Conversational AI: Navigating the build vs. buy decision

Conversational AI is no longer a novelty — it's a strategic component of nearly all leading customer experiences. 

In a recent Experience Exchange hosted by Tom Lewis of TTEC Digital, Mike Myer, the founder of Quiq, shared valuable insights from his extensive experience in the CX space. 

The discussion explored key considerations for organizations as they adopt AI, including the classic 'build vs. buy' debate, integration challenges, and the evolving expectations of customers. 

Here’s a breakdown of the conversation and what it means for leaders in customer experience.

1. Build vs. buy: a complex decision simplified

The build versus buy debate is common for companies venturing into AI. According to Myer, this dichotomy is often misunderstood as a binary choice. In reality, it’s more nuanced. Organizations may have strong arguments for both sides, sometimes leading to internal strife between departments. While IT might favor building for customization, business leaders may doubt their capability to deliver on time or effectively.

“There are a lot of reasons for both sides,” Myer explained. “It’s not just a simple choice—it’s about aligning IT capabilities with business goals.”

The takeaway? Leaders need to evaluate not only technical capacity but also strategic objectives and potential for collaboration across teams.

2. The shifting power dynamics in AI strategy

The conversation revealed an interesting shift: decision-making for AI deployment has become more cross-functional. Previously, CX leaders could spearhead technology decisions, bringing IT in at later stages for implementation. However, as AI becomes more central to business strategy, IT's role has grown significantly. Myer noted that in recent conferences, half the attendees admitted needing IT sign-off before moving forward with AI projects.

“AI is so strategic that everyone wants a seat at the table,” said Myer. This reflects a broader trend where AI governance is being integrated at higher levels, with dedicated councils or compliance teams emerging to oversee AI projects.

3. What problem does AI solve for customers?

Myer was keen to emphasize the core purpose of conversational AI: enhancing customer interactions. While generative AI holds great promise for streamlining service, it’s essential to understand that not all scenarios benefit equally from AI-only solutions. Myer outlined three main touchpoints for AI:

• Direct interactions with customers.
• Assisting human agents to increase efficiency.
• Enhancing backend systems with advanced processing capabilities.

This multi-layered approach ensures businesses can leverage AI without sacrificing human oversight where it’s still needed.

4. Addressing declining customer satisfaction

One intriguing point Lewis brought up was the apparent paradox: while technology advances, customer satisfaction in interactions is declining. Myer attributed this to subpar AI implementations that don’t meet user expectations. For instance, if an AI assistant fails to provide an answer as effectively as simple, widely-used tools like ChatGPT, customer frustration ensues.

The solution, according to Myer, is targeted deployment. “Capabilities are ready for prime time, but use cases matter,” he stressed. Companies need to start with clear, effective applications and expand as they refine their approach.

5. The importance of data integration

A major challenge facing CIOs and IT leaders is the integration of AI with existing systems. Myer highlighted that AI alone isn’t valuable — it’s AI combined with business data that drives impact. He advised against waiting for perfect data conditions before starting an AI project.

“Don’t let perfect be the enemy of good,” Myer recommended. Instead, begin with practical, attainable use cases that can deliver value quickly. Low-hanging fruit, such as automating routine customer queries, can be an ideal starting point while more comprehensive data strategies are developed.

Charting your AI path

The future of AI in customer experience holds immense promise, but success depends on thoughtful strategy and execution. From navigating internal dynamics to ensuring seamless data integration, the insights shared by Myer serve as a vital guide for leaders determined to harness AI's power without falling into common traps.

As host Tom Lewis pointed out, "Your opportunity cost is growing every day that you wait. A lot of organizations are fairly bureaucratic and slow in this regard. As soon as they make a decision, the technology landscape might also change." With AI technologies advancing at lightning speed, organizations must act swiftly — yet thoughtfully — to leverage these innovations or risk being outpaced by competitors.

To effectively chart your AI path, consider starting with pilot projects that allow you to experiment and learn. Build a strong business case that outlines potential ROI and aligns with customer needs. Engage cross-functional teams to foster collaboration and innovation, ensuring that your AI initiatives are integrated into broader organizational goals.

With a proactive approach, you can position your organization to leverage AI not just for efficiency but also to enhance customer experience. Now is the time to explore how you can begin this journey — every small step can lead to significant advancements in meeting and exceeding customer expectations.