02 / BUSINESS DISCOVERY AND AI SOLUTION DESIGN
Solutions Consultant
Turn a vague ambition into a problem worth solving.
STEP INSIDE THE WORK
AN ILLUSTRATIVE PROJECTThe best solution starts before the first slide.
A retailer asks for an AI chatbot. Their actual problem is rising support costs. What needs to be understood?
YOUR MOVE / 01
Follow the customer’s reality.
Map the enquiries, user needs and current workflow. Ask why people contact support and establish a baseline. The brief is a starting point for discovery.
WHAT YOU MAKE VISIBLE
User and stakeholder needs
A mapped workflow
Evidence of the bottleneck
Think from first principles.
Make your reasoning visible.
01 / YOUR MISSION
Ask the question that changes what gets built.
A customer says, “We need AI.” You want to understand what is actually slowing their business down. This role sits where customer problems meet technical possibility. At devx, you will learn to unpack messy workflows, connect evidence to recommendations, and help a team choose work worth doing. If you enjoy technology and can make a complicated idea clear to another person, this is a place to put both strengths to work.
Discovery, solution framing and evidence for a decision before delivery begins. You help customers and engineers agree what to build, why it matters and how a pilot will be judged.
We reimagine customer interactions, business operations and enterprise architecture around AI. You bring fresh questions. Experienced practitioners bring context and judgment. The work connects both to a real customer outcome.
02 / WHAT YOU WILL WORK ON
Make your curiosity
useful.
Understand the business behind the brief
Research a customer's context and join discovery conversations with experienced colleagues. Map users, decisions, handoffs and bottlenecks; separate the stated request from the problem underneath.
Turn ambiguity into a useful brief
Write clear problem statements, user stories and acceptance criteria. Record assumptions, dependencies and open questions so design and engineering can act on the same understanding.
Explore feasible solutions
Compare AI-enabled and conventional approaches with engineers. Consider data access, integrations, human involvement, reliability and effort. Learn enough architecture to explain how the parts fit together.
Make the value case credible
Use spreadsheets or simple analysis to establish a baseline and estimate benefits. Show assumptions and sensitivity instead of presenting a forecast as a guaranteed saving.
Build conviction through evidence
Build demo scenarios, prototype flows and sections of proposals with the team. Connect each demonstration to a customer question and agreed success criteria. Document validated capabilities, unresolved assumptions and delivery commitments for a clear handover to the Outcome Manager and engineers.
What good work looks like
- A problem brief that a customer recognises and an engineer can act on.
- A feasible recommendation with explicit assumptions, trade-offs and success measures.
- A demo and delivery handover that accurately describe what is in scope.
Use approved AI tools for customer research, synthesis and rapid prototypes. Check original sources, distinguish customer evidence from hypotheses, and make sure every recommendation survives questions from both business and engineering colleagues.
03 / YOUR STARTING POINT
Bring a foundation.
Build the range.
Coursework, personal projects, research and student initiatives all count. Previous full-time experience is not required.
- Structured reasoning: break an unclear problem into questions, assumptions and evidence you can test.
- Technical curiosity and basic understanding of software, APIs, data and what AI can and cannot reliably do.
- Clear writing, attentive listening and the ability to explain an idea to technical and business audiences.
- Comfort with spreadsheets and basic quantitative analysis, demonstrated through a project, case study or student initiative.
Useful exposure
SQL, a small prototype, process mapping, case competitions or customer research can help. Prior enterprise sales experience, an MBA and mastery of cloud architecture are not prerequisites.
04 / HOW YOUR OWNERSHIP GROWS
Learn in the work.
Grow through the feedback.
Start by owning a research question, analysis or part of a solution brief. Grow towards leading a bounded discovery workstream and presenting recommendations with senior review. Architecture decisions and commercial commitments stay with the appropriate accountable colleagues as you develop your own judgment.
Explore
Understand the problem and ask useful questions.
Build
Make a defined contribution and learn through review.
Own
Take on broader responsibility as your readiness grows.
05 / THE DEVX CULTURE
The principles
show up in the work.
Understand what the customer is trying to change. Ask the extra question, surface the real obstacle and connect your work to their goals.
A feature shipped, but the workflow is still slow. Stay with the problem and find out why.
Client obsession. Understand what the customer is trying to change. Ask the extra question, surface the real obstacle and connect your work to their goals.
AI-native everything. Use AI to improve how you research, build, analyse and document. Understand its limits, verify the output and handle customer information responsibly.
Hire and train exceptional talent. We bring young talent and experienced practitioners together. Bring ambition, seek direct feedback and put new understanding into practice.
Document to scale. Write down the decision, its context and what you learned. Make your work understandable enough for someone else to continue, question or improve it.
Only ever be honest. Flag risk while there is time to act. Separate what you know from what you assume. Give clear feedback and make uncertainty visible.
06 / START A CONVERSATION
Show us the way
you think.
Share a résumé and one example of a problem you structured. A project note, case presentation or student initiative is welcome. Show the evidence, your recommendation and its trade-offs.
Join through an internship with the opportunity to convert to a full-time role. The hiring team will share internship duration, work arrangements, campus eligibility and conversion criteria during recruitment.
An illustrative project
An online retailer wants a chatbot to reduce support costs. You could analyse sample enquiries, map why customers contact support, and compare self-service, process fixes and AI assistance. Propose a small pilot with agreed measures such as resolution quality and handling time. A strong recommendation explains the trade-offs and what evidence would change your mind.