How to Evaluate an AI-Powered CX Platform

How to Evaluate an AI-Powered CX Platform

Evaluate AI in CX on your own data, with your own questions, scored on the follow-up rather than the first answer. Most demos are rehearsed on curated data and every vendor looks strong. The differences appear when you ask something nobody prepared for, ask why three times, and require the evidence behind each claim. Build the evaluation around that and the shortlist sorts itself.

Decide What You Are Buying First

Most failed AI evaluations in CX fail here, before a single demo.

Operational AI makes the service function more efficient. Auto-categorisation, smart routing, suggested replies, summarisation, deflection. It is measured in cost per contact, handle time and deflection rate. The buyer is the CX or operations leader. The business case is straightforward and the payback is quick.

Intelligence AI answers business questions from customer conversation. Why customers leave, what to build, which competitor gap is open, what risk is forming. It is measured in decisions changed. The buyer is the CEO, CMO or CCO, and the budget usually sits outside operations.

Both are legitimate. They are not alternatives and they do not compete for the same budget. Running one evaluation across both produces a scorecard where a tool that deflects tickets well is compared against a tool that answers boardroom questions, and the result is noise.

“Intelligence should never depend on one vendor.”

Decide which you are buying. If the answer is both, run two evaluations.

The Five-Step Evaluation

Step one: write your questions down first. Before any vendor conversation, list the five to ten questions you want answered. Real ones, in the words your leadership uses. This list is your evaluation specification, and writing it before you see a demo stops the demo from shaping what you think you need.

Step two: insist on your own data. A month of real conversations, anonymised if required. Every vendor handles this differently and how they handle it is itself information. A short paid pilot is normal and worth the money; it is the cheapest insurance against a twelve-month mistake.

Step three: run the questions, then the follow-ups. Ask your question. Then ask why. Then ask why again. Then ask what it puts at risk next quarter. Most tools answer the first well and degrade sharply from the second. Score each level separately.

Step four: require the evidence. For every claim, ask to see the customer conversations underneath it. Read a few. Check they say what the summary says they say. This single step disqualifies more products than any other.

Step five: test the unhappy paths. What happens when a connected system is unavailable, when the data is thin on a topic, when the honest answer is “not enough evidence”? A system that always produces a confident answer is a system that will eventually produce a confident wrong one in front of your board.

Three Claims to Scrutinise

“Our accuracy is high.” Accuracy at what? Sentiment classification, theme extraction, intent detection and conclusion quality are different things with different error profiles. Ask what is measured, on what data, and how errors present. A system that is ninety percent accurate at sentiment and poor at theme extraction will mislead you in exactly the places that matter.

“Integration is easy.” Ask which specific systems, by what mechanism, who performs the work, how long for the first and how long for the fifth. Open standards make later connections cheap. Bilateral integrations do not, and the difference shows up in year two.

“Your data stays yours.” Ask three sub-questions: where is it processed and stored, is it used to train anything shared with other customers, and what exactly leaves on exit and in what format. Get all three in writing. The third is the one most teams forget and most regret.

Who Should Be in the Room

The business owner of the questions. Whoever is accountable for the decisions the answers will change. Without them the evaluation becomes a technical comparison and the winner is whoever demos best.

IT or architecture. For connection mechanism, governance, residency and portability. These are not procurement checkboxes; they determine your options in three years.

Someone who will use it weekly. Executive sponsors evaluate on the vision. The person who opens it on a Tuesday morning evaluates on whether it is usable.

Procurement, late. Commercial terms matter, and bringing them in during problem definition produces a feature-matrix purchase.

How Konnect Insights Approaches It

Konnect Insights is a vendor, so treat the following as the tests to run on us as much as on anyone.

The operational and intelligence layers are deliberately separate. AI Essentials and Agent Empower do the operational work: categorisation, routing, agent assist. KRC does the intelligence work: open business questions across the omni-channel record and, through MCP, across the wider front office stack. They are evaluated differently because they are different purchases.

On the five steps: pilots run on customer data rather than demo data, every KRC answer carries the conversations behind it, and follow-up questions are the normal mode of use rather than a stretch case. On the three claims, ask us the same sub-questions: what accuracy is measured on, which systems connect by which mechanism, and what leaves with you if you go. Data residency covers India, UAE, Saudi Arabia, Germany, the UK and the US, which is usually where the processing and storage question lands.

A vendor uncomfortable with step four is telling you something. That applies here too.