Making sense of mixed feedback
Separates positive and negative views within the same comment, so praise for managers does not obscure concerns about workload. Assesses sentiment only for themes employees actually mention.
— Organisation & Workforce
Analyses large volumes of free-text engagement survey comments to produce a theme-by-theme sentiment read by department, identify what genuinely drives the overall mood, and set out an action plan — without anyone hand-coding a single comment.
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
— USE CASES
Separates positive and negative views within the same comment, so praise for managers does not obscure concerns about workload. Assesses sentiment only for themes employees actually mention.
Shows how sentiment varies across departments and workplace themes. Gives advisers a focused view of where concerns concentrate and where experiences differ across the organisation.
Identifies themes associated with overall sentiment and uses the findings to shape an evidence-based action plan. Supports a more specific discussion of priorities than headline engagement scores alone.
Provides a focused selection of the forty most negative comments with their key phrases. Gives reviewers direct employee evidence to consider alongside aggregate findings.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Each row of your export is processed on the same basis, so no role is skipped however long the table is.
Movement across the organisation in scope is visualised from the computed data, so the trend is legible at a glance.
Findings on the organisation in scope are written up as a document that reads like professional output, with each claim tied back to a role.
Detail rows are summarised into the grouped view of the organisation in scope without losing the underlying roles.
Open-text engagement surveys capture concerns that headline scores often miss, but interpreting hundreds of comments consistently takes time and can flatten mixed views. In practice it shows up as making sense of mixed feedback: separates positive and negative views within the same comment, so praise for managers does not obscure concerns about workload. Assesses sentiment only for themes employees actually mention. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 weeks of senior time whenever it comes round.
Here the same job runs as a Skill. Your material goes in; employee engagement and culture diagnostic report comes out, alongside theme-level sentiment findings covering leadership, managers, workload, recognition, career growth, pay and benefits, culture and wellbeing. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. Net effect: 2–3 weeks down to ~40 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Employee engagement & culture diagnostic exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
Redesigns workflows and roles around the practical use of AI, producing a board-ready plan with task-level recommendations, costed capacity gains and clear workforce implications.
Keeps a standing read on the risk universe in scope, working through risk registers, controls and policy documents on a regular cycle to show where each risk stands, what has moved since the last run and which items now need an owner's attention.
Matches available consultant capacity against the work coming down the pipeline each week, showing where people are about to sit idle, where demand will outstrip the skills you have and which pairings genuinely fit.
Runs a recurring skills survey across the workforce on a set cycle, building a living view of skills and utilisation by department that leadership can track month over month.
Ranks candidate locations against weighted workforce and business criteria, tests how cost assumptions affect the result, and produces a board-ready recommendation with a traceable evidence base.
Reviews a workforce metrics pack and states what the measures actually imply, rather than restating the numbers.