Erosion risks

• Data privacy/localisation rules reduce data availability or cross-border linkage
• Data breach or model failures reduce trust and trigger churn
• Large platforms/banks develop in-house decisioning stacks using cloud ML and first-party data

Leading indicators

• Digital Identity Network transaction volumes (daily/annual)
• Renewal/retention rates in Business Services and Insurance verticals
• Regulatory actions affecting data sharing (privacy, AML/KYC)

Counterarguments

• Other credit bureaus and risk vendors also have large datasets and model capabilities
• Some customers can multi-source data and commoditise decisioning logic

Erosion risks

• API standardisation makes vendor swaps easier
• Procurement pressure forces unbundling of platforms into commodity data feeds
• Model/feature parity reduces differentiation over time

Leading indicators

• Net revenue retention / expansion within top accounts
• Platform adoption metrics (e.g., platform modules per customer)
• Implementation times and integration depth with core systems

Counterarguments

• Some buyers prefer best-of-breed point solutions and integrate them via modern data stacks
• Large insurers/banks can negotiate and switch vendors when contracts renew

Erosion risks

• Open access mandates and funding models compress publisher economics
• Library consortia push back on pricing ('big deal' renegotiations/cancellations)
• Preprints and alternative dissemination reduce reliance on traditional journals

Leading indicators

• Renewal rates and pricing outcomes in large institutional contracts
• Share of revenue from open access publishing vs subscriptions
• Submission volumes and acceptance capacity/quality metrics

Counterarguments

• Authors and institutions can shift to open platforms or alternative publishers over time
• Publishing workflows are contestable; editorial boards can migrate journals

Erosion risks

• Standards-based interoperability reduces integration advantage
• Institutional preference for open bibliometrics/analytics alternatives
• Data quality challenges or bias concerns reduce trust in analytics outputs

Leading indicators

• Adoption and renewal of Scopus/SciVal/Pure contracts
• Product usage/engagement (searches, API calls, seats)
• Competitive displacement in research management systems

Counterarguments

• Institutions can use alternative bibliographic datasets and open analytics
• Multi-homing is common: customers may keep multiple databases/tools

Erosion risks

• Courts/governments improve free access and APIs for primary law
• Litigation/regulation around AI outputs, privacy, or content licensing
• Content parity narrows if competitors secure similar primary/secondary rights

Leading indicators

• Content acquisition/renewal and exclusivity outcomes by jurisdiction
• Quality metrics for AI answers (hallucination rate, citation accuracy)
• Share of usage moving to non-proprietary sources

Counterarguments

• Primary law is increasingly available for free; value shifts to workflow and analytics
• Competing vendors also have deep content libraries and can integrate with AI tools

Erosion risks

• Law firms adopt multi-platform AI copilots that abstract away the underlying research vendor
• Interoperability standards make switching easier across document management and citation tools
• New entrants bundle research + drafting at lower price points

Leading indicators

• Lexis+AI and assistant (Protege) adoption rates by customer segment
• Seat growth and module attachment (research, drafting, analytics)
• Churn/renewal outcomes among top 100 law firms

Counterarguments

• Large firms often subscribe to multiple research platforms (multi-homing)
• Procurement leverage at large firms can cap price increases