Get Your AI Pilot Off to the Best Possible Start

Sometimes you’ve got to “go slow to go fast”, as the saying goes, and this principle certainly applies to the success (or otherwise) of any AI deployment; where taking small, considered steps during the pilot phase can pay dividends later on.

That’s why we asked our panel of technology experts for their best advice on what those key steps are, all to help you see visible ROI on your investment – and avoid an embarrassing “crash and burn” scenario.

Augment Before You Automate

The pilots that build lasting momentum don’t start with customer-facing AI. They start behind the scenes – in the hands of agents and quality teams – where the stakes of a wrong answer are lower and the feedback loop is faster.

Use cases like automated interaction scoring, post-call summarization, and real-time knowledge retrieval all share the same characteristic: a human reviews the output before it matters.

That human-in-the-loop posture isn’t a compromise – it’s a design principle. It keeps agents in control, makes errors visible and correctable, and builds the internal confidence needed to go further. The contact centres that scale AI successfully almost always start with a co-pilot model, not an autopilot one.

Contributed by: Chris Mounce, Product Training & Enablement Specialist, evaluagent

Monitor Customer Feedback, Abandonment Rates, and Transfer Patterns Closely

When AI pilots directly affect customers, for example with tools like virtual assistants, voicebots, or proactive messaging, success depends as much on customer acceptance as on technical performance. Start small, so you can test not just functionality, but trust.

Introduce AI in low-risk, well-defined scenarios first, such as handling simple FAQs or providing out-of-hours support.

Be transparent about when customers are interacting with automation and ensure escalation to a human agent is seamless. Early friction often stems from containment being prioritized over experience.

Also monitor customer feedback, abandonment rates, and transfer patterns closely. A pilot should assess sentiment and behavioural response, not just deflection metrics.

By gradually expanding scope based on proven acceptance and clear service improvements, contact centres can build familiarity and confidence, showing your AI to be a genuine benefit – rather than a purely cost-saving measure.

Contributed by: Carl Townley-Taylor, Product Manager, Enghouse Interactive 

Establish a BAU Baseline to Help Compare Performance After Implementation

AI implementations need to be able to prove measurable value. Research by MIT found that while 95% of generative AI pilots fail, the 5% projects that succeed tend to focus on back-office automation, an area closely tied to CX. 

AI can take on much of the routine admin work that often consumes over 50% of a contact centre worker’s time, from capturing customer intent in the queue to transcribing conversations and surfacing relevant information in real time.

For straightforward issues, intelligent self-service frees up human agents to focus on complex or sensitive interactions, boosting their productivity while enabling more meaningful, empathetic support.

To demonstrate ROI, organizations must first establish a business-as-usual baseline using key metrics such as Average Handling Time (AHT) and First Contact Resolution (FCR), then compare performance after implementation to clearly measure impact.

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

Design for Scale From the Start

Even a small pilot should be built with expansion in mind. Define success criteria upfront, track baseline metrics, and document what works and what doesn’t. Ensure that the data, workflows, and integrations used in the pilot can extend to other teams and use cases.

Just as importantly, involve stakeholders early so they understand the value being created. A strong pilot doesn’t just prove a concept – it creates a repeatable model that can be rolled out across the organization with confidence.

Contributed by: Tatiana Polyakova, COO, MiaRec

Allow Agents to Review Post-Call Summaries to Build Their Confidence

Successful AI integration in the contact centre depends on more than just the technology; it relies on human trust. To maximize ROI, agents must learn for themselves that the technology supports them – rather than being asked to blindly trust the outputs.

A low-risk starting point is something simple like automatically summarizing the wrap notes. By using AI to generate post-call summaries, you immediately reduce wrap time by around 50%.

Allowing agents to review and edit these summaries before submission keeps the human in the loop. This builds confidence in the accuracy of the system.

Once this trust is established, organizations can transition to more complex agent assist applications, such as real-time knowledge base suggestions. This approach creates a foundation of reliability and trust, eventually smoothing the path toward more advanced use cases.

Contributed by: Luke Cuthbertson, Head of CX Consulting Practice, Route 101

Keep the Pilot Short (Around 4–6 Weeks) and Agree Success Measures Upfront

AI pilots work best when they’re designed to deliver one clear win. Start by choosing a single team and a single call type where friction is obvious, like wrap-up time, inconsistent notes, or slow QA.

Then pilot one AI capability that removes that friction, such as call summarization, automated dispositioning, real-time guidance, or QA support.

