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✍️Xavier Vincent

Managing Your AI Projects Like a Portfolio: A Practical Guide for SME Leaders

You run an SME and every new AI or automation project feels like a leap of faith. Vendors promise spectacular results, your teams are cautious, and you’re unsure what will really pay off. The problem usually isn’t the technology itself, but the lack of a simple way to choose, evaluate, and adjust projects over time.

In this article, we’ll see how to manage your AI and automation initiatives like an investment portfolio, even if you’re not technical. You’ll get a concrete framework to track your projects and decide whether to scale, adjust, or stop them early. The goal: focus your resources on what truly creates value for your business, instead of getting lost in experiments that never deliver.

1. Why you should treat AI as a portfolio, not a single big bet

Many SMEs still approach AI and automation as a one-off “big project” that must succeed at all costs. Typical consequences:

  • All the energy is put into a single use case
  • Huge pressure for it to “work” immediately
  • Difficulty admitting that it needs to be changed or stopped

In reality, AI is much closer to a portfolio of small investments:

  • Some projects will perform very well
  • Some will deliver modest but useful results
  • A few will need to be stopped

The real objective is not to make every single project perfect, but to ensure that your overall portfolio creates more value than it consumes in time and budget.

1.1 Three layers of value to focus on

As a non-technical leader, you don’t need complex technical metrics. Instead, track three simple layers of value for each project:

  1. Operational value

    • Hours saved per week
    • Fewer errors or rework
    • Shorter processing times

  2. Business value

    • Revenue impact (more sales, better retention, upsell)
    • Cash impact (faster payments, fewer unpaid invoices)
    • Cost reduction (external services, duplicate tools, manual work)

  3. Human value

    • Lower mental load for your teams
    • Fewer daily irritants
    • Better experience for customers and employees

Every AI or automation project should clearly aim for at least one of these three. If not, it’s very likely to become a gadget.

2. The minimum dashboard you need to steer your AI portfolio

You don’t need a specialized tool to manage this. A simple spreadsheet (Excel, Google Sheets) is more than enough to start.

2.1 The essential columns

For each current or upcoming project, create a line with these columns:

  • Project name (e.g. "Automated client reminders")
  • Business area (finance, HR, customer service, operations, etc.)
  • Business owner (internal sponsor, not a vendor)
  • Primary objective (time, cash, quality, customer experience…)
  • Main business metric (e.g. average payment delay, time to handle a ticket, number of requests per month)
  • Baseline (value of this metric before the project)
  • 3‑month target (realistic goal, not perfection)
  • Status (idea, quick test, pilot, scaled, paused, stopped)
  • Team perception (green / amber / red, with a short comment)
  • Next decision (scale, stabilize, adjust, stop)

This becomes your AI portfolio cockpit: one page that shows where you’re investing time and what’s actually coming back.

2.2 Visualizing the lifecycle of a project

To help everyone understand that AI projects evolve in stages, you can use a very simple lifecycle diagram:

Rendering diagram...

This reminds your teams that stopping a project is not a failure but a normal decision in a portfolio approach. What matters is deciding early, based on facts, instead of letting half-baked projects drain energy indefinitely.

3. How to decide whether to scale, adjust, or stop a project

For each project, set a regular decision review from the start (for example monthly or every two months). At each review, ask the same simple questions.

3.1 Five questions for every review

  1. Is the main business metric actually improving?

    • Yes, clearly: the project is creating measurable value
    • Yes, but only slightly: it may need adjustments
    • No: the project is questionable

  2. Are the teams genuinely saving time or stress?

    • Do they feel relieved, or have you just added complexity?

  3. Do customers or end users notice a benefit?

    • Less waiting, fewer errors, clearer communication?

  4. Is the system stable and reliable enough?

    • Few incidents? Simple ways to handle issues when they arise?

  5. Is the cost (time + money) still reasonable?

    • Are the gains clearly higher than the effort to fix, adapt and explain the tool?

Based on this, you can take a clear decision:

  • Scale: extend scope, add more users, or connect another process
  • Stabilize: stop adding complexity, document the process, ensure reliability
  • Adjust: tweak part of the process, simplify rules, or change a tool if needed
  • Stop: accept that this use case doesn’t create enough value and redirect your resources

3.2 Concrete examples of decisions

  • Automated client payment reminders

    • Result: average payment delay goes from 55 to 40 days
    • Team feedback: finance team feels relieved, less manual chasing
    • Decision: scale to all clients and formalize the process

  • Generic email summarization assistant

    • Result: limited time savings, some summaries are inaccurate
    • Team feedback: lack of trust, everyone re-reads everything
    • Decision: stop this use case and redirect efforts to a clearer process (for instance, summarizing only support tickets or meeting notes)

4. Running a 60‑minute monthly AI portfolio review

Once your dashboard exists, the crucial step is to turn it into a recurring ritual.

4.1 Who should be in the room?

  • You, as CEO / managing director / business unit leader
  • 2–4 business owners (finance, HR, operations, customer service, sales…)
  • Optionally, your main external partner (consultant, integrator) if relevant

The goal is not a technical meeting, but a business review: what do these projects bring to the company, in concrete terms?

4.2 A simple 60‑minute agenda

  1. 5 minutes – Remind the rules

    • We base our discussion on the portfolio dashboard
    • We focus on business indicators, not features

  2. 40 minutes – Review active projects

    • For each project:
      • 2 minutes: recap goal and main metric
      • 3 minutes: current status (numbers + team feedback)
      • 1 minute: decision (scale, stabilize, adjust, stop)

  3. 10 minutes – Look at the portfolio as a whole

    • Do we have too many active projects?
    • Should we focus our efforts on 1–2 priority initiatives?

  4. 5 minutes – Next steps

    • Who does what before the next review?
    • Which new ideas stay in the “Idea” column without being launched yet?

By keeping this meeting short and focused, you maintain control without adding another heavy management layer.

5. Practical section: a simple framework to manage your AI portfolio

5.1 Pre‑launch checklist for any new project

Before you green‑light a new AI or automation project, check that:

  • [ ] The business problem is clear and shared
  • [ ] A business owner is named (internal, not a vendor)
  • [ ] One main business metric is defined and measurable
  • [ ] A baseline value is written down
  • [ ] A realistic 3‑month target is set
  • [ ] The scope is limited (one process, one team, reasonable volume)
  • [ ] A decision review date is booked (with identified participants)

If any box is empty, delay the launch and clarify. You’ll avoid fuzzy projects that drift for months.

5.2 Sample structure for your AI portfolio spreadsheet

In a simple Excel or Google Sheets file, create a AI_Portfolio tab with these columns:

  1. Project_name
  2. Business_area
  3. Business_owner
  4. Primary_objective
  5. Main_metric
  6. Baseline_value
  7. Target_3_months
  8. Status
  9. Team_perception
  10. Next_decision

Start with no more than 2 or 3 projects. The objective is not to list everything, but to build a manageable, high‑impact portfolio that truly moves the needle.

Conclusion

By managing your AI and automation initiatives as a guided portfolio instead of isolated experiments, you:

  • Give your teams a clear, simple decision framework
  • Focus energy on projects that truly create value
  • Reduce the risk of endless technical projects with no visible outcome
  • Make AI more concrete, measurable and reassuring for the whole organization

You don’t need to be technical to do this. You mainly need to ask the right business questions, track a few indicators, and accept to stop what doesn’t work.

If you want support in your digital transformation, Lyten Agency helps you identify and automate your key processes. Contact us for a free initial audit.

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