Sample analysis · Illustrative · Synthetic data · Fictional house · No real client data
ML

Maison Lumière · Manager's Future Demand Sample

0 managers forecast the competencies their unit will need, when, and why they will be hard to find · read against the map of who holds them today · use the filters to change the view

EVA.ai · Workforce Decision Intelligence
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managers in view 0 of 0

How to read this example. Filters explore a prepared synthetic snapshot. Recommendations, targets and timings are proposals to review, not measured outcomes or included delivery commitments. Self and manager ratings are assessment perspectives; access and visibility are agreed for each engagement.

What was asked

Every manager of a unit had one conversation with EVA, about ten minutes, in their own language. The competency lists came from the house's own map, so managers forecast against what they actually have, not against a generic list.

01
Demand forecast

For the 20 technical and 10 cross-functional competencies in their unit's map: will demand strongly decrease, slightly decrease, stay the same, slightly increase or strongly increase over the next two to three years, and why.

02
Urgency and scarcity

When the rising competencies are needed (3 months to 3 years or more), which will be hard to find, and the reasons: pay band, specialised experience, emerging technology, location, market shortage, competition, no internal path, moving target.

03
Decline and development

Why the declining competencies decline (automation, outsourcing, shift of focus, lower demand, simpler processes, cost) and what the unit needs to build the rising ones (training, coaching, budget, hiring, time, mobility, tools, partnerships, career paths).

04
Anticipated vacancies

The roles the unit expects to open: how many, at what level, what contract type, when, and why (retirement, growth, workload, attrition, projects, restructuring, gaps, promotions, funding, seasonality). Each one becomes a forecast job request.

Key results at a glance

·
competencies rising (net demand above +0.5)
·
competencies declining (net demand below −0.5)
·
of managers need the rising competencies within 6 months
·
positions forecast to open in the next 24 months
·
top reason competencies are hard to find
·
summed Advanced/Expert holdings in declining competencies; people may overlap

Three decisions this analysis puts on the table

Computed from the managers in view and the house's map. Build, buy or redeploy: the forecast only earns its keep when it says which.

CHART 1 · THE HERO

Where demand is going, competency by competency

The question: "These are the top 20 technical competencies in your unit's map. Looking two to three years ahead, how do you forecast the demand for each in your unit?" Five answers from strongly decrease to strongly increase, then "Could you explain why?"
Strongly decreaseSlightly decreaseStay the sameSlightly increaseStrongly increaseRight-hand figure: net demand index from −2 to +2
SECTION 2

Rising and hard to find: the build-or-buy map

Each competency placed by how fast demand is rising (across) and how many managers say it will be hard to find (up). Bubble size is how many people hold it today at Advanced or Expert, from the map.

Buy now: rising, scarce, thin benchBuild: rising, scarce outside, bench existsGrow: rising, availableHold or release
SECTION 3

When, and why it is hard

When the rising competencies are needed

Why they are hard to find, share of managers citing each

SECTION 4

What is declining, why, and who holds it today

Why demand is falling, share of managers citing each

People holding the declining competencies today (Advanced or Expert), from the map

Each bar counts holders of one competency across the fictional house. People may appear in several bars. A distinct-person pool requires person-level matching, interest, availability and release checks.
SECTION 5

What managers say they need to build the rising competencies

SECTION 6

Anticipated vacancies: the forecast job requests

The question: "Which positions do you expect to open in your unit in the next two years? Say how many, at what level, what contract type, and when." Each answer becomes a forecast job request in the workspace, attached to the unit, so recruiting starts from a number, not a surprise.

Positions by quarter needed

Why the positions open, share of managers citing each

Forecast job requests, aggregated

SECTION 7

What managers added in their own words

EVA reads every open answer and groups them into themes. The bar is the share of managers whose comments touch each theme.

SECTION 8

One unit, one page

What each manager receives back: their own forecast against the house's, their urgency, their job requests, and what the map says they can do without hiring.

NEXT 90 DAYS

From the forecast to the decisions

Weeks 1 to 4

Buy

  • Open the "buy now" requisitions today; those competencies will not get cheaper.
  • Fix the pay band problem for data and AI roles before the search starts, or the search fails.
  • Language hires for the Lunar New Year and summer peaks approved as a block, not one by one.
Weeks 5 to 8

Build

  • One cohort per "build" competency, taught by the house's own experts from the map.
  • Every rising competency gets a clear learning path; managers named it as the second thing they need.
  • Budget follows the forecast: training money moves to where demand is rising, not where it was last year.
Weeks 9 to 12

Redeploy

  • Everyone in the declining pool gets a conversation about where the map says they can go, before the role goes.
  • Internal mobility opens for the forecast job requests first; external search only for what is left.
  • Re-run the forecast conversation in six months; ten minutes per manager, and the plan stays true.

Explore what this analysis could tell you about your team.

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