AI Strategy Manager Jobs in New York
AI Strategy Manager jobs in New York are among the most active in the country, concentrated in financial services, media, healthcare, and enterprise technology, with openings at every level from associate strategist through VP of AI strategy. Most hiring centers on New York City, with additional demand in White Plains and Albany among employers scaling AI governance and digital transformation programs. Firms like JPMorgan Chase, IBM, and Pfizer have deep, lasting presences in New York and consistently recruit ai strategy managers to lead model deployment, AI roadmap development, and cross-functional integration work. Find a role that fits below and apply directly.
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The AI TO is responsible for accelerating and scaling responsible AI adoption across Wolters Kluwer by helping functions identify high-value opportunities, reinvent workflows, coordinate enabling resources, govern risk, and deliver measurable business impact. Functions and business owners remain accountable for execution, adoption, and outcomes; the AI TO provides the rigor, expertise, visibility, and support required to accelerate progress.
The Associate Director role will work closely with functional leaders, business owners, Finance, Data, Technology, HR, and other stakeholders to determine where AI can materially improve business performance and to build the fact base required to make investment and scaling decisions.
The successful candidate will translate ambiguous questions such as “Could AI fundamentally improve this workflow?” into rigorous, evidence-based answers. This will require understanding how work is performed today, identifying the operational and financial drivers of performance, establishing credible baselines, defining the right KPIs, quantifying value at stake, pressure-testing assumptions, and measuring whether expected value is ultimately realized.
This is a hands-on strategy and analytics role. The ideal candidate combines the structured problem solving and business judgment of a strategy consultant with a strong quantitative orientation and a willingness to dig deeply into data, processes, assumptions, and economics.
Primary Accountabilities
Identify and diagnose high-value opportunities
Partner with functional leaders, process owners, and frontline subject-matter experts to understand how work is performed today and where AI-enabled workflow redesign could materially improve business outcomes.
Conduct business and process diagnostics to identify bottlenecks, sources of cost, delays, capacity constraints, quality issues, risk, or lost revenue.
Help distinguish incremental productivity opportunities from more transformational opportunities to redesign end-to-end workflows around human judgment, AI agents, data, and automation.
Assess the scale and materiality of opportunities and identify the key value drivers that determine whether an initiative merits investment.
Define KPIs and establish credible baselines
Translate broad transformation ambitions into a small number of meaningful business and operational KPIs.
Determine how relevant measures are calculated today, where the underlying data resides, who owns it, and what constitutes a credible baseline.
Gather, reconcile, and analyze information across multiple sources to establish current performance, including volumes, cycle times, throughput, productivity, quality, conversion, capacity, costs, customer outcomes, and other relevant measures.
Identify data gaps, limitations, and assumptions and develop pragmatic approaches for measuring performance where perfect data is not available.
Ensure initiatives have measurable success criteria before investment and implementation decisions are made.
Quantify value at stake
Build transparent, driver-based models that translate changes in operational performance into financial and strategic outcomes.
Quantify potential value from revenue growth, productivity, capacity creation, cost reduction, quality improvement, risk reduction, customer impact, or employee experience as appropriate.
Develop Year 1 and longer-term value estimates, expected operating costs, required investment, and net business impact.
Clearly distinguish between cash savings, capacity released, cost avoidance, revenue improvement, and other forms of value.
Document the critical assumptions behind each value case and identify the sensitivities that have the greatest effect on expected outcomes.
Develop and challenge business cases
Develop rigorous, directional business cases for priority AI opportunities, considering value, feasibility, investment, risk, readiness, adoption, and implementation complexity.
Pressure-test assumptions and challenge sponsors and business owners constructively where supporting evidence is weak.
Identify the critical conditions that must be true for an initiative to deliver its expected value.
Compare opportunities consistently to help leadership prioritize limited investment and execution capacity.
Support build / buy / partner analysis where relevant, incorporating expected economics, differentiation, operating costs, and dependencies.
Design value measurement and evaluate results
Define measurement approaches for pilots and scaled deployments, including baseline, target, leading indicators, operational KPIs, business outcomes, and measurement cadence.
Establish the analytical bridge between AI adoption, workflow change, operational performance, and financial impact.
Compare realized results with expected performance and diagnose the causes of variance.
Determine whether evidence supports scaling, modifying, pausing, or stopping an initiative.
Partner with Finance and business owners to ensure realized value is credible, traceable, and understood consistently.
Build the enterprise AI value view
Aggregate opportunity-level analyses into a transparent enterprise view of expected and realized AI value.
Identify the largest emerging value pools, material assumptions, risks, dependencies, and gaps across the AI portfolio.
Provide leadership with fact-based perspectives on where the enterprise is creating value, where performance is falling short, and where additional intervention or investment is required.
