Case Studies | SaaS B2B

Turning AI ambition into a costed, prioritised roadmap

AI Enablement Discovery

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The Discovery phase set out to resolve four challenges before any solution was built.

From experimentation to an operational framework

The client needed to close the maturity gap between ad-hoc AI use and a coordinated, governed capability, with an AI steering committee and a shared view of opportunity across departments and service lines.

Data as the constraint

Generative AI only performs as well as the information it can reach, and the existing file-share architecture limited tools such as Copilot. Understanding the data

Governance and compliance by design

Operating in a data-driven, regulated context means AI cannot be bolted on without controls. The engagement had to address AI policy, explainability and responsible-use guardrails, mapped to standards such as ISO 42001 and relevant FCA guidance.

Proving ROI before scaling

Every candidate use case had to be judged on tangible value, revenue uplift, margin improvement or consultant-hours saved, and prioritised so that early pilots could repay their investment quickly rather than spread effort thinly.

Solution

Cloud Combinator structured the programme so each phase has clear objectives, activities and outputs. Discovery is the phase contracted here; Design, Pilot and Scale form the forward roadmap that Discovery is designed to justify and de-risk.

5 wks

Discovery phase to a costed, prioritised portfolio

20-30

High-value business processes mapped for use cases

Top 6

AI use cases prioritised by value, cost and complexity

By the numbers:

  • 5 wks - Discovery phase to a costed, prioritised portfolio
  • 20-30 - High-value business processes mapped for use cases
  • Top 6 - AI use cases prioritised by value, cost and complexity
Changes

Discovery is judged against a clear set of sign-off criteria rather than a finished system. It leaves the client with an AI steering committee and functional framework, a fully built-out use-case matrix drawn from in-depth workshops and process maps, and the ability to move into Design with several initiatives aligned across the business and its senior leadership. All targets below are set as forward-looking baselines to be measured through the ROI framework.

  • AI steering framework establishedAn AI Steerco and functional framework is stood up to identify and evaluate opportunities, with named leads across departments and service lines.
  • Use-case matrix builtA full portfolio of candidate use cases, spanning CRM data enrichment, search and match, candidate-pack creation and feedback management, is mapped and scored.
  • Priorities and blockers understoodUse cases are ranked by complexity, cost and ROI, with a pain-point and data-blockers inventory that quantifies the effort and data work ahead.
  • ROI framework definedSuccess metrics, baseline capture and a Power BI adoption-and-ROI dashboard template are put in place so value can be measured, not assumed.
  • HandoverAll Discovery artefacts are indexed in a SharePoint site, ready for the business to progress into the Design phase.

With a validated, prioritised portfolio in hand, the client has a clear path into Design and the two flagship pilots, a Copilot proposal studio and an automated report generator, followed by firm-wide scale-out. The build-operate-transfer model means Cloud Combinator stands the capability up, co-runs it with the client's team, and hands over a documented, self-sustaining operation without lock-in.

AWS Stack

Microsoft Purview

For data governance, sensitivity labelling and responsible-use guardrails.

Amazon Bedrock

And Amazon SageMaker as alternative-cloud patterns for specialised reasoning and anomaly-detection workloads.

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