AI Experience Strategist_Q227

What matters most

• Set the AI experience direction

  The role defines the CX vision, north star, experience principles and roadmaps, using journey mapping and service design to identify high-impact opportunities and future-state experiences.

• Make value and governance measurable

  The posting calls for data-backed business cases and experience governance, with measures spanning ROI, adoption, trust, task completion, containment, fallback performance, revenue impact and efficiency.

• Decide where AI belongs in the journey

  A core responsibility is mapping when AI should assist, augment, automate or escalate tasks, then shaping personas, interaction scenarios, dialogue flows, prompting patterns and human handoffs.

• Validate AI behavior with evidence

  The role defines LLM evaluation datasets, benchmarks and quality criteria, and uses simulations, hallucination audits, behavior assessments and usability tests to assess accuracy, relevance, safety and trust.

• Lead across disciplines and client settings

  The strategist partners with data and engineering, aligns experience strategy with business, technology, risk and financial goals, and contributes to client discovery, workshops, pitches and design discussions.

Likely challenges to think through

• Balance containment with reliable handoffs

  Preparation hypothesis: increasing AI task completion or containment may conflict with safety and user trust when the system is uncertain. Prepare an example decision framework for when to automate, request clarification, fall back or escalate, and how each path would be measured.

• Connect experience measures to business value

  Preparation hypothesis: a better interaction may not immediately show up as ROI or operational efficiency. Prepare to explain how you would connect journey-level measures, such as task completion and trust, to a business case while stating assumptions and measurement limits.

• Keep ambitious concepts technically grounded

  Preparation hypothesis: agentic and generative experience ideas may be difficult to reconcile with function-calling behavior, evaluation results and risk goals. Prepare to describe how you would test a proposed workflow with engineering and data partners, then narrow or revise it based on evidence.

Topics to prepare

• CX strategy and service design

  Revise how a CX vision, north star and experience principles guide a future-state service. Suggested exercise: compare two possible principles for a hypothetical AI-enabled journey and explain how each would change the roadmap. Relevant because the role defines strategy and leads service design; service design is also listed as a preferred skill.

• AI task allocation

  Revise the distinctions between assisting, augmenting, automating and escalating a user task. Suggested exercise: classify steps in a hypothetical customer journey and justify where human control remains appropriate. The posting explicitly calls for AI capability mapping across conversational AI, copilots and agentic workflows.

• Responsible experience governance

  Revise how experience principles, risk considerations, human handoffs and accessibility can shape AI adoption. Suggested exercise: draft decision criteria for approving or revising a hypothetical AI interaction. This relates to the stated responsibility for responsible, scalable experience governance.

• Business value and success measures

  Revise how ROI, adoption, trust, task completion, containment, fallback performance, revenue impact and efficiency measure different outcomes. Suggested exercise: define a small set of measures for one hypothetical use case and explain possible trade-offs. The role explicitly requires data-backed business cases and success metrics.

• Conversational experience models

  Revise information architecture, multi-turn dialogue, generative interface patterns and the limits of prompting. Suggested exercise: explain how a change in user intent would affect dialogue state and escalation. These concepts underpin the interaction design responsibilities in the posting.

• LLM quality evaluation

  Revise the distinction between accuracy, relevance, safety and user trust, including what hallucination audits and usability tests reveal. Suggested exercise: propose evaluation criteria for a small set of hypothetical AI responses. The posting explicitly calls for datasets, benchmarks and AI behavior assessments.

Skills to revise

• Map a future-state journey

  Suggested exercise: draw an as-is and future-state journey for a hypothetical customer task, marking user needs, service touchpoints, AI opportunities and handoffs. This practices the posting’s journey-mapping, service-design and future-state responsibilities.

• Build a use-case business case

  Suggested exercise: create a one-page case with stated assumptions, expected user outcomes, costs, risks and a measurement plan; distinguish estimated ROI from observed results. This reflects the required data-backed cases and alignment with business, technology, risk and financial goals.

• Draft a multi-turn interaction

  Suggested exercise: write a dialogue flow and short prompt-pattern library for a hypothetical assistant, covering clarification, uncertainty, fallback and human escalation. This practices the specified dialogue, prompting, containment and handoff work.

• Specify an agent action

  Suggested exercise: sketch a hypothetical function schema with inputs, expected outputs, validation and an escalation point, then list questions to resolve with engineering. This prepares for the stated collaboration on function schemas and function-calling workflows; no particular platform is specified.

• Run an AI-output assessment

  Suggested exercise: assemble a small hypothetical test set, rate responses against accuracy, relevance, safety and trust criteria, and record hallucinations and usability issues. This mirrors the posting’s evaluation, simulation, audit and testing responsibilities.

• Facilitate client discovery

  Suggested exercise: prepare five discovery questions and a brief workshop agenda that turn client goals and user pain points into prioritized AI experience opportunities. This is relevant to the stated client discovery, innovation workshop and proposal contributions.

• Organize role-aligned evidence

  Prepare a career timeline and project index that show relevant experience, education and service-design work. The posting specifies 12–20 years of experience, a Bachelor of Engineering or Design, and service design as a preferred skill.

• Build a CX strategy walkthrough

  Select one relevant project and prepare a concise walkthrough of its journey map, CX vision, north star, experience principles and future-state roadmap. Distinguish your decisions from the wider team’s work.

• Draft a sample business case

  Prepare a clearly hypothetical AI experience opportunity with assumptions, priorities and measures for ROI, adoption, trust, task completion, fallback performance and operational efficiency. The role calls for data-backed business cases; do not present invented results as actual outcomes.

• Practice an AI interaction design exercise

  Choose a sample user journey and sketch where AI should assist, augment, automate or escalate. Produce a multi-turn dialogue flow showing prompts, a possible function call, fallback behavior and human handoff.

• Prepare an evaluation and governance plan

  Create a sample test set and criteria for accuracy, relevance, safety, trust and usability. Include how you would examine hallucinations, accessibility and failures, and who would review decisions; frame this as a proposed approach, not an Infosys process.

• Rehearse a client-facing presentation

  Prepare a short explanation of how you would move from client discovery to an AI experience roadmap, including trade-offs across user, business, technology and risk goals. The role includes proposals, workshops and client discussions.

• Write questions for the employer

  Ask which AI-enabled journeys are the immediate priority, how success and responsible adoption are measured, and how design collaborates with engineering and data teams. Use the answers to clarify the role rather than assuming a particular project or process.