A technology roadmap should explain which operational problems deserve attention, which prerequisites must exist and what evidence is needed before committing resources. A calendar filled with product names answers none of those questions.
This note proposes a portfolio-design method consistent with the public scope of mining transformation work. It contains no employer portfolio, investment total, internal priority, supplier evaluation or claimed project result. The examples describe hypothetical decisions rather than completed implementations.
Capture opportunities in a comparable form
Write each opportunity around a problem, an affected process and an accountable operational sponsor. Record the present evidence, the decision that would change and the proposed way to measure value. Keep a technology suggestion separate from the problem statement so competing approaches remain possible.
An illustrative opportunity could be “reduce uncertainty in delay reporting,” with evidence that category mappings need reconciliation. Its first deliverable might be a validated dataset and an agreed definition, rather than a new platform. The delay analysis note describes what that dataset would need to defend.
Deduplicate opportunities that address the same constraint. One proposal from operations and another from IT may depend on the same source integration; treating them as unrelated can double-count benefits and underestimate shared work.
Use gates before weighted scores
A score can help order comparable opportunities, but it should not override an unresolved safety, cybersecurity or authorization condition. Establish mandatory gates first, involving the appropriate operational and technical owners. An idea can remain in discovery while its constraints are clarified.
Then compare strategic impact, evidence strength, feasibility, change readiness and risk. Define what each score means and record the supporting evidence. A high score based on a vendor estimate is different from one based on a measured baseline.
| Dimension | Evidence to seek | Common uncertainty |
|---|---|---|
| Operational value | Decision, baseline and affected exposure | Whether the proposed intervention changes the outcome |
| Technical feasibility | Source access, integration boundary and maintainers | Data quality, connectivity and operational constraints |
| Adoption | Sponsor, workflow and training capacity | Who changes behavior after delivery |
| Economic case | Costs, benefit assumptions and evaluation horizon | Recurring support, ramp-up and uncertain benefits |
| Delivery risk | Dependencies, access windows and fallback | Whether prerequisites are controlled by the project |
Keep the assessment inspectable. A single total hides why an opportunity was selected and makes later review harder.
Build an economic case with explicit assumptions
Separate one-time implementation costs from recurring licenses, connectivity, infrastructure, support and training. Document the evaluation horizon, ramp-up, baseline and attribution method. Use the organization's approved financial assumptions and review process.
NPV, IRR, ROI and payback answer different questions. A short payback does not describe the entire lifetime value, and a percentage return does not reveal absolute investment scale. Have the responsible finance team review the model; do not apply a universal hurdle rate or present uncertain estimates as approved savings.
Use scenarios to expose sensitivity: what happens if adoption is slower, coverage is incomplete or maintenance costs are higher? If two initiatives claim the same avoided loss, allocate the benefit consistently instead of adding it twice. Include a decision to stop or redesign when assumptions fail.
Sequence prerequisites and operational capacity
Map dependencies before assigning dates. A dashboard may depend on stable equipment identities, approved KPI definitions and reliable connectivity. A predictive model may additionally need representative labeled history and a workflow for acting on its output.
An illustrative sequence is: confirm the problem and baseline; validate a bounded dataset; test the intervention in an authorized setting; review evidence; then decide whether to scale. This is a learning sequence, not a requirement that every initiative use the same delivery model.
Balance shared foundations against immediate value. Building every integration first can delay learning; isolated pilots can accumulate incompatible definitions. Choose a bounded use case that validates a reusable foundation and budget time for its ownership. The IOC KPI catalog is one such foundation.
Keep the roadmap revisable
For each funded stage, record sponsor, decision gate, evidence required, dependencies and benefit owner. Review progress against evidence rather than only expenditure or task completion. When the baseline or operating constraint changes, revisit priority and document the decision.
A useful roadmap is a sequence of accountable decisions with visible uncertainty. Its credibility comes from explaining why to proceed, defer or stop, and from checking value after deployment instead of counting software delivery as realized benefit.