Role transitions
Reimagine a role: from current role to target role.
Start with the role a person performs today and the role they want to perform next. Compare responsibilities, identify transferable work and gaps, then prepare for the target role through practice and readiness assessment. AI also changes responsibilities within an existing role; that is a separate preparation case.
Project Readiness supports this current-role-to-target-role preparation. Start with existing capability and what your existing enablement programme already covers. Define the target responsibilities and the evidence still needed, then select Simulations, practice environments and Skill Validation Assessments. Tools follow the role needs.
Selected role: forward-deployed-engineer
Selected workflow: Public Sector & Government workflow
Use this context to discuss the transition. It is not an analysis or readiness decision.
Preparation follows the responsibilities you need to take on
A programme can deepen AI-enabled work in an existing role or prepare someone for a new role. Start from their experience, choose relevant projects and review the work they can demonstrate. Agree entry level, tools, modules and assessment with the role owner.
Existing-role preparation
A developer working with Claude and Claude Code
Build on programming, API and terminal skills. Practise with domain projects: create reusable AI capabilities, connect tools and data, then use Claude Code to extend and review a codebase.
- Set up an approved environment and learn the selected model and tool.
- Build projects around real work, such as a customer-service application or a document-processing workflow.
- Check outputs, test exceptions and review generated changes and integration boundaries.
- Bring code, tests, review findings and a documented capstone together as assessment evidence.
Discuss Claude-based role preparation →New-role preparation
An enterprise engineer preparing for FDE responsibilities
Build on enterprise engineering experience and review the gaps. Technical practice and client judgment develop together as the engineer prepares to connect a business problem to a demonstrated solution.
- Bridge required programming skills, then build and evaluate retrieval and agent workflows.
- Practise client problem framing and choosing between AI, retrieval and agents.
- Defend architecture and integration choices, human controls, security and supportability.
- Verify a deployment in the practice environment and defend a capstone with its constraints and operating handoff.
Review FDE preparation and assessment →These examples describe practice and evidence to review. A completed project or assessment does not replace the role owner's readiness decision or approval for operating use.
Current role → target role → practice → readiness evidence
Illustrative transition paths, not automatic role matches, customer results or guarantees of qualification.
Software developer → AI Engineer
- Starting capability
- Software implementation, testing and debugging experience can be relevant starting capabilities.
- Target responsibilities and gaps
- Review the target role's model and tool integration, evaluation, data handling, output verification and operational handoff responsibilities.
- Practice and readiness evidence
- Practise a bounded AI implementation. Review source checks, tests, failures, implementation choices and the handoff against agreed target-role criteria.
Discuss transition to AI Engineer →Solutions or implementation engineer → Forward Deployed Engineer
- Starting capability
- Technical implementation and customer-facing delivery experience can be relevant starting capabilities.
- Target responsibilities and gaps
- Review stakeholder discovery, problem framing, domain constraints, bounded building and demonstration responsibilities for the FDE role.
- Practice and readiness evidence
- Practise discovery with a simulated stakeholder, agree a problem, build with the scenario's data and tools, then explain the result and its limits.
Discuss transition to Forward Deployed Engineer →A separate case: changed responsibilities within the same role
Illustrative transitions to discuss, not customer results or a prescribed redesign for your organization.
Software developer
- Current responsibilities
- Implement a requirement, test the change and hand it over for review.
- Changed responsibilities with AI
- Work with AI-generated code and tests, verify assumptions and dependencies, debug failures and explain the implementation to its reviewer.
- Practice and readiness evidence
- Review a plausible change that fails an important requirement. Demonstrate tests, corrections and a clear technical handoff.
Business analyst
- Current responsibilities
- Gather requirements, describe the process and agree changes with stakeholders.
- Changed responsibilities with AI
- Clarify evidence and constraints, review AI-suggested requirements, define human decision authority and check exceptions across the revised workflow.
- Practice and readiness evidence
- Challenge a proposed requirement based on incomplete evidence. Produce a reviewed problem statement, decision boundaries and handoff notes.
Preparing for a new role: Forward Deployed Engineer
A Forward Deployed Engineer is a new role to prepare for, and it can be a target for someone moving from an existing technical role. Its responsibilities connect stakeholder discovery, a bounded build and a demonstration. Review the person's starting capabilities and gaps before choosing preparation; an existing employee does not become an FDE just by receiving an AI tool.
Review FDE responsibilities and cohort preparation →From role transition to preparation
Define current and target roles
Bring the current role or job description and the target role. Review their workflows, responsibilities, decisions, systems and handoffs. Confirm the destination with the learner and accountable role owner.
Review transferable work and gaps
Compare existing capability with target responsibilities. Use Task Intelligence to examine tasks, what transfers, what must be learned and where people must verify, approve or handle exceptions.
Prepare for the transition
Agree a scenario problem with the role and workflow owners, then select practice for it: discovery conversations, realistic projects, output review, exception handling or technical handoffs. Build on existing enablement and approved tools; confirm access and licences for any additional practice environment.
Review readiness evidence
Agree observable assessment criteria and review the work produced, decisions, corrections and explanations. Human capability, agent behaviour and acceptance of the operating workflow require separate evidence and owners.
Choose practice once the target role needs are understood
Review Project Readiness products for the responsibilities you agreed: Simulations, hands-on practice and Skill Validation Assessments. The AI Bootcamp runs for 6–8 weeks with roles, scenarios, tools and assessment criteria agreed before delivery. Operating acceptance and business impact require the customer's review and evidence.
Public data and explicitly started previews can inform your discussion. Organization-specific preparation plans and saved work belong in the separate authenticated tenant experience at nuvepro.ai.
Discuss a role transition →