In March 2026, Roland Berger published "Crafting tomorrow: how shared capabilities drive AI-first organizations."1 Its argument cuts against how most companies are deploying AI. The winners, it contends, will not be the ones with the most pilots — they will be the ones who stop treating each AI use case as a bespoke project and instead build a small set of shared, reusable capabilities (pricing, reporting, decisioning) that every team can call through a common API, rather than rebuild from scratch. The study's blunt evidence: 60% of companies scaling below their industry average run no platform at all.1
The frame is a platform operating model organized in layers: a core technology platform (data, compute, models), a business capability platform (the reusable engines), and a customer journey platform that orchestrates those engines into end-to-end experiences. Cutting across all of them is a security platform that governs and logs every AI decision. The load-bearing idea is reuse over rebuild:
Read that as an economics claim, not a technology one. A capability built once and reused ten times is governed once, audited once, and improved once — and every journey that calls it inherits those properties for free. A capability rebuilt ten times is ten separate security reviews, ten audit gaps, and ten places value goes unmeasured. The rest of this piece is about why that distinction decides whether AI scales or stalls — and what it takes to build capabilities that are genuinely safe to reuse.
The evidenceReuse is the difference between scaling and stalling
The case for shared capabilities is, at bottom, the case against the pilot graveyard — and the analyst record on both the graveyard and the platform cure is now hard to ignore:
The pattern is consistent: the constraint on enterprise AI is rarely the model. It is whether the organization can reuse what works, prove the value, and control the risk — and all three are properties of the platform around the model, not the model itself. And the highest-leverage capability to get right is pricing: McKinsey's classic finding is that a 1% improvement in price lifts operating profit by roughly 8%, more than any comparable move on volume or cost3 — exactly why Roland Berger's worked example is "pricing-as-a-service." The capability you least want every team reinventing is the one with the most money attached. Which is also where the stakes of reuse rise sharply.
The riskA shared capability shares its blast radius
A reusable capability is a force multiplier in both directions. A pricing engine called by every renewal, quote and journey is enormously efficient — and if it can be steered, it is a single point of failure for revenue. The moment a capability touches money or identity — a price, a credit limit, a customer record — its reuse is also a reuse of its risk. The security field has been blunt about what that demands.
OWASP's Top 10 for LLM Applications (2025) ranks prompt injection as LLM01 — the number-one risk, defined as input that "alter[s] the LLM's behavior or output in unintended ways," including content that "need not be human-visible/readable, as long as the content is parsed by the model."6 For a pricing capability, that "input" is a customer note, a deal-context memo, a CRM free-text field — any of which a motivated actor could craft to nudge a quote. The rule is unambiguous: untrusted input is data, never instructions.
OWASP's companion risk, Excessive Agency (LLM06), governs the other half — how much a shared capability is allowed to do on its own. Its recommended mitigation:
This is the consensus of the application-security community, extended further in OWASP's dedicated Top 10 for Agentic Applications (December 2025), whose mitigations centre on least privilege, sandboxing, and a human in the loop for critical decisions.7 A reusable pricing capability should recommend and prepare; it should never be the thing that commits the price. For regulated operations, that is no longer just good design. It is law.
The lawThe security platform is a legal requirement, not a nicety
Roland Berger draws a security platform across every layer for a strategic reason. Regulators arrive at the same place from a legal one. The EU AI Act explicitly lists AI that evaluates "the creditworthiness of natural persons or establish[es] their credit score" as high-risk8 — and credit, pricing and customer decisioning are precisely the capabilities a platform wants to share. Two obligations then land on any reusable decisioning engine:
- Human oversight (Article 14). High-risk systems must be designed so they "can be effectively overseen by natural persons," who can "disregard, override or reverse the output" and "interrupt the system through a 'stop' button" — while the design guards against automation bias.8
- Record-keeping (Article 12). High-risk systems "shall technically allow for the automatic recording of events (logs) over the lifetime of the system" to ensure traceability.8 High-risk obligations apply from August 2026.
