Innovation Lab Specialist
Enterprise Innovation Lab Specialist
Role purpose
The Enterprise Innovation Lab is a two-person special projects team inside the Enterprise line of business (LoB) AI & Automation programme. The remit: find high-value AI and automation opportunities across the Enterprise line of business, explore new tools and technology from outside the organisation's existing technology stack to apply to use cases, build working prototypes at pace, validate them quickly, and hand solutions to the delivery workstreams to productionise.
The Specialist role will sit in an independent mini-team, the Innovation Lab, with the Specialist and the Manager running under the direction of the AI & Automation Director - fast, self-sufficient and built to make the wider programme go faster. You will manage the pipeline of pilots, contribute to pace and outcomes, and build alongside the Manager.
The successful candidate will contribute to building stronger AI and automation capability over time, independently driving new solutions and technologies, and helping to ensure that AI investment converts into measurable enterprise value through clear outcomes, disciplined delivery, and early-stage building.
What this role offers
Manage a pipeline of prototypes - mandate to deliver and iterate at pace.
Frontier tooling - hands-on with the latest AI platforms, with responsibility to explore and test.
High autonomy - an independent two-person team, light-touch direction and outcome-focused.
Context
Accelerate Enterprise is bringing functional AI and Automation to the Enterprise line of business - a strategic priority for the organisation.
This is an Enterprise LoB role sitting in the commercial business, not engineering. You will spend Real Time with Commercial and Operations teams and their partners (Credit, Compliance, Marketing, and more). Being able to explain what you built and why it matters commercially is part of the job.
The role is not strictly aligned to defined functions - free to find and test opportunities anywhere in the Enterprise LoB, ahead of formal delivery.
Tool and tech scouting is a core part of the remit - the Lab tests cutting-edge tools from outside the organisation's existing technology stack against real use cases and informs what the organisation adopts next.
Responsibilities
Opportunity identification and commercial framing
Build and manage a pipeline of AI and automation opportunities sourced directly from Enterprise Commercial and Operations teams and partner functions.
Frame every pilot commercially before build starts: quantify the value (revenue, cost, risk, speed), define success criteria and clear stop conditions.
Pressure-test ideas with frontline teams before committing build effort.
Build and iterate
Build hands-on alongside the Manager across the existing technology stack.
Run and manage structured pilot tests with real users in Commercial and Operations teams.
Set up measurement for every pilot: baseline, pilot result, delta. Results reported accurately and transparently back to leadership.
Iterate quickly on feedback and flag early when something is not working.
Stakeholder and partner management
Manage relationships with Enterprise Commercial and Operations leads.
Bring partner functions in early - so pilots do not stall at governance gates later.
Tool and tech scouting
Manage the Lab's "tool and tech radar" - track the market for new AI and automation tools worth testing (automation platforms, agent frameworks, etc).
Apply new tools from outside the organisation's existing technology stack to real use cases. Run and manage structured side-by-side evaluations against the incumbent stack: capability, cost, security posture, integration effort.
Share findings to the organisation - tools tested, findings, and recommendations.
Skills required
AI Delivery & Experimentation
Strong awareness and understanding of where AI tools and technologies can be applied to business challenges.
Demonstrated experience building AI-enabled solutions, prototypes, agents or automations.
Strong understanding of strengths, limitations and risks of LLM-based solutions.
Curious by default - picks up new AI tools quickly, and can apply to a commercial environment
Fast and resourceful, ability to work in an ambiguous environment.
Independent and able to manage workload with light-touch direction, as one half of a self-sufficient mini-team.
Stakeholder Engagement
Strong commercial acumen - can size an opportunity, build a simple benefits case and defend the numbers to senior stakeholders.
Comfortable working directly with Commercial and Operations teams - this is a business-facing role, not Back Office engineering.
Able to gather requirements, demonstrate solutions and incorporate feedback rapidly.
Confident presenting findings and recommendations to senior stakeholders.
Problem Solving & Solution Design
Consulting toolkit: structured problem solving, story telling and crisp executive communication.
Ability to translate ambiguous business challenges into practical AI and automation opportunities.
Strong analytical and structured problem-solving skills.
Comfortable operating with limited direction in a fast-moving environment.
Sound judgement when balancing value, risk, complexity and speed.
Experience assessing technology options against business outcomes, costs and implementation effort.
Transparent reporting - concludes underperforming pilots early with clear rationale. Benefits are always evidence-based.
Nice to haves
Payments, fintech or financial services background.
Background in a consulting firm (strategy or digital) and/or a technology company - used to pace, ambiguity and a high bar on output.
Experience across the Salesforce ecosystem, AI agent frameworks, AI Coding Tools, Data Platforms and AWS ecosystem.
ML fundamentals - understands features in, predictions out; can support model-adjacent pilots such as churn.
Experience training or supporting non-technical users on new tools.
Outcomes
A pipeline of 5-10 qualified opportunities, sourced from Enterprise LoB teams and prioritised by value.
Oversaw the prototyping of 5 pilots; with at least 2 progressing to full delivery, each with a documented benefits case.
