It’s Not About the Tech (2 of 4)
How business models, not tools, decide who captures value.
This post is part of a four-part series: Thinking Clearly About AI. The series doesn’t try to predict the future. It looks at patterns from past disruptions and asks better questions about the present. Each post explores one idea.
Technology converges. Business models differentiate.
This is one of the simplest but hardest lessons to absorb. Every major disruption proves it. Yet in the heat of the moment, executives still obsess over tools instead of economics.
History’s Pattern
When MP3s emerged in the late 1990s, every record label had access to the format. The disruption didn’t come from technology. It came from new models: Napster’s peer-to-peer sharing, Apple’s iTunes ecosystem and eventually Spotify’s subscription streaming.
The same story played out in transport. Smartphones, GPS and payment systems were universally available. Uber wasn’t first to the tech. It was first to build the model (dynamic pricing, two-sided networks, asset-light operations) that scaled globally.
Photography too. Kodak literally invented digital cameras. But they clung to film economics. Instagram didn’t win with better lenses. It won by shifting the model: from ownership to social identity, monetised through advertising.
Technology converged. Business models decided winners and losers.
The Temptation Today
I see many leadership teams making the same mistake with AI. Long debates about which LLM is “better.” Committees evaluating vendor A versus vendor B. None of it matters much. Within a few years, performance will converge.
What will matter is how you adapt your model.
Let’s examine three industries where business model shifts are starting to appear: banking, legal services and healthcare.
Case Study: JPMorgan Chase
The old model
Wealth management is a business built on exclusivity. Human advisors manage portfolios for high-net-worth clients. Fees (often 1% of assets under management) make sense when the client has millions. But the model doesn’t scale. The mass market is left out.
What changed
In 2023, JPMorgan announced IndexGPT, a thematic investment tool powered by GPT-4. Clients can build baskets of companies linked to themes like climate tech or cybersecurity. At the same time, JPMorgan rolled out an internal “LLM Suite,” a ChatGPT-like interface for ~50,000 staff in asset and wealth management.
These aren’t gimmicks. They’re steps toward delivering personalised advice (at scale) to clients who never had access to private bankers.
Early results
According to Reuters (May 2025), generative AI tools helped JPMorgan’s asset and wealth division grow sales by 20% year-over-year, even in volatile markets. Analysts credited the AI systems with enabling faster client outreach and tailored recommendations. The bank estimates AI-driven efficiencies across fraud, trading and research have unlocked $1.5 billion in value already.
The model shift
This isn’t about which LLM they picked. It’s about economics:
From scarcity to scale: Advice that was once scarce (limited by human advisor time) becomes abundant.
From margin compression to margin expansion: Technology reduces delivery costs, opening new markets without eroding profitability.
From elite service to mass-market product: Millions of new clients can now be profitably served.
The risk for mid-tier banks is obvious. If your model relies on expensive advisors, AI-assisted competitors will eat your lunch.
Case Study: Legal Services
The old model
For decades, law firms have lived by the billable hour. Productivity was paradoxical: the faster you worked, the less you earned. Efficiency wasn’t rewarded; it was punished.
What changed
In 2022, top firms like Paul Weiss and DLA Piper began experimenting with tools like Harvey (an AI platform built on GPT-4, trained on legal data). These tools draft contracts, review documents, and summarise case law. Dozens of BigLaw firms signed enterprise licenses.
But the most interesting shift came from Fennemore Craig, a 140-year-old US firm. In 2024, it merged with Lucent Law, a small but innovative practice that had pioneered flat-fee, AI-powered document automation. Fennemore launched Project BlueWave, explicitly tying AI adoption to alternative fee models.
Early results
Reuters reported that Fennemore’s flat-fee offerings now account for a growing slice of revenue, attracting mid-market clients who historically avoided top-tier firms because of unpredictable billing. By automating repetitive drafting and contract review, lawyers reclaim hours for high-value strategy. The firm tracks ROI not by hours saved but by whether alternative fees drive client growth.
Meanwhile in the UK, Garfield AI launched AI-backed workflows for tasks like debt collection letters, charging as little as £2 per letter. Approved by courts, these services undercut traditional pricing by 90% or more.
The model shift
The legal sector illustrates the business model problem vividly:
Same tool, different economics: One firm saves hours but bills the same way (margins flat). Another changes pricing (margins expand).
From billing time to billing value: AI forces the question: do we price inputs (hours) or outcomes (results)?
From scarcity to access: Flat-fee and AI-driven workflows bring legal services to clients who were previously priced out.
Case Study: Healthcare
The old model
Healthcare is process-heavy, labour-intensive, and resistant to change. AI has been piloted for years in diagnostics (reading X-rays, MRIs, pathology slides) but adoption has been slow. Tools sit on the edges, never fully embedded into workflows.
What changed
The Mayo Clinic offers a more radical example. Their researchers developed an AI system to detect surgical site infections (SSIs) from patient-submitted photos. Trained on 20,000+ images across nine facilities, the model identifies incisions with 94% accuracy and flags infections with AUC 0.81.
But here’s the key: Mayo didn’t just add a tool. They redesigned postoperative care. Instead of requiring in-person visits, patients submit photos remotely. AI triages cases, routing only risky ones to clinicians.
They’ve also introduced AI-driven “virtual workers” for billing, claims, and coding, cutting administrative bottlenecks. In cardiology, AI flags asymptomatic patients at risk of heart dysfunction, allowing earlier interventions.
Early results
While data is still emerging, early pilots show improved detection speed, reduced unnecessary clinic visits, and faster interventions. For administrators, automation is freeing capacity in billing and compliance.
The financial implications are enormous. US healthcare spending is nearly 18% of GDP. If AI-enabled care pathways shift even 5% of costs, the value unlocked would dwarf most other industries.
The model shift
From reactive to proactive: Detecting infections or dysfunction earlier prevents costly complications.
From centralised to distributed: Care moves from clinics to patients’ homes via digital triage.
From admin-heavy to automated: Virtual AI workers reduce overhead in claims and billing.
Other Emerging Examples
BloombergGPT: trained on financial data and integrated into Bloomberg Terminal. The model isn’t the differentiator: the subscription lock-in is. Bloomberg protected its moat by embedding AI into its economics.
Adobe Firefly: instead of fighting in the open generative AI arena, Adobe embedded Firefly into Creative Cloud. The move isn’t about technology leadership. It’s about defending subscription economics.
Salesforce Einstein: AI embedded into CRM, not as a standalone product, but as a way to justify premium pricing tiers.
These examples show the same pattern: in AI, value capture won’t come from access to models. It will come from embedding them into the economics of how you serve customers.
Why Leaders Miss This
Why do leadership teams default to tech debates? Because they feel safer. You can evaluate vendors. You can issue RFPs. It looks like progress.
Talking about business model change is scarier. It means questioning pricing, cannibalising existing revenue streams and sometimes rewriting how the company makes money but that’s where the value lies.
The Question for You
So here’s the question worth asking: when AI comes up in your boardroom, are you talking about which model to buy or about how your business model needs to evolve?
Because history is clear. Tech converges. Business models decide who thrives and who doesn’t.