Building an Enterprise IoT Strategy and Roadmap

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The full contents of Building an Enterprise IoT Strategy and Roadmap, slide by slide. Read it here, or use the viewer above for the designed version.

1. 02

   Why IoT Strategy Must Be Business-Outcome-Led, Not Technology-Led

   • Technology-first IoT programs tend to accumulate sensors and dashboards without a clear owner for the business result
   • Every initiative should trace back to a named outcome: cost reduction, uptime, safety, revenue, or customer experience
   • Starting with the business case forces prioritization decisions that starting with technology defers or avoids
   • Industry-reported range: a meaningful share of enterprise IoT pilots do not progress to scaled deployment, often because the outcome was never clearly defined
   • The strategy question is not 'what can we connect' but 'what decision or action does this data change'
2. 03

   Anchor the Strategy in Three to Five Enterprise Outcomes

   • Select a small number of outcomes the C-suite already tracks — asset uptime, safety incidents, energy cost, quality yield, service revenue
   • Map candidate IoT use cases to these outcomes before evaluating any vendor or platform
   • Reject or defer use cases that cannot be tied to an outcome owner in the business, not just in IT
   • This discipline keeps the roadmap defensible when budget is contested
   • Outcome ownership should sit with the business unit leader, with IT and OT as enabling partners
3. 04

   Building the Use Case Portfolio

   • Inventory candidate use cases across the value chain: asset monitoring, predictive maintenance, quality control, supply chain visibility, connected products
   • Score each on business value, technical feasibility, and time to impact rather than novelty or executive interest alone
   • Balance the portfolio across quick wins (6-12 months), mid-term builds (12-24 months), and platform investments (24+ months)
   • Avoid concentrating the entire portfolio in a single business unit or plant — early diversification tests platform reusability
   • Revisit the portfolio quarterly as pilot results and cost data become available
4. 05

   Prioritization Framework: Value, Feasibility, and Readiness

   • Value: quantify the range of potential impact, and label estimates as directional until validated by pilot data
   • Feasibility: assess connectivity, data quality, and integration complexity at the specific site before committing
   • Organizational readiness: confirm the business unit has the process discipline to act on the data once it exists
   • Sequence high-value, high-readiness use cases first — they build credibility and funding for harder problems later
   • Illustrative scenario, not a verified case study: a manufacturer might prioritize predictive maintenance on a single high-downtime asset class before extending to the full plant
5. 06

   Build vs. Buy vs. Partner for the IoT Platform

   • Build: justified only when connectivity or data management is a genuine source of competitive differentiation
   • Buy: appropriate when commercial platforms cover the core needs — device management, data ingestion, analytics tooling
   • Partner: useful for domain-specific capability (industrial protocols, edge hardware, vertical analytics) the organization does not want to own
   • Total cost of ownership should include integration, security, and ongoing platform operations, not just license fees
   • Most enterprises land on a hybrid: a commercial core platform with selective in-house development at the application layer
6. 07

   Platform Decision Criteria

   • Interoperability with existing OT systems, ERP, and data platforms — avoid a platform that becomes a new silo
   • Vendor viability and roadmap alignment with the organization's multi-year plans, not just current feature fit
   • Data ownership and portability terms, so the organization is not locked into a single vendor's ecosystem
   • Security architecture, including device identity, patching cadence, and support for zero-trust network segmentation
   • Total cost of ownership modeled across a 3-5 year horizon, including scaling costs beyond the pilot
7. 08

   Organizational Model and Skills Required

   • Establish a cross-functional IoT team spanning IT, OT engineering, data science, and business process owners
   • Close the OT-IT skills gap through blended hiring, upskilling existing engineers, and selective partner augmentation
   • Assign a single accountable owner for the program, reporting to a sponsor with enterprise-wide authority
   • Build a center of excellence to standardize architecture patterns and avoid duplicated effort across business units
   • Plan for the operating model to evolve as the program moves from pilot teams to a permanent operating function
8. 09

   Funding and Staged Investment Approach

   • Fund in stages tied to demonstrated evidence, not a single large upfront commitment across the whole roadmap
   • Stage 1: proof of value on one or two use cases, funded to test the business case, not to scale infrastructure
   • Stage 2: platform investment once proof of value is established and a repeatable pattern is identified
   • Stage 3: scaled rollout funded against a validated ROI model and clear deployment cost per site or asset
   • Maintain a contingency reserve — connectivity, integration, and data quality costs are frequently underestimated at the pilot stage
9. 10

   Measuring ROI and Defining Success Metrics

   • Define success metrics before the pilot begins, agreed jointly by the business owner and the program team
   • Combine leading indicators (data quality, uptime of the IoT system itself) with lagging business outcomes (cost, revenue, safety)
   • Use a consistent ROI methodology across use cases so the portfolio can be compared and re-prioritized objectively
   • Industry-reported range: payback periods for well-scoped industrial IoT use cases commonly span one to three years, varying widely by use case and industry
   • Report metrics to the same governance body that approved funding, on a fixed cadence, to keep accountability visible
10. 11

    Cross-Functional Governance: IT, OT, Security, and Business Units

    • Establish a governance board with standing representation from IT, OT, security, and the sponsoring business units
    • Define clear decision rights: who approves new use cases, platform changes, and data-sharing agreements
    • Resolve the IT-OT boundary explicitly — network ownership, patching responsibility, and incident response roles
    • Require security and privacy review as a gate before any device or data flow goes into production, not after
    • Governance should scale with the program: lightweight for pilots, formal and cadence-driven once deployment is enterprise-wide
11. 12

    Risk Management Across the IoT Stack

    • Device layer: physical tampering, weak default credentials, and long device lifecycles that outlast software support
    • Network layer: expanded attack surface from connecting previously isolated OT networks to enterprise IT
    • Data layer: data quality, integrity, and lineage issues that undermine trust in downstream analytics and decisions
    • Vendor layer: concentration risk if a single vendor controls device firmware, connectivity, and analytics
    • Build an IoT-specific risk register reviewed alongside enterprise risk management, not as a separate IT-only exercise
12. 13

    Scaling from Pilot to Enterprise-Wide Deployment

    • Design the pilot architecture for scale from day one — treat it as reference architecture, not a one-off proof of concept
    • Standardize device onboarding, data models, and integration patterns before the second site or use case is deployed
    • Separate the cost of proving value from the cost of scaling it — treat the second as a distinct budget decision
    • Build a repeatable deployment playbook so scaling becomes an execution exercise, not a redesign exercise each time
    • Illustrative scenario, not a verified case study: an energy company might pilot remote asset monitoring on one facility, then codify the deployment steps into a playbook before rolling out across its full facility network
13. 14

    Why IoT Programs Stall — and How to Avoid It

    • No clear business owner accountable for the outcome, leaving the initiative to drift as an IT project
    • Pilots designed without a path to scale, so success cannot be replicated cost-effectively
    • Underestimating data quality and integration effort, which consumes the pilot budget before value is demonstrated
    • Security and governance addressed too late, forcing rework or program pause after deployment has already begun
    • Lack of a staged funding model, so the program loses support after the first budget cycle without a scaling case
14. 15

    Next Steps: Committing to a Staged Roadmap

    • Confirm the three to five enterprise outcomes the IoT strategy will be measured against, with named business owners
    • Approve Stage 1 funding for two to three prioritized use cases, scoped for a defined proof-of-value window
    • Stand up the cross-functional governance board before the first pilot goes live, not after
    • Commission the platform evaluation (build/buy/partner) in parallel, so Stage 2 is not delayed by a late start
    • Set a checkpoint in 90 days to review pilot evidence and confirm the Stage 2 investment decision