Project leadership for managers who never asked to be project managers.
AI and IT projects don’t have to be overwhelming.
We help business leaders manage with clarity — using simple steps, strong stakeholder alignment, and just enough AI to get results without losing the human side.
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Many managers are asked to lead tech projects on top of their operational responsibilities.
Running a project is different than operations and so needs to be managed differently.
This is no different for an AI project. If you don’t take a second to understand your business purpose, the agreed end-state, some outcomes, and time frames, the project can go on and on, or worse produce something with limited value.
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Managers are accountable for outcomes, timelines, and decisions — often without formal project management training or additional time.
Simple steps include: understanding the purpose, what “done” looks like, some high-level checkpoints, and timeframes, ensuring there is focus and business value that the business can actually use.
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This site is for managers who want a simpler, calmer way to get tech projects moving and keep them on track.
Managing tech projects can be simpler if the essential simple steps are followed: (1) What’s the purpose?, (2) What does “done” look like? (3) Roughly how are we planning to get there?, (4) in what timeframe?
Why tech projects feel harder than they should.
Most tech projects don’t struggle because managers aren’t capable.
They struggle because important fundamentals are skipped in the rush to get started - often without anyone realizing it.
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No shared definition of success
Projects often begin with good intentions but unclear goals. Key supporters quietly have different ideas of “done,” causing rework, delays, and stalled decisions. Worse, those tough talks about what “done” means happen at the end instead of the start.
This isn’t a lack of effort — it happens when the team hasn’t agreed on what “done” looks like.
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People have different ideas about how to proceed. There are various tools, steps, and people involved. Disagreements lead to gossip that can derail even the best efforts. Managers don’t need every detail, but you should agree up front on the main steps.
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Is this a 2-month, 6-month, or 1-year project? When will the major milestones be finished? Unclear timelines hurt confidence and support. Think about the talks that happen when the manager or sponsor isn’t there to guide them—are those conversations consistent, and are delivery expectations clear?
Project Examples
Project Clarity Pieces
Purpose
Done
Outcomes
Timeframes
Example 1:
AI-Powered Customer Support Triage
Use AI to auto-classify incoming customer service tickets for faster response and routing.
1. What is the Purpose?
Free up support staff from repetitive triage tasks
Reduce customer wait times
Improve service consistency for common requests
2. What does Done look like?
The AI categorizes 80% of incoming tickets accurately
Simple dashboard shows trends by issue type
Tickets are auto-assigned to the right teams
3. What outcomes do we expect at each major stage?
Month 1: Train model on past tickets
Month 2–3: Pilot in limited regions or product lines
Month 4–5: Expand to more teams, refine edge cases
4. What are the rough timeframes?
Total: ~4 months from kickoff to rollout
Maintenance: Quarterly reviews of accuracy and routing rules
Example 2:
AI-Assisted Sales
Forecasting
Use AI to analyze past sales data and improve forecast accuracy for planning.
1. What is the Purpose?
Identify patterns in sales cycles we can’t easily spot manually
Improve confidence in inventory or staffing decisions
Create alignment between finance, sales, and ops
2. What does Done look like?
Teams receive updated forecasts monthly, driven by AI
Leadership sees 20–30% increase in forecast accuracy
Clear visual reports explain why the model predicts what it does
3. What outcomes do we expect at each major stage?
Month 1: Align on data sources and key business inputs
Month 2–3: Train/test forecast model on past data
Month 4–5: Use forecasts in planning cycles, gather feedback
4. What are the rough timeframes?
Total: ~5 months
Integration into regular planning cycles by Month 6
Example 3:
AI for Employee Onboarding Content Creation
Use generative AI to draft onboarding docs and job aids faster, with human oversight.
1. What is the Purpose?
Save time for HR and managers creating repetitive content
Create more consistent onboarding experiences
Make materials easier to update regularly
2. What does Done look like?
10–15 onboarding assets drafted via AI, then finalized by staff
Managers can request job aids with simple prompts
Process includes human review before anything goes live
3. What outcomes do we expect at each major stage?
Month 1: Define document types and tone-of-voice templates
Month 2–3: Pilot asset generation with HR + team leads
Month 4: Launch system for 1–2 departments
4. What are the rough timeframes?
Total: ~3–4 months
Review cycle monthly to refine prompt templates and feedback loops
What actually helps
busy managers
run projects.
