From Use Case to Business Case: What an AI-First Strategy Really Requires

Posted On May 29, 2025

The phrase “AI-first strategy” has started showing up in corporate vision statements, annual reports and many slide decks being presented in industry forums. Invoked as a signal of tech ambition, execs and speakers refer to intelligent automation, predictive systems, and decision-making at scale. Yet, for most organisations, these aspirations have rarely extended beyond a collection of disconnected pilots. Maybe a prototype that summarises documents, a chatbot for answering the most inane of queries or may be a data dashboard that analyses some unclean data. Each demonstrates capability, but few deliver sustained value.

Companies are definitely experimenting with AI. But, how many of experiments are translating into operating advantage. IBM’s 2023 Global AI Adoption Index says, while 77% of enterprises report some form of AI use, only 35 percent have implemented it at scale. The numbers reflect a deeper problem: most enterprises are still optimising around use cases, and solving isolated problems, not building towards an AI-first strategy.

The difference is basic; A use case focuses on what is possible; an AI-first strategy defines what is necessary for competitive relevance. It is about discerned choices around data architecture, governance models, talent structure, operating models and business alignment. AI then will form an infrastructure for decision-making, rather than a series of projects. This article, in 3nayan’s series on AI, examines that reframing and redesigning how the enterprise works.

The ‘Use Case’ Trap

The early stages of AI adoption, not different from the nascent Digital Transformation era, often follow a predictable script. A team is put in place to identify a discrete pain point, e.g. predicting customer churn, automating invoice processing, tagging support tickets. A tech partner or internal data science group is designs a solution. A pilot ensues, often with encouraging results. And then, progress halts.

This is the use case trap: a cycle of experimentation that rarely matures into transformation. Use cases are appealing because they are bounded, manageable, and demonstrable. They give stakeholders something to point to. Each pilot is inherently optimised for local success, thus giving stakeholders a flag to wave. But, these are isolated proof points, disconnected from broader systems and business goals.

A 2023 Capgemini Research Institute report, on Gen AI, found that although 59% of large enterprises had implemented at least five AI use cases, only 16% reported any of them being scaled successfully. These pilots succeed, but get shelved because the organisation lacked the operational infrastructure, or the strategic clarity, to scale them.

Some more web-based research shows, the recurring failure patterns:

• Ownership is ambiguous. Once the pilot ends, no single team takes responsibility for ongoing integration, retraining, or monitoring.
• Success is defined too narrowly. Model accuracy becomes the metric, instead of process improvement, cost reduction, or customer impact.
• Data is not enterprise-ready. Use cases often depend on subset and modified datasets that do not reflect enterprise variability, making production rollout challenging.
• Architecture is absent. There is no modified Enterprise Architecture which takes this pilot or its proliferation accounted for, no common platform, no MLOps pipeline, and no governance to industrialise what has been built.

These are strategic oversights, rather than technical ones. AI in these situations remains a side experiment. If it were to be pursued as an operating principle, it would demand a different level of coherence.

Telefónica Example:
Telefónica, the Spanish multinational telecom operator, launched several AI initiatives across its business — including pilots in network optimisation, customer analytics, and fraud detection. However, the company encountered early challenges in scaling these solutions due to fragmented infrastructure, inconsistent data governance, and siloed delivery teams. Recognising the structural nature of these barriers, Telefónica responded by consolidating its AI and data capabilities under LUCA, a dedicated unit focused on enterprise-wide AI solutions. More recently, through Telefónica Tech’s collaboration with IBM on SHARK.X — a hybrid, multi-cloud AI platform — the company has sought to industrialise AI development and deployment across its ecosystem. This transition illustrates the broader point: isolated pilots rarely scale without deliberate architectural and strategic alignment.

The lesson, then, is : Demonstrating feasibility is not the same as delivering value. A successful AI-first strategy requires moving beyond pilots toward deliberate, integrated design.

The Business Case Mindset

In many organisations, AI projects get initiated without a structured rationale for their contribution to business performance. The emphasis is often on showing that a particular problem can be solved with AI, rather than whether it should be done at all, and for what purpose. This misalignment between technical deployment and commercial outcome is one of the main reasons why AI efforts fail to scale.

A business case for AI must have a material and measurable impact, even if derived, on an enterprise level outcome or KPI (which can really be only revenue, profit, business agility, customer retention and community connect). The business case needs to support the pilot, as well as its proliferation later. Creation of such a business case needs the presence of production ready enterprise data, available architecture understanding, and ownership of the pilot and transformation. This, in turn, demands more rigour in defining the problem domain, the economics of the intervention, and parameters of acceptable performance.

Example – Unilever:
Unilever used AI to streamline its talent acquisition process. By analysing CVs, matching candidates to roles, and deploying automated video interview assessments with sentiment analysis, the company was able to reduce hiring time from 4 weeks to just 2, while improving candidate satisfaction. But what made this successful was not the model alone — it was the integration into end-to-end hiring workflows, stakeholder alignment, and governance on fairness and transparency. The AI was embedded in process, not bolted onto it.

Organisations that take the time to construct robust business cases create the conditions for scale. They are better positioned to prioritise AI investments, to assign ownership, and to create feedback loops between business and technology. Without that structure, AI will remain peripheral and a set of isolated efforts that never mature into strategic advantage.

