dooopSoftware · Strategy · 11 min

How to Present a Software Proposal with Embedded AI

Sell observable behavior: supported decision, acceptance criteria, limits, human review, and testing before operation.

Published on September 6, 2026

CENTRAL THESIS

An AI proposal cannot rely on charm. It must turn intelligence into an accepted and operable scope.

The document must state behavior, acceptance, limits, and operation before the sales meeting.

In a sales meeting, a software proposal with AI needs to show observable behavior before discussing artificial intelligence in the abstract. The client must understand which decision will be supported, how the intelligent functionality will act in concrete situations, what criteria allow acceptance of the delivery, and where automation must stop. Without this, the proposal remains attractive in the demonstration but fragile for development, approval, and operation.

Name the Supported Decision in the Commercial Scope

The first question in a software proposal with embedded intelligence should not be which model will be used. It should be: which decision will be better informed, faster, or less ambiguous for the user?

This changes the conversation. Instead of opening with "we will have artificial intelligence to analyze requests," the proposal can say: "when a new support request arrives, the system will assist the analyst in deciding whether to prioritize, forward, or request more information." The technology remains relevant but enters as a means to delimit scope, acceptance, and responsibility over that operational decision.

This care avoids a common confusion: treating AI as a magical layer that improves any part of the product. The presentation of the DORA 2025 report describes AI as an amplifier of existing organizational strengths and weaknesses and highlights the importance of the organizational system for return on investment. This observation does not prove that an AI proposal must follow a specific format but helps remind that technology inserted into a poor process tends to amplify ambiguities, unresolved exceptions, and undefined responsibilities as well.

In the proposal, the supported decision should appear in a simple sentence with three elements:

who uses the functionality;
at what point in the work it appears;
which decision or action it helps to take.

If the sentence cannot be written clearly, the opportunity may still be poorly formulated. In that case, it is worth stepping back and connecting the initiative to the client's problem, as discussed in how to create an AI strategy connected to business.

Describe the Expected Behavior in Real Use Situations

After stating the decision, the proposal needs to translate intelligence into behavior. "The system understands documents," "classifies automatically," or "learns from the user" are too broad phrases. They may sound modern but do not guide scope, testing, or acceptance.

A good description shows what the software will do when faced with normal, incomplete, and ambiguous inputs. This brings the proposal closer to operational reality, where data rarely arrives perfect and exceptions often reveal the true design of the product.

Fictional example: imagine a company offering technical support software for equipment manufacturers. The proposed intelligent functionality should not be described only as "AI for ticket triage." A more useful formulation would be:

when the ticket includes equipment model, defect description, and recent history, the system will suggest a category and priority for analyst review;
when necessary information is missing, the system will indicate which fields need to be completed before forwarding;
when the description contains contradictory signals, the system will mark the case as ambiguous and request human review before suggesting priority.

This example is fictional and does not represent a real case or measured result. Its usefulness lies in showing the difference between selling a generic capability and describing the expected system behavior.

The proposal should also clarify that the intelligent functionality may use third-party models, proprietary models, business rules, information retrieval, or a combination of these approaches. Maturity does not require, by itself, building a proprietary model. It requires knowing what the solution needs to do, how it will be observed, and which limits it must respect.

Turn the Demonstration into Acceptance Criteria

AI demonstrations often impress when they choose good examples. Proposals, however, cannot depend on enchantment. They need to turn the presentation into verifiable criteria.

Acceptance criteria for AI do not need to be overly technical but must allow an objective decision about the delivery. They may include:

a set of scenarios to test, covering typical, incomplete, and ambiguous cases;
types of errors considered acceptable or unacceptable for the context;
response time perceived as adequate for the workflow;
recording the recommendation made by the system and the action taken by the user;
rule for forwarding to human review when there is low confidence or conflicting information;
sufficient explanation for the user to understand why they received a suggestion.

The point is not to turn the commercial proposal into a complete technical specification. It is to prevent acceptance of the delivery from depending on a subjective impression at the final meeting.

The February 2026 update of METR considers new data an unreliable signal of AI's current effect on productivity and points out difficulties such as participant selection, task selection, and time measurement with competing agents. This source does not authorize generalizations about all AI use but reinforces relevant caution for proposals: productivity gain should not be promised as an automatic consequence of intelligent functionality.

If there is a hypothesis of impact, it should be presented as a hypothesis to observe in the client context. For example: "it is expected to evaluate whether assisted triage reduces rework in ticket classification by comparing cases before and after implementation in defined scenarios." Even so, this is not a promise of result. It is an observation design.

This reasoning connects to the logic of the AI roadmap: choosing an initiative is not enough. It is necessary to define how it will be tested, reviewed, and eventually stopped.

Record Limits, Exceptions, and Responsibilities in the Scope

A software proposal with AI gains credibility when it states what the system will not do. This may seem counterintuitive in a commercial conversation but reduces the risk of wrong expectations and improves decision quality.

In the proposal, limits serve to define responsibility, avoid implicit scope, and guide acceptance.

In intelligent functionality, limits may appear in various forms:

input types outside the scope, such as unreadable files, missing fields, or descriptions without sufficient context;
decisions the system can suggest but not execute without authorization;
cases where the recommendation must be blocked;
prohibited uses of the functionality;
situations where automation must forward the case to a person.

