JPMorgan Just Fired the Middlemen: What Happens When AI Replaces Outsourced Judgement

The first major asset manager to cut proxy advisers entirely

JPMorgan Asset Management manages over $7 trillion in client assets. This week, it severed all ties with the proxy advisory firms that have guided shareholder voting for decades. The replacement: Proxy IQ, an internal AI platform that aggregates and analyses proprietary data from more than 3,000 company annual meetings.

This is not a pilot programme. This is not a phased transition. JPMorgan is the first major investment firm to end external proxy advice for US voting entirely, effective immediately.

The move matters because it reveals a strategic pattern that project professionals can learn from. Large organisations are using AI to internalise decision logic once outsourced, tightening control over governance, risk and reputational exposure whilst lowering cost and dependency on external intermediaries.

The same logic applies to professional services that organisations currently buy: risk assessment, compliance review, technical due diligence, and quality assurance. AI doesn't just automate tasks. It enables organisations to bring entire functions in-house that previously required specialist external providers.

Why JPMorgan Made This Move Now

Proxy advisory firms like Institutional Shareholder Services (ISS) and Glass Lewis dominate the market. Together they account for more than 90% of proxy advisory services. They provide research and voting recommendations to investment firms required to cast votes at thousands of shareholder meetings each year.

Jamie Dimon, JPMorgan's CEO, has been advocating for change. Last year he called proxy advisers "incompetent" and said their dominance "should be gone." In his annual shareholder letters, Dimon has highlighted concerns about "undue influence" on shareholder elections.

The criticism extends beyond JPMorgan. Corporate executives and other stakeholders argue that proxy advisers wield disproportionate power and operate business models that may involve conflicts of interest. Some critics say the firms push particular agendas, particularly around environmental, social and governance (ESG) issues, rather than focusing purely on fiduciary duty.

President Trump signed an executive order in December directing federal agencies to investigate proxy advisory firms for violating antitrust, unfair competition and deceptive practices laws. The order specifically highlighted ESG and diversity, equity and inclusion (DEI) policies.

SEC Chair Paul Atkins warned of plans to examine proxy advisory firms over the "weaponisation of shareholder proposals by politicised shareholder activists."

JPMorgan's timing reflects both political environment and technological capability. Regulatory scrutiny is increasing. And the technology to replace external advisers has reached viable maturity.

"By harnessing advanced AI, we no longer need third-party data collection or voting recommendations in the US," the internal memo stated. "This reinforces our unwavering commitment to vote solely in clients' best interests, using our information advantage." Source: governence-intelligence.com

That last phrase reveals strategic thinking: "our information advantage." JPMorgan is demonstrating that its internal data, combined with AI analysis, produces better recommendations than external advisers who work across multiple clients with less granular information.

What Proxy IQ Actually Does

Proxy IQ sits on JPMorgan's Spectrum investment platform, which manages over $3 trillion in assets across more than 11,000 portfolios. The AI platform combines firm policy, research and issuer data to produce voting recommendations that are faster, more consistent and aligned with JPMorgan's own investment views.

The system eliminates reliance on third-party judgements. Instead of outsourcing analysis to ISS or Glass Lewis, JPMorgan processes meeting data internally, applies its own investment criteria and generates recommendations that reflect its institutional perspective.

This approach improves transparency and accountability. When recommendations come from an external adviser, the logic behind them can be opaque. When they come from an internal AI system, the organisation controls the inputs, the weighting and the decision framework. Disputes about voting rationale get resolved internally rather than through intermediaries.

The cost dynamics favour internalisation. Proxy advisory services are expensive, particularly for large asset managers voting across thousands of meetings annually. Building an internal AI platform requires upfront investment, but the marginal cost of each additional analysis is near zero. Over time, the economics strongly favour internalisation.

JPMorgan is completing its transition to Proxy IQ during the first quarter of 2026. The operational model clearly shifts from buying external expertise to deploying internal AI capability.

The Broader Strategic Pattern

Our take on this: JPMorgan's decision demonstrates how organisations will use AI to internalise functions once considered too specialised, too complex or too costly to handle without external providers.

Proxy advisory is not unique. It represents a category of professional services where judgement, consistency and data analysis are the core value. These are precisely the domains where AI creates opportunity. If an organisation has sufficient data and can define decision criteria clearly enough to encode them in an AI system, external advisers become optional rather than necessary.

Consider the parallels in project delivery. Organisations currently outsource risk assessment to specialist consultancies. They hire external quality assurance firms. They bring in technical advisers for complex engineering reviews. They retain legal counsel for contract interpretation. They pay for environmental impact assessments, regulatory compliance audits and safety reviews.

Most of this work involves structured analysis of data against defined criteria. An AI system with access to the right data and properly configured decision logic can perform that analysis faster, cheaper and more consistently than external advisers who must rebuild context for each engagement.

