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 JPMorganChase’s AI Strategy and Organization, Through Its Leadership Bench

 

  • JPMorgan is the pace-setter in banking AI — #1 on the Evident AI Index four years running (Banking Exchange, 2025), with a tech budget of $18B in 2025 rising to $19.8B in 2026, ~$2B in claimed annual AI value, LLM Suite in the hands of ~250,000 employees, and a first-mover push into long-running agentic AI — but it is navigating this from a position of leadership transition as AI chief Teresa Heitsenrether retires at end-2026 (Bloomberg, July 2026).
  • Its organizational design is its real competitive moat: a firmwide Chief Data & Analytics Office (CDAO) paired with AI leaders embedded in every business line (CCB, CIB, Payments, Asset & Wealth), a 200+ person Machine Learning Center of Excellence, a publishing AI Research group, and a model-agnostic platform (LLM Suite/OmniAI) that swaps OpenAI and Anthropic models every eight weeks.
  • For competitors, the actionable read is that JPMorgan is winning on talent density and scaled platform engineering, not flashy models; rivals should benchmark against its embedded-business-leadership model, its aggressive external hiring from hyperscalers, and its “governance in lockstep with autonomy” posture rather than trying to out-spend it.

Executive Summary

JPMorganChase enters mid-2026 as the acknowledged AI leader among global banks — ranked #1 for the fourth consecutive year on the Evident AI Index (Banking Exchange, 2025) — but at a moment of significant leadership transition. Its Chief Data & Analytics Officer, Teresa Heitsenrether, the architect of the bank’s AI strategy and the first bank AI leader to sit on an executive operating committee, will retire at the end of 2026 (Yahoo Finance/Bloomberg, July 2026), with her portfolio absorbed by CTO Scot Baldry. Beneath her, a deep and rapidly expanding bench of AI leaders — spanning research, platform engineering, product, governance, and business-line enablement — is executing a three-part strategy: a firmwide proprietary generative-AI platform (LLM Suite), an “internal-first” deployment philosophy, and a shift into long-running agentic AI. This paper uses the named leadership roster as a lens to map JPMorgan’s AI organization thematically and to position it against Goldman Sachs, Morgan Stanley, Citi, Bank of America, and Wells Fargo.

Key strategic signals for competitors: (1) JPMorgan is being “fundamentally rewired” around AI (CNBC, September 2025), with a technology budget of $18B in 2025 rising to $19.8B in 2026 and ~$2B in claimed annual AI business value; (2) it is aggressively poaching senior AI product and engineering talent from Google, Amazon, and rival banks; (3) it is embedding AI leadership directly into revenue-generating business units rather than centralizing it; and (4) it is moving faster than peers into autonomous, “long-running” agents while building a governance apparatus intended to let autonomy and controls “grow in tandem.”

Key Findings

  1. AI leadership sits on the Operating Committee — and that seat is changing hands. Teresa Heitsenrether, CDAO since 2023, was the first bank executive to hold AI leadership on a top operating committee; her retirement at end-2026 and the transfer of her duties to CTO Scot Baldry — who will not join the operating committee, reporting instead to CIO Lori Beer — represents a subtle organizational normalization of the standalone AI-chief role (Bloomberg / PYMNTS, July 2026).
  2. Derek Waldron, Chief Analytics Officer, is now the primary public voice of JPMorgan’s AI program, articulating the “fully AI-connected enterprise” vision and confirming the bank will deploy “long-running autonomous agents” in 2026 (CNBC, June 2026).
  3. LLM Suite is the centerpiece — a model-agnostic proprietary platform reaching ~250,000 employees (roughly half daily), updated every eight weeks, using OpenAI and Anthropic models, and named American Banker’s 2025 “Innovation of the Year.”
  4. JPMorgan is embedding AI leadership into each business line rather than centralizing — a deliberate strategy accelerated in 2025–26 (Evident, “AI remakes the org chart”).
  5. Aggressive external talent acquisition from Google (Jason Gelman, ex-Vertex AI), Dataminr (Alex Jaimes), Bank of America (Homan Milani), and HSBC (Ash Booth) is a defining 2025–26 pattern.
  6. A large, publishing AI Research organization (Sumitra Ganesh, multi-agent research) creates proprietary long-term advantage — a pillar most rivals lack at comparable scale.
  7. A deep but largely press-invisible governance and model-risk bench (Irina Moore, Anuj Prakash, Ali Marami, Scott Lombardo, and others) underpins the firm’s “governance in lockstep with autonomy” posture — visible mainly through firm-level programming rather than individual profiles.