Help maintain clear accountability for business outcomes and KPI movement across priority initiatives.
Generate executive insight and recommendations
Lead analyses across multiple sources of quantitative and qualitative information; identify meaningful patterns, test hypotheses, surface limitations, and translate findings into practical recommendations.
Develop concise, executive-ready decision materials for the AI TO, functional leadership, Executive Leadership Team, and other senior stakeholders.
Communicate complex analyses simply, clearly articulating the answer, supporting evidence, implications, and recommended actions.
Independently lead analytical workstreams from problem definition through recommendation.
Build repeatable approaches and institutional capability
Develop practical frameworks, templates, benchmarks, and analytical tools that allow AI opportunities to be evaluated consistently across functions.
Capture learnings from initiatives and continuously improve the AI TO's value-realization methodology and operating routines.
Monitor emerging approaches to AI economics, measurement, workflow transformation, and value realization and selectively incorporate relevant practices into the AI TO playbook.
Skills and Competencies
Exceptional structured problem-solving skills and ability to turn ambiguous business questions into clear hypotheses, analyses, and recommendations.
Strong quantitative and analytical orientation, including demonstrated ability to build driver-based business and financial models from imperfect or incomplete information.
Strong business judgment and ability to identify the few metrics and value drivers that matter most.
Ability to quickly understand unfamiliar business processes, operating models, and economics.
Intellectual curiosity and willingness to dig deeply into source data, process details, assumptions, and calculations.
Strong understanding of the relationship between operational performance and financial outcomes.
Ability to synthesize quantitative and qualitative information into clear implications and recommendations.
Excellent written and verbal communication skills, including the ability to create concise, executive-ready materials.
Strong interpersonal skills and ability to build credibility with senior leaders, functional stakeholders, Finance, technical teams, and frontline subject-matter experts.
Confidence constructively challenging assumptions while maintaining productive stakeholder relationships.
Strong ownership mindset and ability to independently lead complex analytical workstreams.
Experience using generative AI tools to accelerate research, analysis, synthesis, and problem solving preferred.
Familiarity with BI tools, SQL, or other analytical tools is helpful but not required.
Qualifications
Bachelor's degree in business, economics, finance, engineering, analytics, or a related quantitative discipline; advanced degree preferred but not required.
5+ years of experience in strategy consulting, corporate strategy, strategic finance, transformation, performance improvement, or a similarly rigorous analytical environment.
Strong preference for candidates with experience at a leading strategy or management consulting firm, particularly in business diagnostics, commercial due diligence, corporate finance, performance improvement, or transformation.
Demonstrated experience developing analytical models, defining KPIs, establishing performance baselines, assessing business opportunities, and translating operational improvements into financial impact.
Experience synthesizing complex information from multiple sources and developing senior-management recommendations.
Ability to rapidly develop a working understanding of unfamiliar functions, industries, and business processes.
Experience in B2B technology, software, information services, or professional services preferred.
Prior experience with AI transformation or emerging technology is helpful, but deep technical AI expertise is not required.
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Compensation:
This role is eligible for Bonus.
Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.
Additional Information:
Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.
The AI TO is responsible for accelerating and scaling responsible AI adoption across Wolters Kluwer by helping functions identify high-value opportunities, reinvent workflows, coordinate enabling resources, govern risk, and deliver measurable business impact. Functions and business owners remain accountable for execution, adoption, and outcomes; the AI TO provides the rigor, expertise, visibility, and support required to accelerate progress.
The Associate Director role will work closely with functional leaders, business owners, Finance, Data, Technology, HR, and other stakeholders to determine where AI can materially improve business performance and to build the fact base required to make investment and scaling decisions.
The successful candidate will translate ambiguous questions such as “Could AI fundamentally improve this workflow?” into rigorous, evidence-based answers. This will require understanding how work is performed today, identifying the operational and financial drivers of performance, establishing credible baselines, defining the right KPIs, quantifying value at stake, pressure-testing assumptions, and measuring whether expected value is ultimately realized.
This is a hands-on strategy and analytics role. The ideal candidate combines the structured problem solving and business judgment of a strategy consultant with a strong quantitative orientation and a willingness to dig deeply into data, processes, assumptions, and economics.
Primary Accountabilities
Identify and diagnose high-value opportunities
Partner with functional leaders, process owners, and frontline subject-matter experts to understand how work is performed today and where AI-enabled workflow redesign could materially improve business outcomes.
Conduct business and process diagnostics to identify bottlenecks, sources of cost, delays, capacity constraints, quality issues, risk, or lost revenue.
Help distinguish incremental productivity opportunities from more transformational opportunities to redesign end-to-end workflows around human judgment, AI agents, data, and automation.