The opportunity requires two prerequisites: data access and decision framework clarity. JPMorgan succeeded because it controls enormous datasets on company performance, governance practices and voting history. It also has clear investment criteria that can be translated into algorithmic logic.

Organisations that lack those prerequisites cannot simply copy JPMorgan's approach. But organisations that do have rich internal data and well-defined decision processes should be evaluating whether they can build equivalent capability in-house rather than continuing to buy external judgement.

What Changes for Project Professionals

The internalisation trend creates opportunities for project delivery in three ways.

First, vendor relationships evolve. When organisations rely less on external advisers, they gain operational flexibility. Dependency on third-party judgement decreases. The organisation owns both capability and accountability. If Proxy IQ performs well, JPMorgan captures the full value. If it needs adjustment, JPMorgan can modify it directly rather than negotiating with external providers.

Second, skills development accelerates. Managing AI platforms requires different expertise than managing external adviser relationships. JPMorgan needs people who can configure AI decision logic, validate outputs and monitor for drift. These capabilities create internal career paths and development opportunities that didn't exist before.

Third, governance structures adapt. When an external adviser makes a recommendation, accountability is clear. When an AI system makes a recommendation, accountability requires definition. The data scientists who built it, the investment managers who configured the decision criteria, and the governance team that approved its deployment all play roles. Organisations that clarify these accountabilities early will move faster.

For project managers, the opportunity is straightforward: identify which external services your organisation currently buys that could plausibly be internalised through AI. Then assess whether you have the data, the decision framework clarity and the technical capability to make that transition.

Not every function will internalise. Some require judgement that AI cannot yet replicate. Some involve relationships or sector knowledge that data alone cannot substitute. But more functions are candidates than most organisations have recognised, and the organisations that move first will capture advantage.

Where This Goes Next

JPMorgan's move will prompt other asset managers to reconsider their proxy advisory relationships. If one major player can eliminate external advisers entirely, others will evaluate whether they can do the same.

The broader signal extends beyond finance. Any sector where organisations rely on external advisers for structured decision-making should expect increased interest in internalisation through AI. The economics are compelling. The control advantages are clear. The technology is ready.

Project professionals should track which functions move in-house over the next 18 months. Those moves will reveal where organisations see AI as mature enough to replace external expertise. They will also reveal where organisations believe they have better data or more aligned incentives than external providers.

JPMorgan argues this improves transparency, accountability and alignment with client interests. Some observers will say it concentrates power, reduces independent oversight and creates potential governance gaps. Both perspectives merit consideration.

The opportunity lies in understanding which functions can be internalised effectively, what governance structures enable that transition and how accountability adapts when AI mediates decisions that humans previously made.

Building Your Internalisation Strategy

JPMorgan has demonstrated what's possible when organisations have the right data, clear decision frameworks and mature AI capability. The path to similar advantage requires deliberate strategic choices.

Identify high-cost external services that involve structured data analysis. Start with functions where you're paying consultants to analyse your own data against defined criteria. Risk assessment, compliance review, quality assurance and technical due diligence are prime candidates. The organisations that map their external spend against AI-enabled alternatives will find substantial opportunity.

Assess whether internal data quality and volume support AI-driven alternatives. JPMorgan succeeded because it controls enormous datasets. Your organisation doesn't need equivalent scale, but you do need sufficient data to train reliable models. Conduct honest assessments of data completeness, consistency and accessibility before committing to internalisation.

Define decision criteria clearly enough to encode in algorithmic logic. If you cannot articulate your decision framework precisely, you cannot automate it effectively. The organisations that document how they currently make decisions will discover which functions can be internalised and which still require human expertise that data alone cannot substitute.

Build or acquire AI capability to process data and generate recommendations. This is a build-versus-buy decision that depends on your organisation's technical maturity and strategic priorities. Some organisations will develop internal capability. Others will partner with AI platform providers. Both paths work if aligned with organisational strengths.

Establish governance structures that clarify accountability for AI-mediated decisions. When an AI system recommends a course of action, who is responsible for that recommendation? Clear accountability enables faster decision-making and better outcomes. The organisations that define these structures early will avoid the confusion that slows late movers.

Create skills development paths for people who will configure and maintain systems. Internalisation creates new roles and career paths. The organisations that invest in developing this talent internally will build capability that external hiring cannot easily replicate.

The window for competitive advantage is open. JPMorgan moved first in proxy advisory. Other organisations will move first in other domains. The internalisation wave is building momentum, and first movers will establish positions that late entrants struggle to match.

Subscribe to Project Flux for strategic analysis that helps you identify internalisation opportunities before your competitors do. Every week, we track how leading organisations are using AI to bring external functions in-house, and translate those patterns into actionable strategies for project delivery. The organisations that recognise which capabilities to internalise are building lasting competitive advantage. Get the insights that put you ahead.