EXECUTIVE INTERVIEW
Third— and this is the one I think gets underestimated the most— is governance and data quality. Everyone wants AI-driven insight, but very few organizations have the clean, structured, compliant data pipelines to make those insights reliable. Globally, we ' re seeing a wave of companies that bought the AI tool before they fixed the data and process foundation underneath it. That sequencing problem is going to show up as a wave of stalled or quietly abandoned AI initiatives over the next couple of years— not because the technology failed, but because the groundwork was never there.
DO YOU THINK AI IS SERVING TO IMPROVE ACCESSIBILITY AND EFFICIENCY? DO WE STILL HAVE MORE WORK TO DO TO MAKE THIS AN OPERATIONAL SUCCESS?
A: Without question, yes— AI has made customer service meaningfully more accessible. Real-time translation and transcription are breaking down language barriers, voice-totext and sentiment tools are helping agents better support customers with hearing or speech differences, and intelligent routing means customers reach the right resource faster instead of being bounced between queues.
But accessibility and efficiency gains aren ' t automatically distributed evenly, and that ' s where the industry still has real work to do. A lot of AI deployments are optimized for the median customer and the median interaction, and they can quietly make things harder for customers with complex needs, non-standard accents, or several overlapping issues in one call— exactly the customers who need the most help, not the least. Getting this right takes deliberate design and continuous human oversight; it doesn ' t happen automatically just because a model gets deployed.
AS BUSINESSES LOOK TO COMBINE OUTSOURCING WITH AI, WHAT DOES IT TAKE TO MOVE BEYOND SIMPLY AUTOMATING TASKS AND ACTUALLY REDESIGN CONTACT CENTER OPERATIONS FOR GREATER EFFICIENCY, QUALITY, AND GROWTH?
A: Combining outsourcing with AI only pays off when you treat it as an operating-model redesign, not a cost-reduction project. Too many organizations approach this as“ automate what already exists”— take the current workflow, the current org chart, the current KPIs, and bolt AI onto the edges. That gets you a 10-15 % efficiency bump at best, and it ' s the ceiling most transformation initiatives are quietly hitting right now.
The real gains come from asking a harder question: if we were designing this operation today, with AI-native tooling, what would the structure, staffing model, and quality framework actually look like? In our own operations, we ' ve found that consolidating specialized teams into fewer, larger hubs— rather than spreading thin generalist teams across many small sites— produces measurably better outcomes: faster escalation handling, tighter knowledge-sharing, sharper quality calibration. That ' s the same principle AI-era contact centers need to apply to themselves. Don ' t automate the old structure— redesign the structure around what AI and skilled people can now do together.
"... I DON ' T THINK THE WINNING MODEL IS“ AI VERSUS AGENTS.” IT ' S AI HANDLING VOLUME AND CONSISTENCY, AND AGENTS HANDLING JUDGMENT AND TRUST..."
AS AI BECOMES MORE EMBEDDED IN CONTACT CENTER OPERATIONS, HOW DO YOU SEE THE ROLE OF PEOPLE EVOLVING, AND WHAT BAL- ANCE BETWEEN AI AND HUMAN EXPERTISE DO YOU BELIEVE BUSINESSES NEED TO ACHIEVE THE BEST CUSTOMER OUTCOMES?
A: I don ' t see this as AI replacing agents— I see it as AI absorbing the parts of the job that never needed a human in the first place: looking up information, filling out forms, summarizing a call, scoring a transcript against a QA checklist. That frees agents to spend their time on what people are still demonstrably better at: reading emotional nuance, de-escalating a frustrated customer, and handling the genuinely novel problem that doesn ' t fit a script.
Businesses getting this balance right are investing as much in upskilling their agents as they are in the AI itself, turning agents into higher-judgment problem-solvers supported by AI, rather than treating AI as a slow-motion replacement plan. Long-term, I don ' t think the winning model is“ AI versus agents.” It ' s AI handling volume and consistency, and agents handling judgment and trust— and the moment either side creeps too far into the other ' s territory, the customer experience is the first thing to suffer.
Linda Harden is President and Founder of Pipeline Publishing Group and Contact Center Pipeline magazine.
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