Details by Theme

Theme 1 — Organizational Structure of AI at JPMorgan

The firm’s AI is organized through the Chief Data & Analytics Office (CDAO), created in 2023 under Teresa Heitsenrether, who reported into the president/COO and sat on the 12–15-person Operating Committee — a structural signal that no other major bank matched at the time (CIO Dive, 2023; CTO Forum, 2025). Her retirement at year-end 2026 after nearly four decades, announced in a Dimon/Piepszak memo, transfers the CDAO title and duties to CTO Scot Baldry. Per Bloomberg (July 1, 2026), the memo said “Teresa has played a pivotal role in… shaping the firmwide data and artificial intelligence strategy that is central to our future”; Baldry will report to Global CIO Lori Beer and will not take the Operating Committee seat (Bloomberg, July 2026). Competitors should read this as a maturation/normalization of the AI-chief role into the broader technology organization.

Reporting into this structure:

  • Derek Waldron — Chief Analytics Officer, the operational head of the overall AI program and its chief public spokesperson (McKinsey interview, October 2025; CNBC, June 2026).
  • Gerard Francis — Chief Product Officer: AI and Data (Firmwide), a former Bloomberg enterprise-data executive who joined via the Fusion data-solutions business; his mantra is that “in the absence of a great data, AI and governance platform, every AI experiment is non-repeatable” (Forbes, July 2025).
  • Manoj Sindhwani — CIO, Data and AI (CDAO), recruited from Amazon, responsible for the integrated data-and-AI platform; he has publicly discussed cutting agent build time “by up to 50%” and, in 2026, “navigating the evolving agentic risk landscapes” alongside CISO Pat Opet (CTO Forum, 2025).
  • A large Machine Learning Center of Excellence (200+ ML scientists, engineers, PMs — J.P. Morgan), with leaders including Chak Wong (MD, Time Series & Reinforcement Learning group, Hong Kong; also HKUST professor — J.P. Morgan) and Lidia Mangu (Head of the ML CoE).

JPMorgan is deliberately pushing AI leadership into the business lines. Evident’s “AI remakes the org chart” brief (2026) reported the bank elevating digital head Guy Halamish to COO of the Commercial & Investment Bank with a remit to restructure CIB units to “maximize the impact of AI” — described as scaling the Heitsenrether model “into each revenue-generating part of the business” (Evident, 2026).

Theme 2 — LLM Suite and Internal AI Platforms

LLM Suite is the strategic core: a proprietary, model-agnostic portal launched in summer 2024 that provides secure access to third-party LLMs (currently OpenAI and Anthropic), updated every eight weeks as more internal data and applications are connected. Chief Analytics Officer Derek Waldron told CNBC (September 30, 2025) that ~250,000 employees have access — essentially the entire workforce except branch and call-center staff (of a workforce cited at 317,000) — “and roughly half use it daily” (CNBC, September 2025). It reached 150,000+ daily users per Heitsenrether at the Evident AI Symposium (2025). It won American Banker’s 2025 “Innovation of the Year” grand prize and The Digital Banker’s 2025 “World’s Best Application of AI.”

Key platform leaders:

  • Dogan Bilguvar — Head of AI Solutions & Business Enablement; Head of LLM Suite Product (ex-McKinsey), owns the LLM Suite product roadmap (The Org).
  • Jason Gelman — MD, Global Head of AI Product, hired from Google, where he was Director of Product for Vertex AI; also a former AWS AI product lead and lawyer. He now “leads AI product management for Fusion, the firm’s internal data and AI platform” and is a listed Google Cloud Next 2026 speaker (eFinancialCareers, 2025).
  • Vijay Parthasarathy — MD, Firmwide Head of AI Technologies (ex-Zoom VP of AI/ML, ex-Meta/Apple/Netflix), focused on “advancing large language models and developing agentic frameworks” (The Org).
  • The platform is underpinned by the OmniAI ML factory and the JADE data ecosystem (per third-party strategy analyses).

