Andreas Timm

Tag: regulated industries

  • Vertical AI is winning the deployment race

    Vertical AI is winning the deployment race

    Horizontal AI slab at the bottom with three taller vertical columns rising from it labeled by domain
    Horizontal is the substrate. Vertical is the value layer.

    Gartner’s April read says eighty percent of enterprises will have adopted at least one vertical AI agent by year-end, and thirty percent of all enterprise AI deployments will be vertical-specific. Bessemer’s vertical AI report from this month is even more direct: vertical AI companies founded after 2019 are reaching eighty percent of traditional SaaS contract values while growing four hundred percent year-over-year. This is not a minor adjustment to the deployment landscape. It is a structural redirection of where the value of agentic AI accrues.

    For boards in 2026, the implication is that the right framework for thinking about AI vendor strategy is no longer horizontal-versus-vertical. It is which verticals you bet on, and how early. Deployment speed defines advantage in this cycle, and the deployment race is now a vertical-by-vertical race.

    The shift: vertical specialization beats horizontal generality at the workflow layer

    Horizontal AI tools — the chat assistants, the general-purpose copilots, the broad productivity overlays — are still the largest category by usage. They are not the largest category by enterprise value. The reason is structural. A horizontal copilot is good at fifty things. A vertical agent is excellent at five things that are deeply embedded in a specific workflow.

    When the enterprise needs to extract value, depth wins over breadth. Abridge in clinical documentation. Harvey and EvenUp in legal. Hebbia in financial research. Specialized clinical-coding agents at major payers. The vertical players ship integrations into existing systems, understand the regulatory and accuracy constraints of the domain, and deliver outcomes that horizontal tools cannot match without significant configuration effort that customers refuse to undertake.

    The defensibility of vertical players is also higher than the market priced in 2024. The data flywheel inside a regulated vertical is genuinely hard to replicate. The customer relationships are stickier because switching costs include re-credentialing within the regulator’s expectations, not just re-implementing software.

    Two rectangle shapes side by side, one wide and shallow, the other narrow and deep
    Wide-shallow loses to narrow-deep at the workflow level.

    The role change is the chief AI buyer becomes a portfolio manager

    Inside enterprises, the executive responsible for AI vendor strategy is increasingly running a portfolio of vertical specialists alongside the foundation-model contracts. The horizontal tools form a substrate. The vertical agents form the high-value layer. The portfolio manager has to balance ROI realization against integration overhead, and has to decide which verticals to deepen versus which to defer.

    The skill set for this role is closer to portfolio investment management than to traditional procurement or IT leadership. The portfolio manager has to read product roadmaps, anticipate vendor consolidation, manage concentration risk, and time entry into emerging verticals where category leaders have not yet emerged. None of this is in the standard procurement or CIO playbook.

    Most large enterprises have not formally structured this role yet. The work is happening inside the CIO function or inside individual line-of-business AI initiatives, with no portfolio-level coordination. The result is double-procurement of overlapping vertical capability and missed early-mover advantage in verticals where the category leader will not stay reasonably priced for long.

    The strategic consequence reshapes acquisition strategy

    For enterprises in regulated industries — banks, insurers, hospital systems, large law firms, accounting firms — the vertical-AI thesis has a direct M&A implication. The category leaders in each vertical are trading at premium multiples now and will trade at higher multiples by 2027 once their data flywheels and customer concentrations are visible in audited financials. The window for acquisition at reasonable multiples is open in 2026 for most verticals. It will close.

    For incumbents who do not acquire, the implication is partnership at scale. The vertical specialists need distribution that incumbents already have. The incumbents need capability that the specialists already have. The deal terms will tilt toward the specialists as their growth rates remain visible. Incumbents that delay partnership decisions to 2027 will pay more for less favorable terms.

    For boards governing AI strategy, the directive question is whether the company is buying or building or partnering for vertical AI capability — and whether that decision is being made deliberately for each vertical, or by default by the absence of a decision. Default-by-absence is the mode most large enterprises are operating in. It is the most expensive mode.

    Four labeled doors in a corporate hallway with one chosen and three closed
    Per vertical: buy, partner, build, or wait — pick deliberately.

