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PPM Consulting & Performance

AI-Augmented Strategy & Execution

AI-Augmented Strategy & Execution: AI prepares the work, people decide

Your teams already use AI, each in their own way, with no common rules and no link to your data. The value lies elsewhere: in the moments where the decision chain spends most of its time on preparation and consolidation. Strategic reviews, portfolio forums, PI Planning, updates to the operating model. We identify those moments, connect AI to Jira, Clarity and Confluence there, and measure what it brings.

Strategy-to-Execution

  1. Strategy

    measurable priorities

  2. Funding

    funding aligned to priorities

  3. Portfolio Governance

    select, prioritise, stop

  4. Execution

    priorities put into action

  5. Teams

    work tied to a priority

  6. Tools & Data

    data owned at source

Execution results feed the next decision cycle.

The principle

AI prepares, consolidates, analyses and flags. Decisions, prioritisation, funding and commitments stay with the people accountable for them. AI never takes an investment or portfolio decision in their place. This division of roles is written down before each use goes live, and it does not change with the tool chosen.

AI does not replace any link in the Strategy-to-Execution chain. It shortens the preparation around each one.

Where AI augments the chain

Strategy

  • Strategy decomposition. Proposals for breaking strategic goals down into themes, objectives and indicators, which leadership keeps, corrects or rejects.
  • Strategic alignment checks. Matching the portfolio against the strategic themes: what contributes, what consumes capacity without contributing, what is missing.
  • Preparing strategic reviews. For each review, a summary of the options, their costs and their dependencies, with sources cited.

Portfolio governance

  • Portfolio synthesis. A view built from Clarity and Jira: progress, variances, rising risks. The portfolio manager reviews and completes it instead of building it.
  • Governance preparation. For each Portfolio Sync or strategic review: Epic status, alerts, the decisions expected. Forums are there to decide, not to catch up.
  • Lean Business Case quality. Help drafting hypotheses, and a completeness check before a case goes to a forum.
  • Initiatives out of line. Spotting Epics with no link to a strategic theme, or whose benefit hypothesis no longer holds.

These uses serve the forums and decision rules designed in Lean Portfolio Management. They do not replace them.

Execution and teams

  • PI Planning preparation. Consolidating candidate features, checking announced capacity against load, detecting undeclared or one-sided dependencies, and a summary for Business Owners.
  • Dependency analysis. Identifying dependencies between teams and between trains from Jira history.
  • Backlog preparation. Flagging stories with no testable acceptance criteria, or that describe a solution instead of a need.

A PI Planning is largely won before the event. See how the operating model organises it.

The operating model itself

  • Process updates. When a governance rule changes, proposed updates to every page it affects.
  • Confluence knowledge management. An assistant answers questions about the model (who decides what, in which forum, under which funding rule) and cites the source page.

The limits we set

  • Accountable people decide. Trade-offs, prioritisation and commitments stay with the people who answer for them.
  • Review before decision. Every output is reviewed by the person who will use it before it reaches a forum.
  • Every answer cites its source. A figure or a summary that cannot be traced back to the ticket or the page it came from is of no use.
  • Source data first. An analysis run on a poorly kept Jira produces wrong conclusions, faster. We fix the data before automating anything.
  • Confidentiality. Your data stays within your perimeter. The tools are chosen with your IT and security teams: AI features built into your Atlassian tools, an enterprise assistant already in place, or a model hosted in your own environment.
  • We measure. Each use has a success criterion defined before it goes live. What does not pay its way stops.

How we work

  • Leadership workshop. What AI actually does today in portfolio governance, demonstrations on real cases, first uses in your context, and the rules to set.
  • Diagnostic. The costly moments in the decision chain, the state of your Jira, Clarity and Confluence data, a prioritised backlog of uses, and the rules of use.
  • Pilot. Two or three uses running on a real cycle, one PI or one governance cycle, and measured at the end.
  • Scaling up. Validated uses become part of the operating model: rules by role, a library of assistants in Confluence, follow-up.

To connect assistants to reference sources, we can use standard connectors such as the Model Context Protocol (MCP), depending on the architecture and security requirements. Automating data flows and reporting, which removes re-entry, is covered in Tools & Data.

Why us

A useful AI use in portfolio governance needs three skills at once: knowing the decision model, knowing the tools where the data lives, and knowing how to connect one to the other. We design Lean Portfolio Management and operating models, we configure Jira and Confluence and Clarity, and we know what those tools really contain.

FAQ

Frequently asked questions

Do we need to buy a new tool?

Not necessarily. Many uses can be built with what you already have: the AI features of your Atlassian tools, or the enterprise assistant already deployed in your organisation. The diagnostic will tell you.

Our Jira data is in poor shape. Can we start?

Yes, but not with AI. We start by fixing what produces the data: workflows, definition of done, transition rules. That is often the first gain.

Can our data leave the organisation?

That is your decision, taken with your IT and security teams, and it is settled during the diagnostic. There are workable options either way.

What happens when the AI gets it wrong?

It does happen. That is why we never use it to produce a decision, only a preparation that can be checked against its source.

Where do we start?

With a single decision cycle and two or three uses. If the gain cannot be measured at the end, we stop.

Related expertise

Go further

Your next portfolio review, prepared differently

Let's talk about what could be prepared, consolidated and checked before then.

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