Most AI programs don’t fail in the lab. They fail in the handoff: the moment a promising pilot meets procurement, legacy integration, audit requirements, and the day-two reality of monitoring and ownership. The fastest way to move “from pilot to production” is to treat scaling as a business system design problem, not a model-selection problem—so the initiative ships with an operating model, evidence trail, and risk controls that do not suffocate delivery.
Strategy before stack: Stop funding “models,” start funding capability
In most organizations, the term “AI strategy” is often reduced to a list of use cases. That sounds actionable, but it usually turns the portfolio into a graveyard of pilots: isolated proofs of concept that cannot survive real constraints (data rights, latency, controls, change management) once the demo glow fades. A more effective strategy describes how the organization intends to leverage AI, and what kinds of AI it is willing to operationalize—because autonomy, learning dynamics, and limited interpretability alter the strategic design space compared to prior IT waves.
Hofmann and colleagues map AI strategy as a taxonomy rather than a slogan, which helps leaders avoid a common trap: trying to scale everything with the same operating assumptions. Once you accept that strategies cluster (e.g., efficiency-driven automation vs. augmentation vs. new AI-native offerings), it becomes easier to assign the proper success criteria, governance burden, and technical architecture to each stream—especially in regulated environments where the “cheapest” architecture can be the most expensive once controls, evidence, and remediation are priced in.
Value is not “ROI later”: Value is a mechanism you can design for
If ROI is treated as a future outcome, teams optimize for near-term model performance, hoping that business value will emerge downstream. Research on AI business value suggests that value emerges through mechanisms such as process redesign, complementary assets, and organizational capabilities—not from algorithms in isolation.
This distinction matters when you scale. A pilot can succeed with heroic effort, bespoke data pulls, and informal approvals. Production requires repeatable mechanisms, including stable data supply, ownership, and decision-making rights. Without those, you get an expensive paradox—high AI activity with low business impact.
Capability beats brilliance: What interviews about AI implementation keep revealing.
Weber and co-authors studied AI implementation through expert interviews and distilled four organizational capabilities that repeatedly separate scalable programs from perpetual experimentation: structured AI project planning, co-development with stakeholders, data management, and lifecycle management for models that must evolve as data and environments change.
Notice what’s missing: “hire better data scientists.” Skills matter, yet scale collapses more often because the organization cannot plan work under uncertainty (inscrutability), cannot maintain stable data to trust, and cannot sustain the lifecycle once models reach production. In regulated industries, this becomes sharper: the same capability gaps create not only delivery risk but compliance exposure, because the organization cannot reliably explain provenance, controls, and operational decisions.
The point of Figure 1 is not to enumerate chores. It is to demonstrate that scale fails at predictable breakpoints, and each breakpoint is a capability issue, not an algorithmic problem.
MLOps is the production contract, not a toolchain
The MLOps label is often sold as tooling. In practice, it’s a contract between teams: what gets versioned, validated, deployed, monitored, and rolled back—and who owns each step when something changes. Kreuzberger, Kühl, and Hirschl frame MLOps as a holistic practice with principles, roles, and architecture precisely because many ML efforts fail to operationalize and automate the path from development to dependable operation.
John, Holmström Olsson, and Bosch take it a step further by tying MLOps adoption to specific activities and maturity stages, arguing that organizations evolve from ad-hoc model deployment toward structured, continuous development supported by explicit practices and governance. Their work is helpful for leaders because it legitimizes the uncomfortable truth: you cannot “buy” maturity; you build it through repeatable work and institutional learning.
A recent systematic review by Zarour and colleagues synthesizes recurring challenges and maturity models across MLOps literature and finds that the friction points are consistent: lack of standardized practices, difficulty maintaining consistency at scale, and ambiguity in judging maturity. For executives: If your organization cannot describe its MLOps maturity in operational terms, it is unlikely to scale AI predictably—no matter how many platforms it licenses.
Governance that enables throughput: Risk controls designed for delivery speed
In regulated sectors, governance is routinely blamed for slow delivery. The deeper issue is design: governance that arrives late behaves like a brake; governance designed into the lifecycle behaves like a steering system. The NIST AI Risk Management Framework structures risk management work across governance, context mapping, measurement, and ongoing management, and explicitly anchors accountability with senior leadership and organizational decision-makers.
Papagiannidis, Mikalef, and Conboy synthesize responsible AI governance as a set of structural, relational, and procedural practices—moving the conversation beyond abstract principles toward operationalization across the AI lifecycle. This framing is constructive when you need to achieve ROI under constraints, as it supports building controls that are repeatable, reviewable, and compatible with continuous delivery, rather than relying on “one-time paperwork.”
ROI that survives scrutiny: Build an evidence chain, not a narrative
Many leadership teams ask for ROI but accept a story: “The model will save money once adopted.” In a regulated enterprise, that story collapses under audit, model drift, and process complexity. The stronger approach treats ROI as an evidence chain that links the deployed system to a measurable decision outcome, ensuring traceability and transparency.
Finance research offers a proper anchor because it forces specificity. Fraisse and Laporte analyze ROI for an AI use case tied to bank capital requirements—an environment where value is inseparable from rules, constraints, and measurable financial impact. Even if your domain differs, the lesson travels: ROI claims become credible when they are connected to a constrained decision system with explicit baselines and observable outcomes.
At the firm level, evidence on the productivity effects of AI adoption is increasingly empirical. For example, Czarnitzki, Fernández, and Rammer estimate firm-level productivity effects using survey data and report positive associations between AI use and productivity in their sample, while addressing endogeneity concerns with IV approaches. This does not promise an automatic ROI; it supports the more nuanced proposition that organizational adoption—when done well—can translate into measurable performance, which is the justification for investing in capability rather than perpetual piloting. Figure 3 reframes ROI as something engineered.
The more regulated the environment, the more ROI depends on the integrity of the evidence layer, because “value” that cannot be defended often cannot be retained.
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