    So what boards should do this quarter

    Map the AI vendor portfolio with horizontal versus vertical breakdown. If the breakdown is more than two-thirds horizontal, the company is missing the value-creating layer. If it is unmapped, that is a more urgent finding.

    Designate an executive owner for vertical AI portfolio strategy with explicit authority across line-of-business silos. The decisions are too consequential to be made silo by silo. The horizontal-tool decisions can stay with the CIO. The vertical-agent decisions need a portfolio view.

    For each major vertical relevant to the business, assign a clear posture: acquire, partner, build, or wait. Defaulting to wait by not deciding is the same as deciding to wait — and in most verticals it is the wrong decision in 2026. Execution speed will separate leaders from followers in this cycle.

  • Davos 2026 made AI sovereignty the policy line — and the corporate one

    Davos 2026 made AI sovereignty the policy line — and the corporate one

    What was announced

    The World Economic Forum 2026 met in Davos January 19–23 with AI as the dominant agenda item. The conversation converged on three themes: risk-proportionate governance, runtime governance for multi-agent systems, and what Microsoft CEO Satya Nadella framed as “corporate AI sovereignty” — firms owning the intelligence layer that encodes their distinctive capabilities. Anthropic CEO Dario Amodei warned the forum that frontier AI is uniquely well-suited to autocracy, calling for targeted chip-export controls. A WEF press release on the same week reported leading organizations are shifting from “potential” to “performance” — measuring AI by realized output rather than pilot count.

    What it means

    The vocabulary shift is the substantive event. For two years, AI policy discussion at this forum was framed as risk management — what to restrict, what to monitor, what to ban. The 2026 framing is different. It treats AI as critical infrastructure where the governance question is who owns it, not whether it should exist. “Sovereignty” applied to AI is a deliberate echo of “data sovereignty” — a recognition that the layer of intelligence inside an organization is becoming as load-bearing as its data layer was a decade ago.

    For governments, this redirects policy from rule-writing to capability-building: domestic compute, domestic foundation models, controlled exports. For corporations, it redirects strategy from procurement to capability ownership: which models do you fine-tune yourself, which workflows encode your tacit knowledge, and which partners do you let inside the trust boundary. Both translations point to the same architectural question: where does the irreducible cognitive core of your organization live, and who can take it from you.

    Andreas’s view

    My read on this: Davos is a leading indicator of where C-suite vocabulary moves over the next 12 months. “Corporate AI sovereignty” is not a slogan — it is a framing that makes specific decisions easier to defend in a board meeting. Building your own model fine-tunes is sovereignty. Choosing not to send your customer interactions through a third-party model API is sovereignty. Maintaining a private inference cluster is sovereignty. The vocabulary justifies budgets that previously read as duplicative or paranoid.

    I don’t think the sovereignty framing is purely defensive. There is a competitive argument inside it: organizations that operate as pure consumers of frontier models are paying rent on the cognitive layer of their own business. Organizations that operate as owner-operators of a fine-tuned, workflow-embedded intelligence layer pay less rent and accumulate a moat that compounds with their data. The Davos talking points are starting to reflect that distinction.

    The way I see it, the question that matters this quarter is not “what is our AI strategy” but “what would it take to lose access to our primary model provider, and what would happen to the business if we did.” If the answer is catastrophic, the sovereignty argument is operational, not philosophical, and it has a budget implication.

    Three things I’m watching

    1. I’ll be watching whether companies run model-dependency stress tests — simulating the operational impact of losing their primary frontier-model provider for 30, 90, and 180 days. The result is the size of their sovereignty problem, and whether they even know that number tells me a lot.
    2. The companies that preserve strategic optionality will be the ones that draw a clear line between work requiring owned cognition (fine-tuned, embedded, internal) and work that can run on rented cognition (API-served frontier models) — and treat that boundary as a capital decision, not a procurement decision.
    3. I’ll be watching how the policy direction develops across major operating jurisdictions. Sovereignty framing in Davos has a consistent track record of translating into sovereignty requirements in regulated industries within 12–24 months.

    References and related signals