Calling a company AI-native is easy. Making the words mean something is harder.

For us, AI-native is not a requirement that every product contain a chatbot, nor a claim that new technology removes the need to understand a problem. It is a working method: use capable tools where they help, build with them thoughtfully, and keep a person accountable for the result.

What the studies actually measured

The results below are useful because they describe specific tasks and participants. They are not a forecast for every AI tool or every team.

Three measured settings

Professional writing: In a randomized study of 453 college-educated professionals doing midlevel writing tasks, access to ChatGPT cut average completion time by 40% and raised rated output quality by 18%.

Customer support: A deployment study of 5,172 agents found a 15% average increase in issues resolved per hour. Gains varied by experience, and the most experienced workers saw only small speed gains and small quality declines.

Software development: Three company field experiments with 4,867 developers found a 26.08% increase in completed tasks when results were combined. The researchers report that the individual experiments were noisy and varied.

Noy and Zhang, Science (2023); Brynjolfsson, Li and Raymond, Quarterly Journal of Economics (2025); Cui et al., Management Science (2026)

These figures measure different tasks, tools, groups, and outcomes. They cannot be compared as if they were a single benchmark. The lesson we draw is narrower: measure a tool in the work where we expect it to help, and do not turn a result from one setting into a promise about another.

Start with the job, not the model

It is tempting to begin with a capability and search for a reason to use it. A model can summarize, classify, draft, translate, search, or generate. Those possibilities are real, but the existence of a capability does not tell us which task deserves it.

Start instead with the person and the work. What are they trying to finish? What is difficult about it today? What would a better result look like from their point of view? Is the difficulty about making a first pass, finding something in a large collection, or turning material into another useful form? If ordinary software solves the problem more clearly, adding a model may make the product less predictable without making it more useful.

This also prevents “AI” from becoming an answer to a question nobody asked. The technology should earn its place in the product the same way any other consequential capability does: by helping with a real job, in a way a person can understand and choose.

Put the tool at the right point in the work

There are places where generative tools can give a person a useful start. They can offer variations, reorganize material, help explore a draft, or make a repetitive transformation easier to inspect. Their value is not simply that they can produce something quickly. It is that the person can spend more attention on the parts that need their judgment.

That only works when the surrounding experience is designed well. The person should know what input the system is using, what kind of output to expect, and what they can do with the result. A suggestion can remain a suggestion. A draft can be revised. A source can be checked. The interface should make those differences clear instead of dressing each result in the same confident presentation.

If the task is high consequence, speed alone is a weak reason to hand it over. If an error would be hard to detect or reverse, we need stronger review, narrower scope, or a different design entirely. Some tasks are better kept under direct human control.

Keep evidence and generated material distinct

A fluent response can sound like evidence even when it is only a plausible answer. That makes provenance part of product quality. People should be able to tell whether a result came from their own material, a system transformation, a linked source, or a human decision.

The same distinction matters inside a team. A generated list of possible user needs can help prepare research. It cannot tell us which needs are real. A draft might help us compare language. It cannot tell us what customers believe. We should not let generated material move into a decision record without marking what has and has not been verified.

This is not a reason to avoid the tools. It is a reason to know what we asked them to do and where their work ends. They can widen a search or help us examine more options. They do not make responsibility disappear.

Design the review, not only the generation

If a person must check a result, make checking possible. Keep the original material available where appropriate. Show meaningful changes. Make it easy to edit, reject, or start again. Do not hide an uncertainty that could affect the next choice.

There is a balance. A wall of technical detail can make the experience harder without making it more transparent. What matters is whether a person can answer the practical questions for this task: what did the system use, what did it change, and how much should I rely on it? Good disclosure is specific to the decision, not a universal disclaimer tucked away out of sight.

Keep learning from the actual product

An AI feature has ordinary product responsibilities as well as model-specific ones. It needs a clear purpose, good interaction, privacy-conscious data handling, sensible failure behavior, and a way to find out when it falls short. The exact requirements depend on the task and information involved.

We should be precise about what we know. A test with a few examples can reveal a bug or confusing interaction. It does not establish that the system works for every person, language, or context. A successful prototype does not tell us how a capability behaves in daily use. The product must give us a way to learn from real use that respects the people using it.

When a result is wrong, the response depends on the harm and the workflow. Perhaps the person needs to correct it. Perhaps we should narrow what the system attempts. Perhaps the feature needs better sources, a different interface, or removal. A company that uses these tools responsibly should be willing to change its mind about where they belong.

Let AI remain a means

At Einhar, AI-native means that we take the technology seriously enough to question it. We want to use it to explore, make, and operate more effectively. We also want the work to remain understandable, accountable, and rooted in problems worth solving.

The important question is not whether a product can be described as AI-powered. It is whether a particular use helps someone do a real task, whether they can judge the result, and whether we are prepared to be responsible for the system we put in front of them. If those answers are unclear, the honest next step is to learn more before adding the feature.

Sources and limits

The three studies above are linked at the point where their results are summarized. Each tests a bounded setting: professional writing tasks, one customer-support deployment, and coding assistants in three companies. Their outcome measures and tool configurations differ, so the reported percentages are not a head-to-head comparison and should not be generalized to a product without testing it there.

For risk-management recommendations, see NIST's AI Risk Management Framework. NIST describes testing before deployment and during operation, documenting uncertainty and limitations, and tracking risks as systems and contexts change. The framework is voluntary guidance, not an evaluation of an Einhar product. Our product principles are informed by it, but they remain our responsibility to apply.