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    • The watermelon test
    • AGIG - Case study
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Hapsicle
  • Home
  • The watermelon test
  • AGIG - Case study
  • About
  • Contact Us

The problem

Programs rarely fail without warning. The warning just isn't in the RAG. 

The reality

88%

only 25%
88%

 of business transformations fall short of their original ambition. 


45%

only 25%
88%

 average budget overrun on large IT projects, which deliver 56% less value than planned. 


only 25%

only 25%
only 25%

 of projects are delivered successfully: on time, on budget and meeting their original goal. 

Do you have a watermelon?

The Watermelon Test: A diagnostic at a depth that wasn't humanly possible before AI

Until now, a program review meant a small team, a sample of documents and a handful of interviews. AI changes that. Our AI workforce reads everything and hears everyone. An experienced human partner, who has run large programs, decides what it means and owns the results.


No one has time to read a year of reports, plans and logs side by side.  Our AI workforce does, and it looks at more than 700 factors to assess your project's health.

AI reads everything

Every status report, plan version, RAID log and set of minutes since day one. Not a sample.

AI engages and hears everyone

Everyone who matters, from the sponsor to the front line, is interviewed, written up and compared.

A human owns the answer

Someone who has run large programs decides what the evidence means, and what to fix first.

Four weeks, start to finish

Week 1: Set up

Weeks 2 and 3: Collect and analyse
Weeks 2 and 3: Collect and analyse

Agree the scope and the questions that matter. Get access to documents, delivery tools and the people we need.

Weeks 2 and 3: Collect and analyse

Weeks 2 and 3: Collect and analyse
Weeks 2 and 3: Collect and analyse

We read every status report, steering pack, plan version, RAID log and set of minutes. Our AI interviews the people doing the work. Everything is tested against over 700 indicators of program success and twelve green-to-red patterns.

Week 4: Results review

Weeks 2 and 3: Collect and analyse
Week 4: Results review

We walk you through what we found and suggested next steps.

The team behind it

One AI Director. Eight agents. One Human.

This is what an AI-native workforce looks like in practice. Work a traditional review team would do one step at a time runs in parallel and around the clock. The AI Director plans the work, launches the agents in parallel, sequences what depends on what, remembers every document and conversation, and keeps a full audit trail. It reports to a human partner every day, who owns every finding. 

What you get - A clear view of what is / isn't really green

Evidence-based RAG

Evidence-based RAG
Evidence-based RAG

Beside your reported RAG, period by period.

Pattern review

Evidence-based RAG
Evidence-based RAG

Assessment of twelve common patterns that turn projects from green to red. 

A truth score

Evidence-based RAG
A scored health view

From 0 to 100: how far your reporting can be relied on.

A scored health view

A scored health view
A scored health view

Across 13 dimensions, with the weakest links named.

Stress tests

A scored health view
Stress tests

What happens if you run one, three or six months late.

Priorities

A scored health view
Stress tests

Quick wins and potential next steps

Research-based, AI-run, owned by human partners

The test draws on a library of over 700 indicators of program success, built from 35 years of experience in large project delivery. Hapsicle's AI workforce does the reading, research and cross-checking. A human partner who has run large programs owns every judgement.

Answers the questions that matter

  • CFO: "What will this actually cost, and what will it return?"
  • COO: "Are we really ready: people, customers, systems?"
  • Board or audit: "Can I believe what I am being told?"
  • PMO lead: "Do I have all the facts I need?"
  • Customers: "What's going to change for me?"
  • Regulator: "Have you implemented our new regulation?"

Contact Us

Is your program green all the way through?

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