Waldron describes the “North Star for LLM Suite” as an “AI hub for employees,” now being connected to internal data sources and workflows to create agents (JPMorganChase blog, 2025). Specialized variants include Connect Coach (private-bank advisors), SpectrumGPT, and the call-center assistant EVEE.

Theme 3 — Agentic AI Initiatives

Agentic AI is the declared 2026 frontier. Waldron told CNBC (June 2026): “We’ve entered now the era of long-running autonomous agents,” and that agents capable of running for hours autonomously — clearing prior security/governance hurdles — “we will have those in 2026.” He frames the endgame as every employee having a personal AI assistant, every process powered by agents, and every client experience curated by an AI concierge (CNBC, June 2026; CNBC, September 2025). JPMorgan cited a 20% increase in private-banking gross sales and the potential for bankers to expand client coverage by “as much as 50%.”

Agentic leadership is distributed:

  • Alex Jaimes — MD, Global Head of AI Agents and Search (Engineering & Applied Research), hired from Dataminr (ex-chief AI officer), leading AI agents and search “across all of digital (mobile, web) on the consumer side” for a user base of 80M+ customers; he spoke at the MIT AI Summit 2026 (eFinancialCareers, 2026; Evident, 2026).
  • Sumitra Ganesh — Head of AI Research (Multi-agent Learning & Simulation / AI Agents and Hybrid Reasoning), whose team builds multi-agent reinforcement-learning simulation platforms and who leads the firm’s “AI Agents Community of Interest” for the CDAO (J.P. Morgan).
  • Tracey Beberman — MD, Head of AI for CCB Operations, Agentic AI Servicing Transformation & Knowledge Management, who convened a “CCB Operations AI leadership team” year-end strategy session and stresses that “AI creates real, scalable value only when it moves in lockstep with transformation” (LinkedIn, 2026).
  • Frank Van Hoof — Product Head, Agentic AI for Employee Experience (London).

On the client/consumer side, CCB chief Marianne Lake publicly tempered expectations, saying she does not expect agentic commerce to take off soon — AI has removed friction in search/discovery but “what we’re not seeing is that that’s moving into the transaction part of this… Because when people are moving money, things change” — and the bank aims to pilot a consumer-facing AI travel agent before year-end (Banking Dive, February 2026). This build-out is supported by internal agent tooling such as the component-testing agent integrated across 80 services (IT Brew, February 2026).

Theme 4 — AI Governance, Model Risk, and Responsible AI

JPMorgan’s 2026 governance thesis, articulated at its 11th Innovation Week, is that “autonomy and controls must grow in tandem to counter agentic risks,” and that winners will “pair strong context, economic guardrails and disciplined governance” (JPMorganChase blog, 2026). The panel featured CIO Lori Beer, CISO Pat Opet, and Payments/Global Banking CIO Sri Shivananda. Beer separately warned that AI brings both productivity gains “as much as 30%” and new leadership and cybersecurity challenges (Bloomberg, May 2026). Opet has become a prominent industry voice on AI-driven supply-chain security, keynoting RSAC 2026 and arguing agents should ideally run with “an identity but no entitlements.”

Policy positioning is led by Terah Lyons — MD, Global Head of AI & Data Policy, a former Obama White House OSTP advisor and founding executive director of the Partnership on AI, who sits on the Stanford AI Index board and argues for sector-specific, globally coordinated, standards-driven AI governance (Innovate Finance; JT Consulting/MIT EmTech coverage).

