AI vendors need to connect insights to action

Faster AI insight needs a path to action

An artificial intelligence (AI) vendor can deliver an answer faster and still leave your team unable to decide. For business and customer experience (CX) leaders, the commercial question is whether that answer supports action the organization can trust, control, and justify. Speed helps when analysis time is delaying a decision. Accuracy concerns, unclear objectives, and approval requirements create different constraints.

The potential time saving is substantial: CMSWire describes AI producing insights in “minutes or hours instead of days, weeks or months” compared with analyst-team timelines. Applying AI to large datasets can therefore bring findings to leaders sooner. But Ryan Tamminga, Chief Customer Officer for Alchemer, argues that vendors also need to help customers act on those findings. Speaking with CMSWire for Is Your AI Vendor Solving the Right Problems?, he puts the business problem at the center of that work.

Tamminga brings a supplier’s perspective: Alchemer sells a customer feedback platform and benefits commercially when customers adopt its AI-supported services. His advice therefore comes from a company with a stake in making AI useful and acceptable to buyers. For buyers, evaluation starts with the improvement they need, then turns to what would allow them to act.

Start with a defined business problem

Defining that improvement gives the vendor discussion a clear purpose. Adding AI to a product establishes that the technology is present, but the buyer still has to determine whether it addresses an important operational problem. Before considering analysis speed, the team needs to explain what is going wrong and what a better outcome would look like.

Tamminga describes Alchemer’s priority in those terms: “Our customers are still asking us to solve business problems. It’s nice when I can solve that problem for them by deploying a type of AI, but the mission is still focused on the pragmatic view of ‘How do I solve a business problem my customers have?’” For buyers, the question is which customer problem the proposed use of AI will help resolve. The vendor then has a specific improvement to explain.

A specific improvement also changes how a team evaluates a demonstration. A vendor may show that its technology processes feedback quickly, while the buyer still needs to decide which business decision that analysis should improve. Asking for the problem and intended outcome before discussing AI features keeps the demonstration tied to its purpose. It also helps the buyer judge whether faster processing addresses the actual obstacle.

Without that clarity, faster processing can compound a poorly understood problem. Analysts cited by CMSWire warn that organizations can introduce new problems when they use AI to bypass the work of understanding the underlying issue. A deployment without a clear purpose can preserve existing inefficiencies, magnify them, or spread errors faster across operations. Establishing the objective first gives the team a basis for choosing a task and judging its output.

Build confidence in the output

Judging the output deserves as much attention as judging its speed. Research cited by CMSWire ranks improving decision confidence equally with analyzing data faster and describes trust in AI accuracy as “a top adoption barrier.” CMSWire also argues that making teams confident enough to act is harder and more valuable than optimizing delivery time alone. So buyers need to establish why the output is reliable enough for its intended use.

The importance of that question varies with an organization’s willingness to use AI. CMSWire describes attitudes ranging from aggressive adoption to fear of exposing company systems to the technology. Skepticism about accuracy and doubts about AI’s ability to produce meaningful change can both prevent adoption; in discussing those concerns, CMSWire describes trust in accuracy as “the top adoption barrier.” A faster answer leaves those concerns unresolved.

Addressing them requires an explanation the buyer can use internally. A vendor can help the team explain why AI fits the problem and justify its proposed use to colleagues responsible for the consequences. Internal communication therefore becomes part of the evaluation. It also gives the vendor and buyer a more useful question to discuss: what does the organization need before it will accept an output and act on it?

Set human authority before automating decisions

Accepting an output is one decision; allowing a system to act on it is another. A team can trust a finding while still requiring a person to approve the response because the consequences demand human judgment. A “human in the loop” means keeping that judgment and authority within an AI-supported process. In Tamminga’s account, the appropriate amount of automation depends on the company’s tolerance for risk.

Tamminga illustrates that approach with AI auto-responders: “Take AI auto-responders as an example. The technology works because teams can set rules based on their company’s specific level of risk tolerance. For example, being able to control how clear guidelines and parameters allows AI to handle low-risk, five-star reviews automatically, while allowing more nuanced or emotionally charged issues to get routed to humans,” he says. The order matters: the company establishes its risk tolerance, then sets the rules for automated responses and human involvement.

