Client Success Story

Background

A large biotech manufacturing organization was seeing to accelerate its AI transformation strategy. Executives were seeking to develop an actionable portfolio of AI initiatives that could be delivered and scaled. Leaders across the organization recognized the potential of AI and large language models (LLMs), but needed support in developing impactful use cases. The complexities of manufacturing processes, disparate technologies and siloed data sources made delivering on AI use cases challenging. There was a need for a structured approach that could bring diverse stakeholders together to explore possibilities, align on priorities, and translate ideas into realistic delivery plans.

The goal was to create a shared understanding of where AI could create meaningful value across the enterprise—particularly within manufacturing and operations—while ensuring proposed solutions were grounded in the realities of the organization’s data landscape, technology stack, and AI maturity. We partnered withhte company to facilitate a design thinking approach to encourage creativity, collaboration, and user-centric problem solving, while still enabling disciplined decision-making.

Company size

Revenue

Manufacturing sites

4,000+ employees

$1B+

5+ globally located

The Challenge

Despite strong interest in AI, stakeholders faced several barriers to defining impactful and feasible use cases.

The current data landscape was highly fragmented. A subset of data was available in an enterprise cloud, but the majority of process data was locked away in siloed manufacturing systems.

The organizations's AI capabilities were just beginning to mature, with basic large language models just becoming available for production use. A small subset of large language modes must be used for the initial use cases.

Many ideas were being constrained by existing, human-driven processes. Stakeholders needed support in envisioning how AI could fundamentally change their ways of working and ultimately redesign their workflows for human on or out of the loop workflows.

The stakeholder group had a varying range of technology and AI understanding. A baseline capability showcase needed to be created to ensure the group could effectively design AI use cases. Ideas risked being overly aspirational or not scalable without a shared understanding of the current data environment and the available AI technologies, The organization needed a way to balance “blue sky” innovation with practical feasibility. Ultimately prioritizing initiatives based on value and effort to build credible business cases leadership could confidently support.

Timeframe

Scope

Deliverables

4 months

Comprehensive digital maturity assessment across 5 + sites

• Executive readouts

• Detailed scorecards

• Recommendations

Our approach

We designed and facilitated a design thinking–workshop centered on a series of immersive, multi-day sessions that brought together business leaders, technology teams, and manufacturing operations stakeholders. The approach intentionally began with a blue sky ideation phase, encouraging participants to step away from current processes and imagine future-state scenarios. In these scenarios, AI acted as a core enabler of decision-making, insight generation, and operational execution.

Using design thinking methods such as problem reframing and future-state journey mapping, we generated more than 15 distinct AI use cases. The use cases spanned manufacturing, operations, analytics, and decision support. We then guided the group through a structured convergence and prioritization process, assessing each use case against criteria including business value, strategic alignment, technical feasibility, data availability, and suitability for current AI models.

To move from concepts to action, we worked with stakeholders to refine top-priority use cases into clearly defined problem statements, value hypotheses, and high-level solution outlines. This included identifying data sources, integration needs, and implementation considerations. The result was a prioritized, delivery-ready AI use case roadmap—grounded in real business value, informed by technical realities, and aligned across the functional teams. The roadmap enabled the organization and executive leadership to confidently progress from ideation to execution.

Assessment approach

Framework

Phases

Hybrid: In person and virtual assessments

Biotech industry standard assessment tailored to meet the client needs

• Site preparation

• Assessment

• Executive readouts

Key Client Successes

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