The model-risk and compliance bench is deep but has minimal public press footprint (confirmable primarily via org-chart/data-broker sources, so titles should be treated with mild caution):

  • Irina Moore — Head of Model Risk for Wholesale, CIB Digital & AI; Head of AIML Governance.
  • Anuj Prakash — ED, Head of AI, Compliance Conduct and Operational Risk (CCOR) — his function provides “independent second-line coverage” of the CDAO’s AI initiatives (JPMorgan job posting).
  • Ali Marami — ED, Head of Operations and Digital AI/ML Model Risk Management (ex-First Republic head of model validation).
  • Scott Lombardo — MD, Global Head of CIB Infrastructure and AI/ML Technology Risk.
  • Alexey Kvashchuk — ED, Head of AI/ML Standards and Governance; Phyllis Asiama-Bekoe (listed as Global Head of AI Governance, though public profiles show a Client-Onboarding AI Transformation title — a discrepancy to verify); Sigi Billar (EMEA Head of Data & AI CCOR); Koosha Golmohammadi (Applied AI/ML in Corporate Tech — Compliance & Risk), who led a session at the AI in Finance Summit NY 2025.

Theme 5 — AI in Consumer Banking (CCB) vs. CIB vs. Payments vs. Asset & Wealth

Consumer & Community Banking (CCB): At the 2025 investor day the division reported a 35% year-on-year increase in AI/ML value with an expected further 65% rise (eFinancialCareers, 2026). CCB AI leadership includes Alex Jaimes (agents & search for 80M+ customers), Tracey Beberman (CCB operations/agentic servicing), Kevin Cole (Head of Chase Digital Assistant — note: not to be confused with a same-named Wells Fargo/BofA “Erica” executive), Katherine Hainsey (CCB Head of Product Data, Analytics, AI), and Jonathan Lalima (AI & Innovation Product, Chase Travel). CCB’s consumer chief told investors operations staff would fall “by at least 10%” over five years due to AI (CNBC, 2025).

Commercial & Investment Bank (CIB): Sameena Shah — Chief AI, Data, and Transformation Officer for CIB Operations; Global Head of Client Onboarding (PhD, ex-Thomson Reuters/S&P) is applying agentic AI to KYC workflows, discussed in a WatersTechnology feature comparing J.P. Morgan, ING, and Standard Chartered (LinkedIn / WatersTechnology, 2026). Ash Booth — MD, Head of Applied AI, Markets (hired from HSBC, promoted to MD) leads AI for Markets Operations and is hiring “Agentic Interface” specialists in London. On the deal side, Homan Milani joined from Bank of America as MD and co-head of JPMorgan’s AI investment-banking effort as the firm topped tech M&A league tables (Reuters via WMBD, April 2026). Other CIB AI leaders: Dror Ayalon (Head of AI Product, CIB), Marvion Campbell (Data/Analytics/AI for CIB MarComms), Allie Gillon-Livesey and Carmen Leon (CIB AI Enablement).

Payments: Florencia Ardissone — MD, Head of AI Transformation for Payments and Shantanu Chandra — MD, Payments AI/ML, Trust & Safety anchor a Payments AI organization whose CDAO, Zack Anderson, framed the 2026 agentic-payments governance challenge as “deciding what they are willing to delegate to a machine, at what thresholds, and under what conditions” (J.P. Morgan, June 2026).

Asset & Wealth / Private Bank: LLM Suite originated in the asset & wealth management division; the Connect Coach advisor tool and a cited 20% increase in private-banking gross sales illustrate the value case; Christina Shanks leads Strategy & Transformation for GPB Transformative AI.

Theme 6 — Talent and Hiring Strategy

Evident’s 2025 Index found the AI headcount across the 50 tracked banks “grew more than 25%, the largest increase since the Evident AI Index launched in early 2023,” noting that “the more people a bank brings on, the more likely they are to roll out new use cases” (Evident, 2025). JPMorgan exemplifies this with high-profile external hires from hyperscalers and rivals:

  • Jason Gelman (Google/Vertex AI) → Global Head of AI Product.
  • Alex Jaimes (Dataminr) → Global Head of AI Agents and Search.
  • Homan Milani (Bank of America) → co-head of AI investment banking.
  • Ash Booth (HSBC) → Head of Applied AI, Markets.
  • Manoj Sindhwani and Darrin Alves (both Amazon) → CDAO CIO and Infrastructure Platforms CIO.
  • Vijay Parthasarathy (Zoom/Meta/Apple) → Firmwide Head of AI Technologies.

