Thomson Reuters has reported a 10 per cent increase in third-quarter revenue, supported by its legal, corporate and accounting businesses, but a decline in its share price has highlighted growing investor concerns over the financial consequences of expanding artificial intelligence investment.
The Toronto-based information and technology company announced on Tuesday, 3 November, that quarterly revenue had reached $1.83 billion, compared with $1.66 billion during the corresponding period in 2025.
The company also raised its forecast for organic revenue growth in 2026 to between 8.5 and 9 per cent, reflecting stronger performance across its principal business divisions.
However, its shares fell approximately 7 per cent during trading as investors examined the effect of increased technology expenditure on margins and cash generation.
The contrasting developments illustrate the changing expectations surrounding AI investment. Revenue growth alone is no longer sufficient to resolve questions about how quickly substantial expenditure on new technology will generate financial returns.
Strong performance across professional services
According to the company’s official financial results, its three principal business divisions — Legal Professionals, Corporates, and Tax, Audit & Accounting Professionals — recorded combined organic revenue growth of 10 per cent.
These operations account for approximately 88 per cent of total company revenue.
The results were supported by recurring subscriptions and increased transaction-based sales.
Recurring revenue is particularly important for professional information businesses because customers depend on continuous access to specialist data, research, software and compliance services.
This commercial model provides more predictable income than one-off technology purchases.
Thomson Reuters has also been expanding its AI-enabled professional products, including CoCounsel Legal and CoCounsel Tax, Audit & Accounting.
Chief executive Steve Hasker said customer engagement with the company’s latest AI applications had been encouraging.
The company is also exploring commercial opportunities for Thomson LLM, its specialised language-model technology.
These developments demonstrate how established information providers are attempting to incorporate generative AI into products already used by professional customers.
Revenue growth does not guarantee higher profitability
Despite higher sales, Thomson Reuters reported a 22 per cent decline in operating profit to $402 million.
The comparison was affected substantially by a gain recorded in the previous year from the disposal of an investment, rather than solely by deterioration in ongoing operations.
Nevertheless, other financial indicators reveal additional pressure.
Adjusted earnings before interest, taxation, depreciation and amortisation increased by 10 per cent to $651 million, but the adjusted margin declined slightly from 35.8 to 35.5 per cent.
Free cash flow fell from $526 million to $408 million.
The company’s legal division also experienced margin pressure associated with higher AI investment.
These figures require careful interpretation.
The decline in reported operating profit cannot be attributed entirely to artificial intelligence expenditure. Changes in investment gains, software amortisation, working capital and taxation also affected the results.
However, the performance indicates that AI development and deployment are becoming important considerations in evaluating the profitability of professional information services.
Why enterprise AI is expensive
The commercial cost of AI extends beyond purchasing access to a language model.
Businesses developing AI products for professional customers must integrate them with existing databases, software environments and workflows.
Legal and accounting applications require particular attention to accuracy, confidentiality, data security and the traceability of generated information.
This creates development and operating costs that may not be immediately recovered through higher subscription fees.
Professional customers must also be convinced that AI products offer improvements over established research and workflow systems.
A technically advanced application has limited commercial value if users do not adopt it or if productivity gains fail to justify its additional cost.
For Thomson Reuters, integrating AI into widely used platforms provides an existing customer base and substantial proprietary information resources.
It does not eliminate the financial challenges of developing, maintaining and supporting new services.
An important change in investor expectations
The market reaction reflects a broader shift in how technology companies are being evaluated.
Investors initially rewarded businesses that announced ambitious artificial intelligence strategies, often placing considerable emphasis on expected future growth.
Attention is increasingly turning towards implementation costs, commercial adoption and measurable financial results.
The market reaction to Thomson Reuters’ results demonstrates that even companies reporting stronger revenue can face pressure when expenditure raises questions about future returns.
The same scrutiny applies to companies outside the technology sector.
Financial institutions, professional services firms and corporate communications businesses are investing in AI tools to automate research, document production, customer support and internal processes.
Each investment creates a requirement to establish whether productivity gains justify licensing, training, integration and oversight costs.
What this means for corporate communications
The financial results carry implications for organisations using AI to manage information and communications.
AI-enabled research and document-processing systems can reduce the time required to retrieve information, prepare initial material and analyse large collections of documents.
However, professional communications also depend on factual accuracy, source verification, confidentiality and editorial judgement.
Organisations adopting these technologies must therefore consider not only the cost of software but also the resources required to supervise its output.
As QuestComms recently examined, AI is changing how businesses distribute information and how audiences discover corporate content.
The technology is also changing the economics of producing and managing that information.
The Thomson Reuters results suggest that enterprise AI is becoming commercially established, but that the relationship between investment, adoption and profitability remains under examination.
For businesses, the relevant question is no longer simply whether AI can perform a particular task.
It is whether the resulting improvements in efficiency, quality or revenue exceed the full cost of deploying and managing the technology.
That distinction will become increasingly important as AI moves from experimental projects into permanent corporate infrastructure.
