As the founder of Quiq and a veteran in the CX technology space, I recently had the pleasure of joining TTEC Digital‘s Experience Exchange Series over on LinkedIn Live to discuss one of the most pressing questions facing enterprises today:
Should organizations build their own AI solutions or buy existing ones?
In my conversation with Tom Lewis, SVP of Consulting at TTEC Digital, we explored this complex decision-making process and its implications for customer experience success. Here’s an overview of our discussion —and the highlights of our conversation if you missed it.
My key takeaways:
- Assess your organization’s capabilities and resources honestly before deciding to build or buy
- Ensure strong collaboration between CX and IT teams
- Prioritize knowledge and data quality and governance for both building and buying
- Consider a hybrid build and buy approach when appropriate
- Maintain focus on risk management and compliance
- Stay adaptable as technology evolves and keep your eye on prize AKA CX outcomes
Understanding the AI build vs. buy dilemma.
The rapid advancement of AI technology has created both opportunities and challenges for enterprises. While the promise of AI to transform the customer experience is clear, the path to implementation isn’t always straightforward. Organizations must carefully evaluate their resources, capabilities, and objectives when deciding between building custom AI solutions or purchasing existing platforms.
When considering the build approach, organizations gain complete control over their AI solution and can tailor it precisely to their needs. However, this comes with significant investments in time, talent, and resources. During our discussion, I emphasized that building in-house requires not just initial development capabilities, but ongoing maintenance and governance of the system.
On the buy side, organizations can benefit from immediate deployment, proven solutions, and regular updates from vendors who specialize in AI technology. The trade-off here might be less customization and potential dependency on third-party providers.
Bridging the gap with IT.
One crucial aspect we explored was the importance of alignment between CX leaders and IT departments. Success in AI implementation requires a collaborative approach where both teams understand:
- Technical requirements and limitations
- Integration capabilities with existing systems
- Data security protocols
- Scalability needs
I shared that the most successful implementations often occur when CX and IT teams establish clear communication channels and shared objectives early in the process.
Data and knowledge are the foundations of AI success.
Regardless of the build or buy decision, data preparation and having the right knowledge in your knowledge base to train the AI is crucial. Organizations need to:
- Audit existing data quality and accessibility
- Establish data governance frameworks
- Ensure compliance with privacy regulations
- Create clear data management protocols
During our conversation, I stressed that the quality of AI outputs directly correlates with the quality of input data. We recently released a guide on 3 Simple Steps to Get Your CX Data Ready for Quiq — I highly recommend you check that out for more actionable tips on data readiness.
Don’t let perfect be the enemy of good.
Many CIOs are concerned that it’ll take years to prepare their knowledge and data for AI. To that, my advice is: ‘Don’t let perfect be the enemy of good.’ You’ve got to start somewhere, and there is sure to be a crawl-walk-run framework you can devise with the data available to you now. It’s all about identifying and isolating a first use case.
My other piece of advice to CIOs who may be inundated with AI data concerns is to get your hands dirty and start using AI. Get started with an implementation that you don’t expect to last a whole five years, but are rather expecting to learn, iterate, and fail forward from. Now is the time to lean in, not sit back—even if things are not perfect to start.
Managing risk and ensuring compliance.
One thing I highlighted to Tom was that AI is not super valuable to your business all by itself. What makes it so is combining it with your company data. And that means risk management is paramount. Key considerations include:
- Data privacy and security
- Regulatory compliance
- Transparency in AI decision-making
When planning for risk management and compliance, organizations can build trust by:
- Implementing robust security measures
- Maintaining clear communication about AI use
- Regular auditing and monitoring of AI systems
- Establishing clear governance frameworks
What happens when AI creates delightful experiences that customers want to interact with even more?
Tom’s theory is that if you make communicating with a brand effortless, consumers will interact with that brand more, not less. I not only agree, but I think it’s a goal brands should strive for.
Customers are more likely to self-service via AI-powered conversations than on the phone or in person, especially when it comes to the minutia of decision-making. For example, a customer is more likely to ask “What’s the sofa frame made out of?” when evaluating a furniture purchase over chat or their digital messaging channel of their choice. These types of questions are not usually the ones people pick up the phone or march into a physical store to ask, but they are the kind of conversations that lead to more purchases.
Similarly to how retail clerks are ever-present for customers to ask questions, AI that understands and responds to natural language can create even more delightful experiences that build relationships and brand loyalty while driving more revenue.
Final thoughts and looking forward.
The path to AI implementation in CX isn’t one-size-fits-all. Success lies in making informed decisions based on your organization’s unique needs, capabilities, and objectives. Whether building or buying, the focus should remain on delivering value to customers while managing risks and resources effectively.
That said, this technology is exciting, moving fast, and stands to deliver on its promises when done correctly. In fact, I think in the next five years, there’s going to be a shift in customer perception that AI provides even better service than human agents.
Want to listen to my whole conversation with Tom? Check out the replay here.