In the fictional example of technical support software, the proposal could declare that the system does not close tickets alone, does not approve part replacements, and does not change priority when there is disagreement between the customer's description and equipment history. In these cases, it only signals the inconsistency and requests review.

This type of statement reduces scope disputes and improves acceptance. The client understands what they are acquiring. The technical team avoids building a solution based on implicit interpretations. Leadership can discuss risk before deployment.

This is also where the proposal should separate human judgment from "late correction." Human review should not appear only as an improvised safety net when AI fails. It can be a design choice from the start, especially when the decision requires context, responsibility, commercial exception, or consequence evaluation.

Explain How the Delivery Will Be Tested Before Going into Operation

Testing intelligent functionality is not just verifying that it runs. It is observing how it behaves in situations representing the expected real use.

The proposal should indicate a practical validation logic. This may include a sample of historical cases, scenarios created from frequent situations, comparison with current rules, and qualitative review of errors. The vocabulary can be simple as long as the criterion is verifiable.

Microsoft describes its ExP platform as a way to incorporate experimentation into the development cycle, validate hypotheses, measure impact, and iterate products. This does not mean every company needs the same platform nor that every feedback retrains a model. The useful point for a proposal is more basic: intelligent functionalities should be evaluated as product hypotheses in context, not as promises closed in the demonstration.

Before going into operation, the delivery should answer questions such as:

do the tested scenarios represent the user's real work?
are the errors found acceptable for this process?
does the user understand the recommendation enough to decide?
are ambiguous cases being forwarded correctly?
is there a record to review behavior after deployment?

These questions show whether the proposal explains how the recommendation will be validated in use. An organization may have a sleek interface but not know how to explain how the recommendation will be tested. Another may start with a less flashy solution but better delimited, with clear criteria and viable operational learning. To diagnose this starting point, it is worth relating the proposal to the discussion of AI maturity.

Separate Deployment, Learning, and Future Evolution

A common mistake in software proposals with artificial intelligence is treating evolution as automatic. "The system learns from use" can mean many different things. It can mean users give feedback. It can mean rules are adjusted. It can mean data will be analyzed in future cycles. It can mean model retraining when there is a base, governance, and technical decision for that.

These things are not equivalent.

Therefore, the proposal should separate three layers:

first delivery: expected behavior for the initial version;
monitoring: data, records, and signals that will be observed after deployment;
future evolution: possible changes, conditioned on analysis, prioritization, and new decision.

This care prevents the client from understanding learning as a guarantee of automatic improvement. User feedback alone does not retrain a model. Even when feedback is collected, it is still necessary to decide how it will be used, who reviews it, which data may enter the process, and what change will be authorized.

The proposal can say, for example, that the first version will record recommendations, human reviews, and reasons for changes. It can also foresee a routine for analyzing these records. But it should avoid promising that the software will progressively improve without explaining the mechanism, responsibility, and condition for evolution.

Here, executive language matters. Instead of "the system will learn continuously," a more honest wording would be: "the solution will record interactions and reviews to support future improvement cycles, which will depend on analysis of collected data, prioritization decisions, and validation before a new version."

Close the Proposal with Operational Responsibilities

Software with AI does not end at deployment. The delivery puts into circulation a capability that recommends, prioritizes, classifies, summarizes, or triggers flows. Someone needs to monitor this behavior.

The proposal should conclude with explicit responsibilities. It does not need to become a bureaucratic document. It needs to answer who decides and who acts when the functionality encounters exceptions.

A simple matrix, described in text, can cover:

who configures rules, parameters, and information sources;
who approves exceptions and ambiguous cases;
who monitors recommendation and review records;
who decides behavior changes;
who evaluates incidents or relevant deviations;
who authorizes new versions of the functionality.

This clarity avoids a trap: treating embedded intelligence as if it were only a technical delivery. It is also a change in how decisions circulate within the product and the client's operation.

Proposal Checklist for Software with Embedded Intelligence

Use this checklist before presenting the proposal. It does not replace technical analysis but helps turn promise into an evaluable scope.

Supported Decision

Does the proposal state which user decision will be supported by the intelligent functionality?

Acceptable evidence: there is a sentence connecting user, usage moment, and decision. Fictional example: the analyst receives a priority suggestion to review support requests before forwarding to the technical team.

Observable Behavior

Does the proposal describe what the system does with normal, incomplete, and ambiguous inputs?

Acceptable evidence: at least three scenarios are described, covering typical case, case with insufficient information, and case requiring human review.

Acceptance Criteria

Can the delivery be accepted or rejected based on verifiable criteria?

Acceptable evidence: criteria include test sample, tolerable error types, response time, recommendation record, and forwarding rule for review.

Declared Limits

Does the proposal specify what AI will not decide alone?

Acceptable evidence: there is a list of decisions out of scope, blocking conditions, and cases where user authorization is needed.

Realistic Measurement

Does the proposal avoid promising automatic productivity gain?

Acceptable evidence: the proposal defines how impact will be observed in the client context, without turning demonstration, opinion, or isolated use into proof of result.

Operation After Delivery

Does the proposal specify who monitors, reviews, and decides changes after deployment?

Acceptable evidence: responsible parties are defined for monitoring, behavior correction, rule updates, and decisions on new versions.

Before the meeting, review whether the proposal answers three questions: what the system will do, how the delivery will be accepted, and which scope and operation limits will be respected. If any of these answers is missing, the proposal is still selling intention, not an operable capability.

If you want to discuss this decision in your company's context, talk to dooop.

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