How can companies define AI innovation priorities based on customer needs?

First establish what task customers need to complete

Look for difficulties customers have recently encountered in recurring support questions, sales conversations, field observations, and authorized interviews. Use a simple table to record the context, participants, current approach, alternatives, obstacles, and information sources, distinguishing direct observations from customer statements and team assumptions. Let customers describe the task in their own words first, then check their account against business records. Keep anything that cannot be verified as an assumption, and do not turn a single opinion into a claim about widespread demand.

For example, suppose customers repeatedly ask during inquiries whether a service is suitable for them. The team could first review the pages they read, their exact questions, and how they currently choose, to determine whether the difficulty lies in understanding the explanation or making a choice. At this stage, describe the opportunity as “helping customers judge whether the service fits their current task,” rather than immediately proposing an AI assistant. Move on to selecting a direction only when the task, obstacle, and supporting evidence are clear; gather more observations when information is insufficient.

Select directions by value, information readiness, and consequences of failure

Rank candidate opportunities by frequency, impact on the customer’s task, verifiability, and consequences of failure. Also check whether the information is lawful to use, reliable, and sufficiently current, and whether someone can handle exceptions and complaints. For each direction, specify an owner, scope, required resources, and evidence gaps. When departments disagree, first establish whether the disagreement concerns facts, objectives, or boundaries of responsibility, then have the person responsible for the business confirm the decision. Avoid substituting technical availability for business judgment.

For example, suppose the team is considering two directions: “explaining when a service applies” and “making judgments based on sensitive information.” For each, it could list the required information, how errors would be handled, and human responsibilities. The first still requires checking the basis for its explanations; the second should undergo appropriate professional review first. The selection outcome can be to proceed, gather more information, or defer. If the conditions for using information are unclear or no one owns exception handling, an opportunity should not enter a pilot simply because a prototype is easy to build.

Use a limited set of tasks to assess whether the pilot is usable

Write a one-page pilot brief for the priority direction, specifying target users, the task, success signals, data sources, human responsibilities, a fallback when errors occur, the trial period, and stopping conditions. Success signals should correspond to observable behavior, such as whether users can make a choice, recognize uncertainty, and find help. First assess these behaviors through a prototype and a limited set of real tasks. Capabilities that lack supporting information should not be described in the pilot materials as already available.

For example, suppose the pilot helps customers choose a contact route after reading a service description. Participants could independently read, choose, and make contact while observers record pauses, misunderstandings, follow-up questions, and exact wording, along with the device, page version, and task conditions. Test facilitators should not explain the answers during the session. If participants continue to misunderstand the scope or cannot find human help, revise the prototype before retesting with the same task, and check for new sources of confusion. Small-scale findings help identify problems; they cannot represent all customers.

Decide whether to expand investment based on task outcomes

During the review, work through the pilot brief item by item, recording which assumptions are supported, where information remains insufficient, and which steps need redesign. Assess whether customers can complete the task separately from whether the demonstration runs smoothly. Judge value using customer feedback and actual delivery together, without treating visits or clicks alone as evidence. Expand the pilot only when benefits, risks, and maintenance responsibilities are clear. Unresolved key issues should define the boundaries of the next stage of work.

For example, suppose a webpage appears clear, but sales staff still have to repeatedly correct customer expectations during inquiries. Review the first page customers saw alongside subsequent inquiry records and delivery materials to locate where the discrepancy arises. If it comes from information across channels or training for delivery, revise that specific area. If customers cannot even identify the task itself, return to researching the context. Link the basis for each judgment, the changes made, and the retest findings in the records so there is evidence for deciding whether to continue, adjust, or stop.

Keep information and brand messaging aligned with capability changes

After launch, assign an information maintainer, a business reviewer, and a content owner, and record information sources, versions, and the next review date. When the product or business changes, first determine whether the change affects functionality, audiences, responsibilities, or service paths, then check the website, sales materials, demonstrations, and service processes. Retain valid explanations, remove or mark outdated content, and inform support and sales. The update is complete when the relevant touchpoints can provide explanations consistent with actual delivery.

For example, suppose the intended audience for a service changes. The content owner could update the relevant pages, the business reviewer could verify eligibility limits, and support and sales could adjust their responses accordingly. Do not promote capabilities before they are available, and record the basis for corrections and their completion when errors are found. When citing external research or exhibition viewpoints, link to the specific original material and explain that the article is the company’s independent analysis. Do not add forum statements, customer conclusions, or industry figures without direct supporting evidence.

Discussion Background and Scope

CES · AI & Creativity: Navigating the New Frontier in Advertising · 2025-01-09. Official page date: 2025-01-09. The source discusses ai, marketing creativity and human involvement. It is background reading, not evidence for each practical recommendation in this article. The recommendations are Qingmei Future’s independent analysis.

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