Those rules give buyers concrete boundaries to examine. The evaluation should establish which decisions the business retains, which conditions send an interaction to a person, and how those conditions reflect its risk tolerance. Limited autonomy can make a proposed use acceptable to the people responsible for its consequences. The organization can therefore choose a narrower scope when broader automation would give the system more authority than it is willing to allow.

Make the customer value clear enough to approve

Even within acceptable boundaries, an initiative needs support from the people who authorize it. Tamminga identifies communication as a reason work stops: “Things stall out when you can’t communicate the value of the improvement for your customer,” he says. A potential improvement therefore needs an explanation that other stakeholders can evaluate. The team may understand the opportunity and still lack permission to pursue it.

Financial stakeholders need that explanation to assess the proposed spending. Tamminga points to financial planning and analysis teams and the chief financial officer (CFO): “You can have all the ideas in the world, but if you can’t get the attention of your FP and a team or CFO, it’s hard to get anything approved.” Identifying an opportunity and securing support take separate work. The proposed use must connect clearly to a customer improvement so those approving expenditure understand why it matters.

Making that connection means explaining what would change in the business. The team needs to describe how AI would affect its interpretation of feedback, its choice of next steps, and the resulting customer experience, with measurable effects that stakeholders can assess. This gives financial decision-makers a more concrete basis for approval than analysis speed alone. The vendor can help the buyer build that internal case.

Connect feedback analysis to customer follow-through

The internal case becomes more concrete when it follows feedback through the business. Companies collect and track information across the customer journey, building a more detailed understanding of their customer base. They then need to combine that information, interpret it, and identify buying patterns. The intended outcomes include better processes, an improved customer journey, and greater customer acquisition and retention.

Combining information requires connections between the systems different teams use. Alchemer connects feedback information across customer relationship management (CRM), support, and analytics systems. These connections bring together information from different parts of the business, but teams still have to understand it and decide what to do. The operational value depends on how the combined information changes a response to the customer.

The volume of information can make that decision harder. Tamminga describes CX teams becoming overwhelmed by data, which inhibits action and weakens confidence about where to focus. In his account, AI can identify broad themes and make the feedback easier to interpret. Teams can then use those themes to decide which response the feedback calls for.

Tamminga makes the next step explicit: “One of Alchemer’s core principles for AI is to move from insight to action,” he says. In his explanation, AI should help teams determine “what to do next” and follow through with customers. For a buyer watching a demonstration, this means looking beyond a summary or set of themes. The vendor needs to explain which business step the interpreted feedback supports and how that step reaches the customer.

Reaching the customer completes the feedback loop: customer input receives an appropriate response through the business. Recognizing a recurring concern starts the work, while choosing and carrying out the next step completes the operational requirement. Faster findings can still be followed by delays. Examining follow-through helps establish whether analysis time is the constraint the proposed AI use will address.

Match AI tasks to clear safeguards

Once the team understands where work is delayed, it can choose an appropriate task for AI. CMSWire identifies faster decision cycles and repetitive-task automation as useful applications, particularly for clearly specified, structured challenges. Such tasks let the team explain what the system should do and how its output will be used. That clarity connects faster execution to the business objective established at the start.

For work delayed during research and interpretation, possible uses include automating the research process and extracting insights from large datasets. Analyzing complex customer-journey data can also help teams understand patterns across interactions. Where assigning work causes the delay, routing customers to appropriate support agents provides an operational next step. Each application needs to be assessed against the task it performs, the output it produces, and the decision that follows.

The decision that follows determines where safeguards belong. After choosing a task, the business needs to establish which activities AI can perform and where people must retain judgment, with particular attention to complex or high-stakes situations. The auto-responder example shows how risk-based rules can divide that work. For any proposed deployment, the team must then assess the business implications of the result and decide what action is justified before allowing it to proceed.

Final thoughts

Faster analysis is useful when it removes a delay that matters to the business. If the obstacle is uncertain accuracy, unclear ownership, or a lack of approval, speed alone will not resolve it. Leaders evaluating an AI vendor need to establish which constraint they are trying to address before choosing the technology.

The evaluation should follow a proposed use from the business problem through to the customer response. What decision will the output support? Why should the team trust it? Which actions can the system take, and which require human approval? The answers give leaders a basis for setting scope, assessing risk, and explaining the investment.

A vendor’s value therefore extends beyond producing insights. It includes helping the organization use those insights within its operating rules and connect them to measurable customer improvements. The buying decision should rest on whether that path is clear enough to act on, not simply on how quickly the system produces an answer.