Making Technology Value Legible Across Borders

A technology presentation can be accurately translated and still leave its audience unable to decide. A customer hears performance specifications but cannot see how work will change. An investor hears demand but cannot distinguish repeatable revenue from bespoke delivery. A local partner hears an attractive market ambition but cannot identify a workable role.

Our view is that useful cross-border communication connects four things: the technical claim, the local problem, evidence relevant to that problem and a clear responsibility for delivery. Different audiences need different explanations, while the underlying facts must remain consistent.

Start with the decision behind the question

When a customer asks whether a product is “proven”, they may be asking about reliability in a particular workflow. An investor may use the same word to ask whether anyone pays for it at a sustainable margin. A partner may want evidence that the supplier can train, support and retain customers once introductions have been made.

Answering all three with the same performance chart creates avoidable ambiguity. Ask what decision the evidence must support, then identify the relevant comparison. For a business user, the comparison may be the existing manual process. For an investor, it may be the resources required to win and retain another customer. For a partner, it may be another supplier competing for the same implementation team.

Even within Singapore’s public procurement guidance, value extends beyond the lowest price to considerations including reliability and long-term cost. Its formal scope is government purchasing. The broader lesson we draw is to understand the buyer’s full comparison before choosing the headline. Singapore Ministry of Finance, Government procurement.

Keep the claim, evidence and promise at the same scale

A laboratory result may support a technical claim under stated conditions. It does not automatically support savings at a new customer site. A successful demonstration may show that an AI application can answer selected questions. It does not establish the net benefit once review, correction and system integration are included.

This is also a practical communication problem. If the strongest number in a presentation carries qualifications in a distant appendix, the audience may attach it to a broader promise. Put the conditions beside the claim: what was measured, against which baseline, in what setting and with what exclusions.

For AI, NIST’s AI RMF 1.0 places intended use and deployment context within its risk-mapping function. It is a voluntary framework; citing it is not proof that an application has been evaluated. NIST, AI RMF Core. Our recommendation is to make that context visible in commercial materials before a buyer has to reconstruct it.

A hypothetical cooling proposal, explained three ways

Consider a hypothetical overseas supplier offering industrial cooling equipment to an owner in Southeast Asia. This is an illustration, not a client case. The supplier has controlled test results but has not measured performance at the proposed site.

For the customer, the useful message is: “We propose assessing whether the equipment improves the cost of your cooling process under your operating conditions.” The next evidence concerns the baseline, measurement approach and installation constraints. The customer should be able to question whether a test will disrupt operations.

For an investor, the useful question is whether the commercial model can be repeated. The supplier should distinguish what is standard equipment from work requiring site-specific adaptation, and show which cost assumptions remain untested. For a local partner, the discussion concerns installation responsibilities, training, support and the economics of maintaining the relationship.

All three conversations share the same technical facts and limitations. They differ in the decision being made. Translating the message well may reveal that the supplier is ready for a feasibility discussion while still lacking evidence for a wider sales or financing claim.

Build an audience–evidence table

The following is our proposed tool for reviewing a deck, proposal or introductory conversation. Use one row per audience, and keep unsupported expectations visible as assumptions.

Scroll the table horizontally, or focus it and use the arrow keys.

The table should also work in reverse. A local partner may need to explain to an overseas headquarters that the proposed support model cannot be staffed economically. That information is part of the value proposition: a promise becomes more credible when it reflects the conditions under which someone must fulfil it.

Let translation improve the proposition itself

If an audience repeatedly misunderstands a claim, inspect the underlying offer as well as the language. Perhaps the benefit belongs to one department while the cost sits with another. Perhaps the supplier’s promise depends on data the customer cannot make available. Better wording cannot resolve either condition on its own.

Revise the proposition when the evidence exposes a mismatch, and update all audience versions together. The strongest explanation leaves each party able to state what is supported, what remains uncertain and which bounded action could make the next decision possible.

Sources

• Singapore Ministry of Finance: Government procurement — public-sector value-for-money considerations.
• NIST: AI RMF 1.0 Core — voluntary 2023 framework; used here for the importance of intended use and context, not as a certification.