Director of Business Value from Enterprise AI (R5463)
Full Time Employee 280000 - 420000 USD per-year-salaryJob Overview
Job Description:
This Director owns the full demand-to-value pipeline: sensing where the business needs help, separating signal from noise, identifying the highest-leverage opportunities, deciding when to buy versus build, driving adoption, and proving measurable ROI.
This role is the AI team's primary operator with non-engineering functions across Shield, and is expected to build and lead the team that executes this pipeline at scale. Success is defined by turning scattered interest into a focused portfolio of business-backed use cases, converting pilots into real adoption, and demonstrating hard value in ways that earn trust, budget, and follow-on demand.
While this role is business-facing rather than a technical builder, it requires genuine technical grounding in machine learning and generative AI. Sound buy/build calls, credible use-case shaping, and defensible ROI cases all depend on the leader understanding what these systems can and can't do, how they're evaluated, and where the real technical risk sits — not just what vendors claim.
Job Description:
This Director owns the full demand-to-value pipeline: sensing where the business needs help, separating signal from noise, identifying the highest-leverage opportunities, deciding when to buy versus build, driving adoption, and proving measurable ROI.
This role is the AI team's primary operator with non-engineering functions across Shield, and is expected to build and lead the team that executes this pipeline at scale. Success is defined by turning scattered interest into a focused portfolio of business-backed use cases, converting pilots into real adoption, and demonstrating hard value in ways that earn trust, budget, and follow-on demand.
While this role is business-facing rather than a technical builder, it requires genuine technical grounding in machine learning and generative AI. Sound buy/build calls, credible use-case shaping, and defensible ROI cases all depend on the leader understanding what these systems can and can't do, how they're evaluated, and where the real technical risk sits — not just what vendors claim.
What you'll do:
- Apply working technical knowledge of ML and generative AI — including model capabilities, limitations, training/tuning basics, and evaluation approaches — to pressure-test vendor claims, size technical risk in buy/build calls, and shape use cases that are technically realistic.
- Distinguish generative AI from traditional ML in business conversations, since the two carry different technical risk profiles, build timelines, skill requirements, and cost structures, and translate that distinction into sharper prioritization and roadmap decisions.
- Partner with engineering and technical evaluators as a credible counterpart during vendor evaluation and market scans, able to interrogate technical claims rather than relay them.
- Incorporate model performance, drift, and technical limitations into ROI frameworks and success metrics, so measured value reflects real system behavior, not just adoption numbers.
- Own the single demand pipeline across Workplace AI: intake, shaping, prioritization, buy/build recommendation, rollout, adoption, and value measurement.
- Partner directly with business leaders, including senior executives, to surface needs, pressure-test requests, and distinguish real opportunities from low-value noise.
- Translate ambiguous business problems into clear requirements, decision-ready use cases, and a prioritized portfolio of AI opportunities.
- Identify where AI can create the most leverage, whether through productivity, cycle-time reduction, quality improvement, risk reduction, or better decision support.
- Lead build-versus-buy decisions, including market scans, vendor evaluation, recommendation development, and ownership of purchased-solution rollout.
- Establish baselines, define success metrics, and build the ROI cases that show whether a solution is working after deployment.
- Own adoption as seriously as selection: partner with the business on training, behavior change, communications, and local champion models so tools actually stick.
- Serve as the senior business-facing counterpart to the VP, integrating business pull and technical push into one clear prioritization and operating rhythm.
- Build, lead, and develop a team responsible for demand shaping, triage, and business-facing execution, establishing standards and processes that scale as the function matures.
- Prepare decision memos, frame tradeoffs, and resolve most prioritization questions before they reach the VP.
- Act as the trusted AI advisor to non-engineering leaders, communicating in the language of outcomes, constraints, and business value.
- Own the strategic roadmap and budget for the demand-to-value function, aligning it to enterprise priorities over multi-quarter horizons.
What success looks like:
- Business units see Workplace AI as a trusted partner that helps clarify problems, not just respond to tickets.
- Buy/build decisions are faster, sharper, and better grounded in business value, technical reality, and market conditions.
- Deployed tools have defined baselines, measurable adoption, and defensible ROI.
- Training and change management are treated as core parts of delivery, not afterthoughts.
- Reliably handles shaping, triage, and business-facing execution functions, with a team scaled to meet demand.
Required qualifications:
- 15+ years in business transformation, product, strategy, operations, enterprise technology, or related leadership roles.
- 5+ years leading cross-functional programs or transformations that required influencing senior stakeholders and driving behavior change, including direct people-management experience.
- Substantive technical fluency in modern AI and generative AI — including how models are trained, tuned, and evaluated, and where their practical limitations sit — sufficient to assess vendor claims critically, shape technically realistic use cases, and hold a credible technical conversation with engineering counterparts. This is a genuine requirement, not surface familiarity, even though the role is not a hands-on technical builder.
- Demonstrated ability to distinguish generative AI from traditional ML in terms of technical risk, skill requirements, cost, and timeline, and to apply that distinction to prioritization and staffing decisions.
- Proven track record translating business needs into deployed technology solutions with measurable business impact.
- Strong judgment on build versus buy decisions, including vendor evaluation, external market scanning, and procurement partnership.
- Experience defining baselines, KPIs, and ROI frameworks for new tools, workflows, or transformation efforts.
- Exceptional executive communication skills, especially with non-technical leaders and sponsors.
- Proven track record building, leading, and developing high-performing teams, including hiring and mentoring talent.
- Strong operator instincts: comfortable with ambiguity, able to create structure where little exists, and willing to own outcomes rather than hand off work.
Preferred qualifications:
- Experience in internal AI, automation, digital transformation, or enterprise productivity programs.
- Background in consulting, high-growth operating roles, or business-side product leadership.
- Experience leading vendor-backed implementations as well as internally built solution rollouts.
- Familiarity with change management, enablement, and adoption measurement in enterprise environments.
- Change-management certification or equivalent demonstrated practice.