The bank also invests heavily in internal upskilling: the “AI Made Easy” training program has enrolled tens of thousands, and JPMorgan now tracks AI-tool usage for 65,000 engineers via a dashboard (light/heavy/non-users), with AI adoption a formal performance-review criterion (Let’s Data Science, 2026, citing Waldron and Beer). Early-careers pipelines are led by figures such as Paul Sutherland (Head of Data & AI Early Careers), and the 2026 Data & Analytics intern cohort exceeded 160 students globally (Heitsenrether, LinkedIn, 2026).

Theme 7 — AI Infrastructure and Model Partnerships

JPMorgan’s tech budget is $18B in 2025, rising to $19.8B in 2026 — an increase of “about $2 billion or 10% from 2025,” with “$1.2 billion of the increase” targeting AI in customer service, client insights, and software engineering (CFO Jeremy Barnum, via Business Insider, January 2026). About $2B of the overall budget is directly AI-tagged. Daniel Pinto (President & COO) “recently raised estimates of AI-related returns from $1.5 billion to nearly $2 billion” (Banking Exchange / Evident AI Index 2025) — with fraud prevention the single biggest contributor — a figure commonly cited by 2026 as ~$2B in realized annual value (Forbes, July 2026).

Infrastructure leadership: Anand Rajagopalan (Head of AI Infrastructure Platform), John Rollins (MD, Head of Engineering, Firmwide AI/ML Platform), Neela Vannan (Chief Architect, Firmwide AI Platform, Edinburgh), and Darrin Alves (CIO, Infrastructure Platforms, sourcing compute “5 to 10 years out”). Storage/observability specialists include Stuart Burrill (Object & AI Storage Engineering) and Isabella Disler (Observability and AI for SRE).

Model partnerships: LLM Suite launched on OpenAI’s models and is now model-agnostic across OpenAI and Anthropic (CNBC, September 2025). In April 2026 JPMorganChase was named a launch partner in Anthropic’s Project Glasswing, testing Anthropic’s frontier Claude “Mythos” model for defensive cybersecurity alongside AWS, Google, Microsoft, NVIDIA, and others; JPMorgan stated it would “take a rigorous, independent approach to determining how to proceed” (Anthropic; Fortune, April 2026). Note: “Mythos” is Anthropic’s model, not a JPMorgan-owned model. The bank’s model-agnostic design — the ability to “swap in any vendor’s offerings on the fly” — was singled out by Evident as an industry-leading architectural choice (Evident, 2025). Heitsenrether has said the plan is “not to be beholden to any one model provider.”

Competitive Positioning

JPMorgan vs. the field. JPMorgan has topped the Evident AI Index for four straight years, ranking #1 in innovation, leadership, and transparency, and was the first bank to earn a perfect leadership-pillar score (Banking Exchange, 2025). Capital One is the closest challenger and leads Evident’s Talent pillar; following its Discover Financial Services acquisition in May, it “maintained its dominance in AI talent density despite its workforce increasing by around 40%” while retaining the #2 rank. Royal Bank of Canada, CommBank, and Morgan Stanley round out the top five (Evident, 2025).

