look at how shadow AI develops inside ordinary workflows and how leaders can create practical visibility without blaming employees or blocking useful work.

Read the full issue through the link below.

08/12/2026

AI work often gets evaluated at the prompt level.

Was the prompt clear?
Was the output useful?
Did the tool save time?

Those questions matter, but they do not go far enough for business, federal, or operational environments where the output affects decisions, documentation, customer communication, compliance, or program delivery.

The better question is: how does the work move around the AI?

Who owns the source information?
Who reviews the result?
Who uses the output next?
Who owns the final decision?
Where do exceptions go?

A strong prompt can still create risk if the surrounding workflow is unclear.

Before scaling an AI-supported task, map the handoff around it. That is where many hidden ownership, review, and accountability gaps show up.

Your company may not have an AI strategy yet, but your employees may already have AI workflows.A browser extension summa...
08/12/2026

Your company may not have an AI strategy yet, but your employees may already have AI workflows.

A browser extension summarizes a webpage. A meeting assistant captures notes. A personal account helps clean up a proposal. Each shortcut solves a real work problem.

Together, they raise a question leaders cannot answer from a policy document alone:

Where is company information going?

The next ClearPath issue looks at shadow AI, the hidden gap between everyday AI use and organizational visibility. It explains why the answer is not to block useful work, but to understand the tools, accounts, data, and connections already involved.

“Your Team Is Already Using AI. Do You Know Where Your Data Is Going?” publishes Thursday, August 13.

Subscribe to ClearPath through the link below.

08/07/2026

Most initiatives have a plan to start.

Fewer have a clear plan to pause, shift, or unwind the work if conditions change.
That gap can become expensive once the work is already underway.

In modernization, AI adoption, cybersecurity, vendor management, and federal-facing delivery, the question is not only how the initiative begins. Leaders also need to understand what happens if the tool no longer fits, the requirement changes, the pilot does not perform as expected, or a dependency becomes a risk.

That does not mean planning for failure. It means planning for continuity.

Before a major decision moves into ex*****on, it helps to ask:

• What would cause us to pause this effort?
• Who has the authority to change direction?
• What data, documentation, or access would need to be preserved?
• What work would continue manually if the tool or vendor changed?
• What would make a transition harder six months from now?
• What decision point should be built into the plan?

Exit planning is often treated as a procurement or vendor issue. It is really an operating issue.

A team that understands how to change direction can make better decisions at the beginning. It can also protect the organization from being locked into a path simply because the work became too difficult to unwind.

08/06/2026

One common reason work stalls is that teams understand who approved the decision, but not who is accountable for the outcome.

Approval and ownership are often treated like the same thing. They are not.

An approver gives permission to move forward.

An owner keeps the work moving after that decision is made.

That distinction matters in program management, cybersecurity, modernization, and AI adoption because approval alone does not answer the operational questions that come next.

• Who keeps the documentation current?
• Who manages exceptions?
• Who decides when a risk has changed?
• Who coordinates across teams or vendors?
• Who knows when leadership needs to reenter the conversation?

This is especially important when organizations approve new tools, platforms, or AI use cases. The decision to proceed may be clear, but the work can still lose momentum if no one owns data quality, review standards, user guidance, security requirements, or sustainment.

A useful way to clarify ownership is to ask:

• Who owns the result?
• Who owns the process?
• Who owns the risk?
• Who owns the communication?
• Who owns the decision if conditions change?

If those answers are unclear, the project may depend too much on informal coordination.

Good ex*****on needs more than a yes. It needs someone accountable for what happens after the yes.

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