Sector insights
Patterns we see across sectors — what tends to qualify, what tends to be BAU, and how to build evidence that is clear, consistent, and proportionate to the work performed.
Different sectors produce different kinds of R&D evidence — not because the definition changes, but because the nature of uncertainty and the way teams run trials varies. A software team may evidence “advances” through architectural decision records, benchmark results, and iterative test artefacts; an engineering team may rely on prototype test plans, failure analysis, and design iteration logs; life sciences often needs strong experimental controls and traceable lab records.
The aim of this note is to help you scope “what’s likely in” (and why), avoid common overreach, and structure an evidence pack that a reviewer can follow without guesswork. If you’re filing under the merged scheme, consistency between your narrative, AIF disclosures, and cost logic matters more than ever.
A simple way to think about sector evidence
In every sector, strong claims usually show three things: (1) what the uncertainty actually was (not just the business goal), (2) what competent professionals tried and why those attempts were non-trivial, and (3) what was learned (including failures) and how that moved the project forward.
Practical test: if you removed all cost data, could a technical reviewer still understand what was uncertain, what was tried, and why the outcome wasn’t obvious at the outset?
Sector-by-sector: signals, pitfalls, and “what good looks like”
1) Software & IT (including MarTech)
Strong software claims typically centre on genuine technical uncertainty — not feature delivery. The most credible narratives articulate the constraint (latency, throughput, concurrency, security model, data consistency, model behaviour, integration limitations) and demonstrate systematic experimentation.
- Signals: architectural trade-offs; performance benchmarking; complex integration; novel data processing; algorithmic uncertainty; reliability/observability challenges.
- Common pitfalls: UI/UX iteration presented as R&D; “we built X” without the uncertainty; optimisation with no baseline or test results; routine integration described as “complex”.
- Evidence that lands well: ADRs, benchmark outputs, load tests, incident/post-mortem learnings, prototype comparisons, technical spikes, rejected approaches with rationale.
2) Engineering & manufacturing
Engineering claims often become persuasive when you show the build-test-learn loop: design intent, constraints, test plan, results, and the iteration driven by what failed or underperformed.
- Signals: prototyping; novel materials/process constraints; thermal/fluid/structural challenges; complex tolerance/assembly issues; repeat test cycles; reliability validation.
- Common pitfalls: presenting first-time manufacture as R&D without uncertainty; documenting “what we built” rather than what was unknown; excluding failed prototypes (often the best evidence).
- Evidence that lands well: test plans/results, failure analysis, FEA/CFD outputs with interpretation, iteration logs, design change notes, validation protocols.
3) Life sciences & biotech
Life sciences typically needs strong experimental design and traceability. Reviewers respond well to clarity on controls, variables, hypotheses, and interpretation — especially where the outcomes are genuinely uncertain.
- Signals: experimental protocols with non-obvious outcomes; assay development; scale-up issues; stability/specificity challenges; method validation with iterations.
- Common pitfalls: routine testing/QC described as R&D; protocols without stated uncertainty; lack of control/variable logic; outcomes reported without interpretation.
- Evidence that lands well: ELN extracts (sanitised), protocol versions, validation reports, trial matrices, control results, statistical analysis summaries, decision logs.
4) Architecture, construction & built environment
The best built-environment narratives focus on technical uncertainty, not design novelty. The work is strongest where it involves non-standard engineering constraints or performance targets that required structured testing or modelling beyond routine application of known solutions.
- Signals: performance modelling (energy/thermal/acoustic); novel façade/systems integration; bespoke structural solutions; constraints-driven iteration and validation.
- Common pitfalls: aesthetic design framed as R&D; “unique project” without technical uncertainty; using standards compliance as the “advance”.
- Evidence that lands well: modelling outputs + interpretation, simulations, test/commissioning results, design iterations driven by failures or constraints, options analysis.
A pragmatic sector framework we use
To keep sector claims consistent, we often run the narrative through a simple lens: uncertainty, systematic approach, learning, and transferable outcome. The wording differs by sector — but the shape stays the same.
Uncertainty that is genuinely technical
State what was unknown at the outset (constraints, behaviour, performance, compatibility, reproducibility) and why competent professionals couldn’t readily resolve it.
Systematic trials and decision logic
Show the structured approach: hypotheses, prototypes, tests, comparisons, and the decisions that followed. Failed routes often strengthen the story.
Learning captured as evidence
Record what changed your understanding. Benchmarks, test results, validation outputs, incident learnings — each sector has its “natural” artefacts.
Cost logic that maps cleanly to activity
Once the narrative is solid, map costs to the work done. Keep apportionments explainable and consistent with project scope and roles.
Common overreach patterns (and how to fix them)
The most common issue across sectors is describing the product rather than the uncertainty. A second frequent issue is trying to reverse-engineer eligibility from timesheets or expenditure reports. If you start from costs, you’ll usually end up with a narrative that feels generic, and a methodology that is hard to defend.
- Fix 1: write the uncertainty and trials first; then map the people/costs to those activities.
- Fix 2: capture “what changed” — baselines, test outputs, iterations, rejected approaches.
- Fix 3: keep AIF project descriptions aligned to the report (avoid parallel stories).
- Fix 4: ensure roles make sense (e.g., BAU PM/ops shouldn’t dominate the R&D percentage).
Want a quick sector-scope review before you draft?
If you share a short list of projects and a high-level breakdown of who did what, we can sanity-check which items look “in scope” for your sector, what evidence will matter most, and where claims typically overreach.