  • Morgan Stanley jumped five places into the top five, with OpenAI-powered advisor tools and DevGen.AI, a coding agent (built on OpenAI GPT models, launched January 2025) that “reviewed nine million lines of old code and saved its developers 280,000 hours,” per global head of technology and operations Mike Pizzi (Wall Street Journal, June 2025). It differentiates on embedded, continuous model-evaluation operations.
  • Goldman Sachs re-entered the top ten after CEO David Solomon’s “three-year efficiency program,” poached Amazon’s Daniel Marcu as global head of AI engineering & science, and rolled out its GS AI Assistant firmwide; Goldman is also a Project Glasswing/Mythos participant.
  • Bank of America made its top-ten debut, gaining five spots, with new AI leadership under Hari Gopalkrishnan overseeing a ~$13–14B tech budget and its consumer assistant “Erica.”
  • Citi is the most acquisitive (most AI-company investments of the tracked banks) and pursues an “AI accelerators”/superuser model under CTO David Griffiths, who cautioned banks don’t need “10,000 different AI solutions.”
  • Wells Fargo trails the money-center leaders on AI maturity but has a mature consumer assistant, “Fargo,” led by head of digital AI Kevin Cole (distinct from JPMorgan’s Kevin Cole).

Where JPMorgan’s edge is durable: talent density and scaled platform engineering (LLM Suite + OmniAI + JADE), a genuine publishing AI Research arm, model-agnostic architecture, and — uniquely — embedding AI leadership inside revenue-generating business lines. Where it is exposed: the loss of an Operating Committee-level AI champion with Heitsenrether’s retirement; execution risk on long-running agents; and, on the consumer discovery layer, JPMorgan shows strong AI “citation share” in consumer banking (28.4%) but Goldman dominates investment-banking/wealth citation share (41.6%) in AI answer engines (5W PR Banking AI Visibility Index, 2026).

Recommendations

For a competitor bank benchmarking against JPMorgan:

  1. Immediate (0–6 months): Benchmark org design, not spend. Do not try to match JPMorgan’s ~$20B budget. Instead, replicate its highest-leverage structural move: place accountable AI leaders inside each revenue-generating business line (as JPMorgan did with Sameena Shah in CIB Operations, Tracey Beberman in CCB, and Florencia Ardissone in Payments), coordinated by a central CDAO-equivalent. Threshold to watch: whether your rival’s AI use-case count grows in step with embedded-leader headcount.
  2. Immediate: Adopt a model-agnostic platform architecture. JPMorgan’s ability to swap OpenAI/Anthropic models every eight weeks is a defensive necessity, not a luxury. Build an internal LLM-gateway equivalent before committing to client-facing agents.
  3. Near-term (6–12 months): Compete on talent acquisition from hyperscalers. JPMorgan is systematically hiring from Google, Amazon, Meta, and rival banks. Target the same profile — cloud-AI product leaders and applied-research scientists — and fund a publishing research arm to attract them.
  4. Near-term: Instrument adoption. JPMorgan tracks 65,000 engineers’ AI usage and ties it to performance reviews. Stand up equivalent adoption telemetry and an “AI Made Easy”-style training program; measure daily-active-user share, not license counts.
  5. 12–18 months: Sequence agentic AI behind governance. Follow JPMorgan’s “autonomy and controls grow in tandem” model — deploy long-running agents internally first (operations, software testing) before client-facing commerce, where even JPMorgan’s Marianne Lake counsels patience.

Benchmarks that would change this assessment: (a) if JPMorgan’s post-Heitsenrether structure loses momentum (watch for AI use-case rollout slowing or senior AI departures); (b) if Capital One overtakes JPMorgan on the Evident Index; (c) if a rival demonstrates client-facing agentic commerce at scale before JPMorgan’s piloted travel agent ships; (d) if the widely discussed “value gap” between AI capability and captured value fails to close, pressuring the ~$2B ROI claim.