What ‘AI-First Strategy’ Actually Entails

An AI-first strategy is often misunderstood as simply scaling pilots or increasing model deployments. In practice, it refers to a shift in how organisations design their systems and structure their operations to create value with AI at the core. This shift is architectural, operational as well as managerial.

The foundation lies in treating data, models, and decisions as connected assets. This means designing in-line AI workflows (rather than layered on top). This also means ensuring that models are developed, and embedded into decision points (e.g. pricing engines, fraud detection modules, or supply chain platforms).

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AI-first Strategy, or even related thinking also changes how delivery happens. In conventional models, AI is often treated as a solution developed externally to the business function, then handed off for use. This results in poor adoption, limited feedback loops, and weak accountability. In contrast, true AI-first organisations embed model deployment directly within operational teams, where its outputs influence day-to-day decisions. The focus, then, shifts from proof of concept to reliability, version control, and system resilience under real-world conditions.

Example – Volvo Group:
Volvo integrated AI into its quality assurance process on production lines to detect defects using vision systems. The AI was not implemented as a standalone tool but configured to work alongside existing manufacturing controls and reporting systems. This allowed real-time feedback to production staff, reduced manual inspection, and improved defect detection rates. AI became part of the process logic, not a bolt-on experiment.

This type of execution requires proactive and embedded governance. AI-first companies establish controls for model transparency, performance tracking, and revalidation. Not just in regulated industries, but also retail and logistics companies now routinely monitor for model drift, decision errors, and fairness indicators, especially when AI directly affects customer-facing actions or employee processes.

For these organisations, AI is designed into the workflow. It shapes systems response, resources allocation, and exception handling. This degree of ’embeddedness’ leads to AI transitions to infrastructure, and from being a cost centre to a performance driver.

What Strategic Scaffoldings Must Be in Place

Organisations that scale AI successfully rely on specific structural enablers. These elements provide the scaffolding needed to support implementation, align ownership, and ensure long-term value delivery.

• First, a unified data strategy is foundational. AI-first execution depends on production-grade data that is accessible, governed, and reliable. Maersk addressed this by building shared platforms that enable real-time logistics visibility, allowing predictive models to operate across its global network.
• Second, AI ownership must be clearly defined. AI-first firms adopt delivery models where central teams set guardrails, while business units own implementation. Standard Chartered created a central AI hub for governance and tooling, with regional teams responsible for outcomes.
• Third, success metrics must reflect business value. Many initiatives fail because performance is measured technically, not commercially. Rolls-Royce structured its predictive maintenance program around engine uptime and reduced unplanned servicing, not model accuracy, thus tying impact directly to operational efficiency.
• Fourth, funding must support continuity. AI programs often require phased investment and extended runway, thus budget cycles or project approval based interruptions must be considered early. Some firms have introduced dedicated innovation budgets linked to delivery milestones, enabling smoother transitions from pilot to scale.
• Fifth, change management must be built in. AI changes how work is done, not just what tools are used. Without redesigning roles, KPIs, and workflows, adoption falters. Schneider Electric embedded AI into its building energy systems and simultaneously redesigned manager dashboards and reporting structures to reflect AI-driven decision inputs.

Each of these scaffolding elements is individually important, but they are most effective when aligned, and can be iterated. Together, they create the conditions under which AI can be institutionalised — not as a project, but as a core operational capability.

Measuring the Real Impact

A consistent challenge in AI programmes is the misalignment between the measured and what matters. Model performance metrics, while necessary during development, aren’t sufficient to demonstrate value to the business.

For an AI-first strategy to hold credibility, impact must be measured in operational and financial terms. Easy examples could be – in customer operations, reduced average handling time, improved resolution rates, or higher customer satisfaction scores. In supply chains, improvements in forecast accuracy, fewer stockouts, inventory turnover, or reduced shrinkage. The metric must match the mechanism through which AI is expected to deliver value.

Example – Walmart:
Walmart used AI to manage shelf inventory in real time, reducing stockouts by up to 30 percent. The gains were not just technical — they translated into improved sales continuity and better supplier alignment.

Measuring impact also means tracking how AI systems perform over time. Organisations must monitor for model degradation, unintended bias, and shifts in data patterns. This requires logging operational performance and integrating AI metrics into the same dashboards used for business KPIs. When models are updated or retrained, changes must be transparent and attributable.

Finally, the direct costs of development, deployment, and monitoring must be weighed against savings or gains through a cost benefit analysis. In some industries, this could be avoided risk or improved compliance and in others, it could be better throughput or reduced headcount. Without these financial checks and balances, AI risks becoming a technical cost centre over time.

Organisations must understand that AI is infrastructure, and part of workflow. Similarly, its metrics need to be embedded into the rhythm of operations as well. This discipline separates experimentation from enterprise value.

The Long Road from Demos to Money

Most AI efforts hit terrain not due to lack of innovation, but because they remain disconnected from business value. Scaling impact requires structural readiness.

An AI-first strategy embeds AI into core workflows, with clear ownership, reliable data, measurable outcomes, and sustained investment. Without this framework, the pilots do not scale. The differentiator is not volume of use cases, but clarity of business case. AI becomes meaningful when it stops being an initiative and starts becoming infrastructure.

Note: The data points, and industry examples have been AI researched and collated based on available data and information from the Web