Keep the pilot short (around 4–6 weeks) and agree success measures upfront: reduced after-call work, improved quality scores, stronger compliance, faster onboarding, or better customer outcomes.

When the scope is tight, teams feel less disruption and leaders get clean data they can trust. Prove value in a controlled environment first – then scale what works with confidence.

Contributed by: Ben Booth, CEO, MaxContact

Evaluate Resolution Accuracy and Customer Satisfaction – Not Just Cost Reduction

Most CX leaders struggle with the same question – “What should we automate first?”. The most successful AI pilots begin with narrow, well-defined use cases grounded in real operational data.

Instead of launching broad automation initiatives, contact centres should analyse historical conversations to identify high-volume, repeatable interactions such as account updates, order status checks, or simple policy enquiries. These workflows provide a controlled environment to test AI performance while delivering immediate value.

A strong pilot also requires clear success metrics. Teams should evaluate outcomes such as goal completion, resolution accuracy, containment, and customer satisfaction rather than relying on cost reduction alone. This ensures the pilot measures real customer experience impact.

Contributed by: Ashish Nagar, CEO, Level AI

Resist the Urge to Simply Rebuild Broken Legacy IVR Flows

While automation is the typical starting point for AI in contact centres, it is often the most expensive and time-consuming route.

For a faster return on investment, I recommend beginning with insights and analytics – especially as a conversational analytics platform can be deployed in days, not months or years.

This approach allows you to understand the “Voice of the Customer” and identify operational friction before committing to automation.

By grounding your strategy in data first, you avoid the common mistake of using expensive agentic AI to simply rebuild broken, legacy IVR flows.

Starting with analytics ensures that when you do move toward AI assistants and automation, you are building a truly transformational experience – rather than just automating old problems.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

Be Specific About Your Requirements When Choosing a Vendor

Assess the market! There are many vendors available, so speak to a few, get some demos, and understand what they’re offering. Be specific about your requirements, especially when it comes to AI. It’s a very generic term; we need AI.

Ask vendors what AI functionality their product offers, how it is applied, and what outcomes it delivers. This helps cut through the noise and ensures alignment with your goals.

Finally, review existing processes. Focus on the long, manual processes that take up the most time, and use the pilot as an opportunity to challenge whether they should stay.

Question the vendor you’re considering about how their product can help to solve those processes and what impact you can expect, to set real expectations.

Contributed by: Zaineb Ahmed, Marketing Manager, Peopleware

Celebrate Quick Wins Visibly to Build Momentum for Broader Roll-Out

Contact centre leaders should begin with one workflow with clear boundaries, like automated post-call summaries, and define the KPI the pilot must improve.

​​Teams can then build quickly using low-code tools and test within a limited environment, like a specific customer segment, where performance can be monitored without disrupting wider operations.

Safeguards should be routinely evaluated and updated as the pilot scales. New capabilities should be tested thoroughly before full deployment. For example, AI-generated responses should be reviewed by agents before being sent to customers in early pilots.

Pilots work best when they reflect real operating conditions. Integrating them with existing channels, CRM systems, and reporting tools ensures teams improve current workflows – rather than creating separate tech layers. Also make a conscious effort to celebrate quick wins visibly to help build momentum for broader roll-out.

Contributed by: Rodney Hassard, Head of Product, Applications, Vonage

Do Not Ignore Tougher Questions in Favour of the ‘Happy Path’

AI options are fast moving, but starting small with pilots, thinking big but importantly moving fast, will deliver success.

Begin with tightly scoped pilots that solve clear, low-risk problems that deliver actual value. This helps teams visualize what AI can unlock.

Early pilots often focus on internal use cases such as agent assistants to reduce handling times, or cross-functional work with HR, IT or Finance to build wider buy-in. Engage stakeholders early across operations, IT, infosec and frontline teams to avoid friction later and create a sense of shared ownership.

While ‘happy path’, non-production demos can spark excitement, do not ignore tougher questions, so be clear on data security, governance and how solutions will scale with a path to production.

Stay laser-focused on success metrics and customer impact from day one. Momentum comes from proving value quickly, then scaling with intent rather than adding complexity too soon.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

What Helped to Make Your AI Pilot a Success?

Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

For more great insights and advice from our panel of experts, read these articles next:

• Upgrade How You Listen to Customer Feedback
• Where Self-Service Scheduling Tools Have the Biggest Impact
• Don’t Let Tech Adoption Be an Afterthought

Author: Megan Jones
Reviewed by: Xander Freeman