Assess the scale and materiality of opportunities and identify the key value drivers that determine whether an initiative merits investment.
Define KPIs and establish credible baselines
Translate broad transformation ambitions into a small number of meaningful business and operational KPIs.
Determine how relevant measures are calculated today, where the underlying data resides, who owns it, and what constitutes a credible baseline.
Gather, reconcile, and analyze information across multiple sources to establish current performance, including volumes, cycle times, throughput, productivity, quality, conversion, capacity, costs, customer outcomes, and other relevant measures.
Identify data gaps, limitations, and assumptions and develop pragmatic approaches for measuring performance where perfect data is not available.
Ensure initiatives have measurable success criteria before investment and implementation decisions are made.
Quantify value at stake
Build transparent, driver-based models that translate changes in operational performance into financial and strategic outcomes.
Quantify potential value from revenue growth, productivity, capacity creation, cost reduction, quality improvement, risk reduction, customer impact, or employee experience as appropriate.
Develop Year 1 and longer-term value estimates, expected operating costs, required investment, and net business impact.
Clearly distinguish between cash savings, capacity released, cost avoidance, revenue improvement, and other forms of value.
Document the critical assumptions behind each value case and identify the sensitivities that have the greatest effect on expected outcomes.
Develop and challenge business cases
Develop rigorous, directional business cases for priority AI opportunities, considering value, feasibility, investment, risk, readiness, adoption, and implementation complexity.
Pressure-test assumptions and challenge sponsors and business owners constructively where supporting evidence is weak.
Identify the critical conditions that must be true for an initiative to deliver its expected value.
Compare opportunities consistently to help leadership prioritize limited investment and execution capacity.
Support build / buy / partner analysis where relevant, incorporating expected economics, differentiation, operating costs, and dependencies.
Design value measurement and evaluate results
Define measurement approaches for pilots and scaled deployments, including baseline, target, leading indicators, operational KPIs, business outcomes, and measurement cadence.
Establish the analytical bridge between AI adoption, workflow change, operational performance, and financial impact.
Compare realized results with expected performance and diagnose the causes of variance.
Determine whether evidence supports scaling, modifying, pausing, or stopping an initiative.
Partner with Finance and business owners to ensure realized value is credible, traceable, and understood consistently.
Build the enterprise AI value view
Aggregate opportunity-level analyses into a transparent enterprise view of expected and realized AI value.
Identify the largest emerging value pools, material assumptions, risks, dependencies, and gaps across the AI portfolio.
Provide leadership with fact-based perspectives on where the enterprise is creating value, where performance is falling short, and where additional intervention or investment is required.
Help maintain clear accountability for business outcomes and KPI movement across priority initiatives.
Generate executive insight and recommendations
Lead analyses across multiple sources of quantitative and qualitative information; identify meaningful patterns, test hypotheses, surface limitations, and translate findings into practical recommendations.
Develop concise, executive-ready decision materials for the AI TO, functional leadership, Executive Leadership Team, and other senior stakeholders.
Communicate complex analyses simply, clearly articulating the answer, supporting evidence, implications, and recommended actions.
Independently lead analytical workstreams from problem definition through recommendation.
Build repeatable approaches and institutional capability
Develop practical frameworks, templates, benchmarks, and analytical tools that allow AI opportunities to be evaluated consistently across functions.
Capture learnings from initiatives and continuously improve the AI TO's value-realization methodology and operating routines.
Monitor emerging approaches to AI economics, measurement, workflow transformation, and value realization and selectively incorporate relevant practices into the AI TO playbook.
Skills and Competencies
Exceptional structured problem-solving skills and ability to turn ambiguous business questions into clear hypotheses, analyses, and recommendations.
Strong quantitative and analytical orientation, including demonstrated ability to build driver-based business and financial models from imperfect or incomplete information.
Strong business judgment and ability to identify the few metrics and value drivers that matter most.
Ability to quickly understand unfamiliar business processes, operating models, and economics.
Intellectual curiosity and willingness to dig deeply into source data, process details, assumptions, and calculations.
Strong understanding of the relationship between operational performance and financial outcomes.
Ability to synthesize quantitative and qualitative information into clear implications and recommendations.
Excellent written and verbal communication skills, including the ability to create concise, executive-ready materials.
Strong interpersonal skills and ability to build credibility with senior leaders, functional stakeholders, Finance, technical teams, and frontline subject-matter experts.
Confidence constructively challenging assumptions while maintaining productive stakeholder relationships.
Strong ownership mindset and ability to independently lead complex analytical workstreams.
Experience using generative AI tools to accelerate research, analysis, synthesis, and problem solving preferred.
Familiarity with BI tools, SQL, or other analytical tools is helpful but not required.
