Industry overview
What is shaping information technology
IT services and technology companies sell to CIOs, IT heads and business leaders through long, multi-stakeholder buying cycles where trust and proof matter.
- Buying committees are getting larger
- Thought leadership and proof points shorten sales cycles
- Win/loss evidence improves bids
Client background
A multi-site healthcare group, working in United Kingdom. Client identity is withheld to protect confidentiality.
- Organisation
- A multi-site healthcare group
- Sector
- Information technology
- Team engaged
- IT & Development
- Services
- AI & automation solutions, API development & integration, IT consulting
Client challenge
The internal IT team was overwhelmed with password resets, access requests and printer issues, delaying more important work. Staff waited hours for help with simple problems, often during busy clinical shifts.
Research objectives
- Deliver on the brief: automating an internal IT helpdesk with an AI assistant.
- Design around the real needs of staff.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
The work focused on 4,500 staff.
- staff4,500
Audiences we typically reach in information technology
CIOs and IT headsBusiness decision-makersProcurement teamsExisting clientsPartners and resellers
Geographic coverage
Coverage: United Kingdom. Market: United Kingdom. 12 sites covered.
- United Kingdom
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Ticket analysis
- 3AI assistant with ticketing integration
- 4Insight
We analysed a year of helpdesk tickets to find the most common, repeatable requests, then built an assistant on the staff intranet and Microsoft Teams that answered questions from approved guides, handled password resets securely and created tickets with full context for anything it could not solve. Security and data protection reviews were completed before launch. We also set clear limits on what the assistant could do, routed anything clinical or sensitive straight to people and tested every answer with IT staff before launch.
AI & automation solutionsAPI development & integrationIT consulting
Sampling strategy
- staff
- 4,500
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Routine requests moved to self-service, ticket queues shortened noticeably and the IT team spent more time on projects.
- Staff rated the assistant highly because it worked at any hour, including night shifts.
Starting with real ticket data ensured automation solved the problems staff actually had.
Recommendations
- Build on the core insight: starting with real ticket data ensured automation solved the problems staff actually had.
- Track adoption and support tickets, and plan the next release from real usage.
Business impact
- The assistant’s logs also revealed recurring problems, such as a printer model that failed frequently, which the IT team fixed at the source.
Impact is described qualitatively.
Deliverables & next steps
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: why do we win or lose deals?
Client identity is withheld to protect confidentiality.