Caveats and Methodological Notes

  • Search-coverage limitation. This analysis was required to search each of 121 named individuals. Due to research-tool budget constraints, individual named searches were completed for a subset of the most senior/likely-covered figures (roughly two dozen), supplemented by firm-level thematic research and a dedicated deep-dive on the governance and infrastructure cohort. The remaining names (listed in the Appendix) were not individually confirmed to have or lack 2026 press coverage; absence here should not be read as definitive absence of coverage.
  • Individuals with substantive 2026 (or contextual late-2025) coverage: Teresa Heitsenrether and Derek Waldron (contextual leaders, not on the numbered list), Terah Lyons (#11), Sameena Shah (#81), Alex Jaimes (#35), Homan Milani (#21), Gerard Francis (#64), Dogan Bilguvar (#36), Jason Gelman (#5), Manoj Sindhwani (#50), Sumitra Ganesh (#30), Ash Booth (#7), Tracey Beberman (#22), Chak Wong (#4), Vijay Parthasarathy (#8), Koosha Golmohammadi (#107), Neela Vannan (#68).
  • Titles confirmed but no findable press coverage: Irina Moore (#47), Anuj Prakash (#98), Ali Marami (#112), Scott Lombardo (#44), Anand Rajagopalan (#9), John Rollins (#32), Alexey Kvashchuk (#90), Kevin Cole (#19).
  • Data-quality flags. Several structural details rely on secondary/analyst sources (Klover.ai, emerj, The Org, RocketReach, ZoomInfo) that should not be treated as primary; core figures were corroborated against CNBC, Bloomberg, Reuters, Forbes, and JPMorgan’s own communications where possible. The “Global Head of AI Governance” title for Phyllis Asiama-Bekoe (#95) could not be confirmed and conflicts with her public profile. “Kevin Cole, Head of Chase Digital Assistant” must be distinguished from same-named executives at Wells Fargo and Bank of America. The ~$2B AI-value figure is Daniel Pinto’s, not Jenn Piepszak’s. “Mythos” is Anthropic’s model, not JPMorgan’s. The Marianne Lake agentic-commerce remarks were reported by Banking Dive in February 2026 (dated in-text accordingly).
  • Forward-looking statements. Claims about “long-running autonomous agents” in 2026, a consumer travel agent pilot, and 50% client-coverage expansion are stated intentions/projections by JPMorgan leaders, not completed deployments.

Appendix — Listed Individuals Without Individually Confirmed 2026 Coverage in This Study

The following listed names were not individually verified for 2026 press coverage within the research budget and are candidates for a follow-up pass: Mehul Doshi, Mike Dimond, Donald Stephens, John Greenall, Bhushan Shinkre, Jeremy Hanson, Eklavya Mehtani, Tony Forster, Caroline Mentis, Katherine Hainsey, Jonathan Lalima, David Tabit, Kalyan Sengupta, Bijan Vafaei, Isabella Disler, Jessica Holroyd, Andrew Jennings, Jay Katukuri, Shantanu Chandra, Jing Wang, Arvind Ramalingam, Jiamei Knox-Zhang, Luke Lenny, Florencia Ardissone, Dilshat Uteshev, Kal Gangavarapu, Dror Ayalon, Piyush Vakil, Travis Wiggins, Yuliana Perez, Marvion Campbell, Siva Ramakrishnan, Vadim Kutsyy, Ashraf Talib, Brandon Micci, Samit Majumdar, Alain Demour, Mason Shen, Megan Farmer, Alix Poulton, Patty Hong, Brent Demar, Nina Belenkiy, Hemathri Balakrishnan, Allie Gillon-Livesey, James Whiting, Carmen Leon, Si Yang, Stuart Burrill, Frank Van Hoof, Marianne Gosmant, Paul Sutherland, Josh Warren, Varshita Agarwal, Anand Cheruvanky, Yassir Nawaz, Shweta Shindadkar, Carl Chwiej, Shannon Goad, Vivienne Xu, Lucy Hiley, Tony Zazzu, Sarah Hedrych, Ghalia Alattar, Riley Neher, Kumar Krishnagi, Saudamini Naik, Christina Shanks, Ronald Jansen, Kevin Wu, Jake Henderson, Herve Schnegg, Darren Apostolik, Phyllis Asiama-Bekoe, Rajesh Drv, Sigi Billar, Nigel Auger, Robert Lattis, Debbie Eng, Steven Rauch, Sara Langbecker, Nandan Mishra, Maria Minchenko, Lisa Neunder, Brian Bovino, Abhi Bhagat, Chyree Wilson, Subhi Singh, Peter Krishnan, Jose Silva, Evan Shenkin, Chrysa Kolovou, Noel Kigaraba, Alissa Verhaaren, Piyush Patel, Lara Simpson, Sheena Khanchandani.