What is the Thomson Reuters Future of Professionals Report 2026?

It is Thomson Reuters' annual global survey of how technology is reshaping professional work. The 2026 edition surveyed 1,816 professionals across law, tax, audit, accounting, compliance, and related fields in March–April 2026.

What does Thomson Reuters mean by a $143 billion AI value gap?

Thomson Reuters estimates that up to $143 billion in U.S. client revenue is under active reconsideration because organizations are falling behind on effective AI implementation — not because AI tools are unavailable.

What is Fiduciary-Grade AI?

Thomson Reuters' term for AI built for high-liability professional work: authoritative domain content, privacy and security controls, transparent and verifiable outputs, and human accountability at every step.

Why does the AI value gap matter for personal injury firms?

PI and mass tort practices compete on speed of evidence. Firms that adopt AI for medical-record analysis and workflow without governance risk talent loss and client skepticism; firms that execute with verifiable tools convert the adoption wave into faster case decisions.

Thomson Reuters Warns of a $143 Billion AI Execution Gap for Professional Firms

Thomson Reuters released its 2026 Future of Professionals report on June 22, warning that AI ambition is outrunning implementation across law, tax, and audit. Based on a global survey of 1,816 professionals, the firm estimates that up to $143 billion in U.S. client revenue is under active reconsideration as clients and talent respond to uneven AI execution.

The message for plaintiff firms is direct: buying AI tools is no longer the differentiator. Proving that outputs are accurate, auditable, and tied to case outcomes is.

$143B

U.S. client revenue under active reconsideration from AI execution gaps (Thomson Reuters)

1,816

Professionals surveyed globally for the 2026 Future of Professionals report

Jun 222026

Report release date via Thomson Reuters / PR Newswire

Ambition Without Accountability

The report's core finding is not that professionals reject AI — many already use it weekly — but that organizations fail to convert usage into trusted workflows. Thomson Reuters President and CEO Steve Hasker framed the standard for liability-heavy professions bluntly: when outputs shape legal judgments or client advice, "'almost right' isn't good enough."

That framing tracks what plaintiff firms already see in medical-record work. Summaries that miss a revision surgery date or invent a diagnosis code create malpractice risk, not efficiency. The firms gaining ground are those that treat AI as a supervised evidence system, not a drafting toy.

Not all AI is created equal. In professions where there is real liability, the standard has to be much higher. When outputs shape legal judgments, regulatory filings, or client advice, 'almost right' isn't good enough.
Steve Hasker
President and CEO, Thomson Reuters

What "Fiduciary-Grade AI" Means in Practice

Thomson Reuters brands the required standard as Fiduciary-Grade AI: tools built on authoritative domain content, rigorous privacy and security, subject-matter expertise, transparent and verifiable outputs, and access to human support. For litigation teams, that maps to concrete checklist items:

AI Firms Can Defend

Outputs linked to retrieved medical pages and dates
Human review gates before demand letters or inventories
BAAs, encryption, and no-training-on-client-data policies
Firm policies on disclosure, billing, and supervision

AI That Creates Risk

Generic chatbots drafting chronologies without source pins
No audit trail from PDF page to summary claim
Client PHI used to train third-party models
Associates pasting outputs into filings unchecked

The same gap showed up earlier in 2026 when the 8am Legal Industry Report found individual AI use far ahead of firm policy — a governance problem Thomson Reuters now prices in client-revenue terms.

Implications for Plaintiff Practices

Case velocity is the competitive surface

Personal injury and mass tort firms win or lose on how fast they can turn records into valuation. AI that shortens retrieval-to-chronology cycles — when verified — directly affects settlement timing. Platforms focused on medical evidence, including LlamaLab, sit inside that workflow rather than beside it.

Talent watches the stack

The Thomson Reuters survey links weak AI execution to retention risk. Associates who spend weeks chasing records while peers at AI-enabled firms work from structured chronologies notice. Hiring markets increasingly treat tool quality as culture.

Clients are shopping the gap

"$143 billion under reconsideration" is not a forecast of vanishing legal spend — it is a signal that buyers will move work when firms cannot demonstrate reliable AI-assisted delivery. For contingency practices, that can mean referral partners choosing different co-counsel.

Looking Ahead

Key Points

Essential takeaways from this article

Treat AI procurement as an evidence-integrity decision, not an IT experiment

Require source-linked outputs for any medical chronology or case summary used in valuation

Document supervision and confidentiality protocols before expanding AI to more practice areas

Measure ROI on time-to-complete-records and inventory readiness — not vanity usage metrics

The Bottom Line

Thomson Reuters' 2026 report puts a dollar figure on a problem plaintiff firms already feel: AI availability is solved; AI accountability is not. Firms that close the execution gap will convert technology into faster, defensible case decisions. Firms that do not will watch clients and talent reconsider.