As a manager, your tech projects don’t need heavy frameworks or complex tools.
They need a small set of high-impact simple habits that create clarity, ownership, and momentum — without adding overhead.
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Define success first.
Projects move faster when everyone knows the problem and what success looks like in plain terms.
No heavy docs - it can be done in a 1-pager - just a clear reference about the purpose, what “done” looks like, the high-level steps and timeframes.
At that point all the key players know and agree about where things are going.
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Once there is clarity amongst the leadership about the purpose, end-state, high-level steps and schedule, then involve the team in a way that engages them and builds collaboration.
Deliverables: Clarify deliverables that are possible at the key steps.
Activities: Decide on what tasks and actions are to be taken to complete the deliverables.
Review: Get opinions and input from each team members. Listen and ensure there is understanding regarding their perspective.
Revise: Each time the team meets whether weekly or multiple times per week, review and revise the deliverables and activities with the team to make sure the high-level steps can be achieved.
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Small cadence beats big plans.
A lightweight weekly check-in creates more momentum than detailed plans that never get revisited.
Consistency matters more than complexity when projects compete with day-to-day operations.
Each week the manager needs to review the key pieces to the project plan, the status of where things are at, then review and revise with the team, and then communicate to the team and key stakeholders.
How managers are supported.
Support is available at different levels, depending on how much structure or guidance a
manager wants.
No jargon. No overcommitment.
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Practical, self-serve tools.
Short guides and templates designed to help managers create clarity quickly — without learning a full project methodology.
Ideal if you prefer working independently.
Links to available material: (Feb 20, 2026)
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Learn a simple execution rhythm.
Structured learning focused on weekly habits that reduce effort, risk, and stress while improving outcomes.
Designed specifically for managers balancing projects alongside operational work.
Jan-Apr 2026: No open slots at this time.
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Calm guidance while you execute.
For managers who want experienced judgment alongside them while running a real project.
This is about clarity and support — not oversight or micromanagement.
Meeting times are flexible based on each manager’s need and urgency.
Jan-Apr 2026: No open slots at this time.
How I Work.
I work with managers the same way I’ve worked with executive teams and technical teams for decades.
Calmly. Practically.
Focused on what actually moves things forward.
The goal isn’t perfection.
It’s progress that feels manageable
There’s no one-size-fits-all framework — just a small set of proven steps applied thoughtfully to your context.
Also, extensive methodology experience allows adapting the organization’s comprehensive methodology into simpler but compliant form for a manager.
Choose a simple
place to start.
You don’t need to do everything at once.
Start where it feels most useful.
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Field notes & short guides.
Practical observations and lightweight resources you can apply immediately.
Links coming on or before Feb 20, 2026
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Courses & routines.
Guided learning focused on reducing project overhead and improving weekly execution.
No open slots at this time until April 2026.
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Advisory & coaching.
Ongoing or time-bound support for managers running real projects.
No open slots at this time until May 2026.
Why
This Approach Works.
I’ve spent decades leading and recovering complex tech projects across business and technology environments.
But more importantly, I’ve worked closely with managers and directors balancing operational responsibility with project accountability.
I’ve held roles as a senior director in Fortune 500 companies in both the US and Canada, where I also managed tech projects on the side; so I’ve been there, done that, got the t-shirt, and so know how to deliver within those constraints.
This work is about making that reality easier — not more complicated.
You don’t need more theory.
You need clarity and a way forward.
If you’re managing a tech project, you’re not alone — and you don’t have to overcomplicate things to succeed.
And you can learn to use the new AI tools in a practical way that saves you time, energy, and resources, and provides creative input that you can manage and tailor to serve what you need to get things done.
Organizations where value has been delivered
Clients value the ability to deliver transformative results while building strong, lasting relationships. From executive leadership to project teams, work is recognized for exceptional project management, strategic thinking, and a commitment to exceeding expectations.