EMUG Tech’s AI strategy and use-case consulting covers the complete pre-investment phase of an AI programme: AI readiness assessment across data, infrastructure, and organisational capability; structured use-case identification workshops with function leads; ROI scoring and business case development for prioritised use cases; AI solution architecture and build-vs-buy analysis; 12 to 24 month phased delivery roadmap; data strategy and governance gap analysis; EU AI Act risk classification; and proof-of-value planning and execution. The NORTH Framework ensures every AI investment decision is grounded in evidence — not in vendor marketing or analyst projections — before a single line of model development code is written.

EMUG NORTH is the five-phase AI strategy methodology: Navigate — AI readiness assessment across data, infrastructure, and organisational capability; Outline — structured use-case identification and ROI scoring; Route — AI solution architecture and roadmap design; Test — proof-of-value validation for prioritised use cases; Harvest — production deployment planning and value realisation tracking. NORTH addresses the most common AI failure mode: organisations committing to AI implementation before they have validated use cases, assessed data readiness, designed integration architecture, or confirmed proof-of-value. NORTH-led strategy engagements identify an average of 12 to 18 viable AI use cases per organisation, with the top 3 to 5 delivering measurable ROI within 12 months.

EMUG Tech uses a structured scoring matrix in the NORTH Outline phase: each AI use case is scored across ROI potential (cost reduction, quality improvement, productivity gain), data readiness (volume, quality, completeness, labelling effort), implementation complexity (integration requirements, infrastructure needs, model complexity), strategic alignment (fit with digital transformation and enterprise system roadmap), and time-to-value. The top-scored use cases are validated in workshops with function leads before the roadmap is finalised. This approach prevents organisations from investing in technically impressive AI programmes that lack the data foundation or business case to deliver measurable value.

EMUG NORTH ROI models use three-part evidence: baseline measurement of the current metric the AI will improve (defect rate, OEE, inspection cost, engineering cycle time); AI improvement projection based on comparable deployments in similar manufacturing environments — not generic AI industry statistics; and full-cost implementation estimate covering data preparation, model development, enterprise system integration, deployment infrastructure, and ongoing model governance. The result is a specific NPV, payback period, and IRR calculation — the same financial metrics manufacturing CFOs use for capital equipment decisions. Vague AI business cases built on analyst market projections consistently fail capital approval in manufacturing organisations.

Data requirements depend on the AI application area. Computer vision inspection requires 500 to 5,000 labelled images per defect class. Predictive maintenance AI requires 12 to 24 months of vibration, temperature, and acoustic sensor data with documented failure events. Generative AI knowledge assistants require a structured document corpus in PLM or SharePoint with consistent metadata. RPA programmes require stable digital processes with consistent input formats. EMUG NORTH data strategy assessment profiles data availability for each prioritised use case and defines the data preparation work required before model development — preventing organisations from committing to AI programmes they cannot yet support with data.

AI strategy and use-case consulting (NORTH Framework) defines what AI to build, why, in what sequence, with what architecture, on what data, and at what investment level — before any development begins. AI implementation (PRISM Framework) builds, deploys, and governs the AI solutions defined in the strategy phase. Most organisations that fail at AI do so because they skip or rush the strategy phase — deploying AI without validated use cases, without adequate data, without enterprise system integration design, and without a governance framework. EMUG Tech recommends a NORTH strategy engagement before any PRISM implementation programme for organisations that have not yet deployed AI at production scale.

A focused EMUG NORTH engagement for a single business unit — covering readiness assessment, use-case prioritisation, architecture design for top use cases, and proof-of-value planning — typically completes in 6 to 10 weeks. A full enterprise AI strategy engagement covering multiple business units, global sites, and a 24-month delivery roadmap typically takes 12 to 16 weeks. The proof-of-value Test phase for a single use case typically takes 4 to 8 weeks depending on data availability. These timelines assume access to function leads for workshops and data access for readiness assessment.

AI strategy and use-case consulting programmes are delivered across automotive OEMs and Tier 1 suppliers, aerospace and defense organisations, industrial machinery manufacturers, energy companies, and high-tech electronics firms in 20 countries: Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic in Europe; India, Japan, South Korea, China, Malaysia, Thailand in Asia-Pacific; UAE and Saudi Arabia in the Middle East; USA, Canada, Mexico, Brazil in the Americas. AI strategy delivery from Hyderabad, Germany, and Dubai with on-site workshop and assessment capability at client engineering and manufacturing facilities globally.