Qualifications
Bachelor's degree in business, economics, finance, engineering, analytics, or a related quantitative discipline; advanced degree preferred but not required.
5+ years of experience in strategy consulting, corporate strategy, strategic finance, transformation, performance improvement, or a similarly rigorous analytical environment.
Strong preference for candidates with experience at a leading strategy or management consulting firm, particularly in business diagnostics, commercial due diligence, corporate finance, performance improvement, or transformation.
Demonstrated experience developing analytical models, defining KPIs, establishing performance baselines, assessing business opportunities, and translating operational improvements into financial impact.
Experience synthesizing complex information from multiple sources and developing senior-management recommendations.
Ability to rapidly develop a working understanding of unfamiliar functions, industries, and business processes.
Experience in B2B technology, software, information services, or professional services preferred.
Prior experience with AI transformation or emerging technology is helpful, but deep technical AI expertise is not required.
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Compensation:
This role is eligible for Bonus.
Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.
Additional Information:
Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.
See All 73 AI Strategy Manager Jobs in New York
Find roles in New York that match your experience and apply in just a few clicks.
Find AI Strategy Manager JobsAI Strategy Manager Jobs by City in New York
Where New York roles are concentrated, by current openings.
AI Strategy Manager Job Market in New York
A snapshot from current New York openings, updated as new roles post.
Who's Hiring
- JPMorganChase14
- Logic8
- EY5
- Citi3
- Bloomberg3
Top Industries Hiring
- Technology & Software
- Retail
- Science & Research
- Banking & Financial Services
- Healthcare & Medical Services
What New York Employers Look For
The qualifications that appear most often in AI strategy manager jobs across New York.
- Bachelor's or master's degree in computer science, business analytics, or a related field
- Demonstrated experience developing and executing AI or machine learning strategy within an organization
- Proficiency with AI frameworks and tools such as TensorFlow, PyTorch, or Azure AI services
- Strong stakeholder management and executive communication skills across technical and non-technical audiences
- Experience with AI governance, ethics frameworks, or responsible AI policy in regulated industries
- Familiarity with New York's financial, healthcare, or media sector AI compliance and regulatory landscape
AI Strategy Manager Jobs in New York: Frequently Asked Questions
How do you become a ai strategy manager in New York?
Most ai strategy manager roles in New York require a bachelor's degree at minimum, with many employers preferring a master's in data science, business analytics, computer science, or an MBA with a technology focus. New York does not issue a state license for this role. Employers typically want candidates who have led AI or data initiatives, so building a portfolio of projects, earning credentials like the Google Professional Machine Learning Engineer or AWS Certified Machine Learning certification, and targeting industries where New York hiring is deepest, finance, healthcare, and media, is the most concrete path.
Which companies hire ai strategy managers in New York?
Employers hiring ai strategy managers in New York right now include JPMorganChase, Logic, and EY, based on current listings on Migrate Mate as of September 2026. New York's concentration of global financial institutions, major healthcare systems, and headquarters-level media and technology operations means demand for this role tends to stay consistent across economic cycles.
Which New York cities have the most ai strategy manager jobs?
New York, Albany, and Atlanta are the cities with the most ai strategy manager openings in New York. New York City dominates because it is home to the global headquarters of major financial, media, and professional services firms that are investing heavily in enterprise AI, while White Plains and Albany attract openings from large healthcare systems and state government technology modernization programs anchored in those metros.
Are there remote ai strategy manager jobs in New York?
Yes, and more than most fields. AI strategy is primarily a desk, analytical, and stakeholder-facing role, which makes it well suited to remote and hybrid arrangements. About 64% of ai strategy manager openings tied to New York are remote or hybrid as of September 2026, reflecting how broadly employers have accepted distributed work for strategy and advisory functions. The parts of the role most commonly performed remotely are roadmap development, vendor evaluation, and executive briefing preparation.
How can I get hired as a ai strategy manager in New York with little or no experience?
The most realistic entry path is moving into AI strategy from an adjacent analytical or product role such as data analyst, business intelligence analyst, or product manager at a New York employer already building out an AI practice. Large New York firms like JPMorgan Chase, Deloitte's New York offices, and IBM run rotational technology and strategy programs that accept candidates without direct AI strategy experience. Earning a recognized AI or data science certification alongside a portfolio of self-directed or academic AI projects strengthens a candidacy considerably, particularly for associate or junior strategist roles that serve as feeders to manager-level positions.
Where can I find and apply to ai strategy manager jobs in New York?
You can find and apply to ai strategy manager jobs in New York on Migrate Mate, which lists current New York openings updated regularly. Search the available roles, find the ones that match your background and target industry, and apply directly to the employers posting them.
See All 73 AI Strategy Manager Jobs in New York
Find roles in New York that match your experience and apply in just a few clicks.
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