The U.S. NIST AI Risk Management Framework reaches the same destination from a different direction: its four functions are Govern, Map, Measure, Manage, with Govern described as "a cross-cutting function that is infused throughout AI risk management."9 "Cross-cutting" is exactly Roland Berger's security platform: governance is not a layer you bolt on after the capability works — it is the condition under which the capability is allowed to be shared at all. And a logged, signed decision per call is what makes one engine serving many journeys auditable instead of a black box.
There is a sovereignty dimension too. Cisco's 2025 Data Privacy Benchmark Study found 90% of organizations believe local storage of data is inherently safer, and 64% worry about inadvertently sharing sensitive information with AI systems.10 A shared pricing or reporting capability reads your most sensitive data — contracts, margins, customer records. "Where does it run" decides whether you are permitted to centralize it at all.
The synthesisA reusable capability is a platform object, not a feature
Put the thesis and the constraints together and the design falls out. For a capability to be genuinely safe to reuse, the platform — not each calling team — has to guarantee a handful of properties on every invocation. Build them into the capability once and every journey inherits them; leave them to the caller and you are back to ten security reviews and the Gartner graveyard. Four non-negotiables apply to every capability flow8 runs:
And it runs self-hosted — on-premise, private cloud or air-gapped — so the data a shared capability reads never crosses a boundary you don't own, answering the sovereignty concern Cisco quantifies.
flow8 in practiceThe platform thesis, running as governed flows
We built Roland Berger's platform layers as concrete flow8 flows. Three are reusable business capabilities; one is a customer-journey orchestrator that calls them rather than reimplementing them. All write into one value bus — a signed actions ledger — so the whole platform rolls up to a single, human-reviewed view. The architecture, not the prose:
capabilities catalog, one signed ledger — never forked per team.actions ledger. Each prepares and recommends; nothing touches money or identity without a human.actions
The journey orchestrator never recomputes a price or a report. It looks the capability up in the registry, calls it in dry-run mode, assembles a prepared draft, and hands the customer-facing action to a human. That is the platform thesis in one sentence: journeys orchestrate; capabilities execute; humans approve; the ledger remembers.
The takeawayBuild the capability once, govern it once, reuse it everywhere
Roland Berger is right that platform power — shared, reusable capabilities — is what separates organizations that scale AI from those that drown in pilots. But the half of the story that decides whether it works is the unglamorous half: a money capability that only ever prepares, an input that is always treated as hostile, a single signed ledger instead of a fork per team, and value measured per capability against a baseline — all on infrastructure you own. Get that governed core right and each new capability is a fast, safe addition the whole organization inherits. Get it wrong and "shared capability" just means you have centralized the blast radius.
Turn your highest-leverage capability into a platform.
flow8 is the platform for running reusable AI capabilities in a standardized, secure, governed way — prepare-only on money, a human on every high-consequence decision, one signed ledger, on infrastructure you own.
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- Roland Berger, "Crafting tomorrow: how shared capabilities drive AI-first organizations," March 2026. rolandberger.com
- Gartner, "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027," press release, June 25, 2025. gartner.com
- M. Marn, E. Roegner, C. Zawada, "The power of pricing," McKinsey Quarterly, Feb 2003 (a 1% price rise lifts operating profit ~8% for the average S&P 1500 company, volumes held constant). mckinsey.com
- Gartner, "Top Strategic Technology Trends for 2024" — platform engineering (by 2026, 80% of software-engineering organizations will establish platform teams, up from 45% in 2022), Oct 2023. gartner.com
- McKinsey (QuantumBlack), "Demystifying data mesh" (a company developed use cases ~7× faster after shifting to reusable data products). mckinsey.com
- OWASP, "LLM01:2025 Prompt Injection," OWASP Top 10 for LLM Applications 2025. genai.owasp.org
- OWASP GenAI Security Project, "Top 10 for Agentic Applications" (released Dec 9, 2025). genai.owasp.org
- EU AI Act — Article 14 (Human Oversight), Article 12 (Record-keeping), Annex III §5(b). artificialintelligenceact.eu/article/14 · article/12
- NIST, "Artificial Intelligence Risk Management Framework (AI RMF 1.0)," NIST AI 100-1, Jan 2023. nist.gov
- Cisco, "2025 Data Privacy Benchmark Study," Apr 2, 2025. newsroom.cisco.com