A pipeline of 5-10 technology and tool recommendations.
Role purpose
The Enterprise Innovation Lab is a two-person special projects team inside the Enterprise line of business (LoB) AI & Automation programme. The remit: find high-value AI and automation opportunities across the Enterprise line of business, explore new tools and technology from outside the organisation's existing technology stack to apply to use cases, build working prototypes at pace, validate them quickly, and hand solutions to the delivery workstreams to productionise.
The Specialist role will sit in an independent mini-team, the Innovation Lab, with the Specialist and the Manager running under the direction of the AI & Automation Director - fast, self-sufficient and built to make the wider programme go faster. You will manage the pipeline of pilots, contribute to pace and outcomes, and build alongside the Manager.
The successful candidate will contribute to building stronger AI and automation capability over time, independently driving new solutions and technologies, and helping to ensure that AI investment converts into measurable enterprise value through clear outcomes, disciplined delivery, and early-stage building.
What this role offers
Manage a pipeline of prototypes - mandate to deliver and iterate at pace.
Frontier tooling - hands-on with the latest AI platforms, with responsibility to explore and test.
High autonomy - an independent two-person team, light-touch direction and outcome-focused.
Context
Accelerate Enterprise is bringing functional AI and Automation to the Enterprise line of business - a strategic priority for the organisation.
This is an Enterprise LoB role sitting in the commercial business, not engineering. You will spend Real Time with Commercial and Operations teams and their partners (Credit, Compliance, Marketing, and more). Being able to explain what you built and why it matters commercially is part of the job.
The role is not strictly aligned to defined functions - free to find and test opportunities anywhere in the Enterprise LoB, ahead of formal delivery.
Tool and tech scouting is a core part of the remit - the Lab tests cutting-edge tools from outside the organisation's existing technology stack against real use cases and informs what the organisation adopts next.
Responsibilities
Opportunity identification and commercial framing
Build and manage a pipeline of AI and automation opportunities sourced directly from Enterprise Commercial and Operations teams and partner functions.
Frame every pilot commercially before build starts: quantify the value (revenue, cost, risk, speed), define success criteria and clear stop conditions.
Pressure-test ideas with frontline teams before committing build effort.
Build and iterate
Build hands-on alongside the Manager across the existing technology stack.
Run and manage structured pilot tests with real users in Commercial and Operations teams.
Set up measurement for every pilot: baseline, pilot result, delta. Results reported accurately and transparently back to leadership.
Iterate quickly on feedback and flag early when something is not working.
Stakeholder and partner management
Manage relationships with Enterprise Commercial and Operations leads.
Bring partner functions in early - so pilots do not stall at governance gates later.
Tool and tech scouting
Manage the Lab's "tool and tech radar" - track the market for new AI and automation tools worth testing (automation platforms, agent frameworks, etc).
Apply new tools from outside the organisation's existing technology stack to real use cases. Run and manage structured side-by-side evaluations against the incumbent stack: capability, cost, security posture, integration effort.
Share findings to the organisation - tools tested, findings, and recommendations.
Skills required
AI Delivery & Experimentation
Strong awareness and understanding of where AI tools and technologies can be applied to business challenges.
Demonstrated experience building AI-enabled solutions, prototypes, agents or automations.
Strong understanding of strengths, limitations and risks of LLM-based solutions.
Curious by default - picks up new AI tools quickly, and can apply to a commercial environment
Fast and resourceful, ability to work in an ambiguous environment.
Independent and able to manage workload with light-touch direction, as one half of a self-sufficient mini-team.
Stakeholder Engagement
Strong commercial acumen - can size an opportunity, build a simple benefits case and defend the numbers to senior stakeholders.
Comfortable working directly with Commercial and Operations teams - this is a business-facing role, not Back Office engineering.
Able to gather requirements, demonstrate solutions and incorporate feedback rapidly.
Confident presenting findings and recommendations to senior stakeholders.
Problem Solving & Solution Design
Consulting toolkit: structured problem solving, story telling and crisp executive communication.
Ability to translate ambiguous business challenges into practical AI and automation opportunities.
Strong analytical and structured problem-solving skills.
Comfortable operating with limited direction in a fast-moving environment.
Sound judgement when balancing value, risk, complexity and speed.
Experience assessing technology options against business outcomes, costs and implementation effort.
Transparent reporting - concludes underperforming pilots early with clear rationale. Benefits are always evidence-based.
Nice to haves
Payments, fintech or financial services background.
Background in a consulting firm (strategy or digital) and/or a technology company - used to pace, ambiguity and a high bar on output.
Experience across the Salesforce ecosystem, AI agent frameworks, AI Coding Tools, Data Platforms and AWS ecosystem.
ML fundamentals - understands features in, predictions out; can support model-adjacent pilots such as churn.
Experience training or supporting non-technical users on new tools.
Outcomes
A pipeline of 5-10 qualified opportunities, sourced from Enterprise LoB teams and prioritised by value.
Oversaw the prototyping of 5 pilots; with at least 2 progressing to full delivery, each with a documented benefits case.
A pipeline of 5-10 technology and tool recommendations.