SalesTechStar Interview with Charlie Lawhorn, Chief Digital Advisor, World Wide Technology

Charlie Lawhorn, Chief Digital Advisor, World Wide Technology comments on the growth and increasing dependency on generative AI:

_________

Welcome to this SalesTechStar chat Charlie, tell us about yourself – how has your journey in the B2B tech market been? We’d love to hear about your role at World Wide Technology. 

In my role, I help our global clients refine their digital business strategies and work with them to translate those strategies into executable plans that drive positive business outcomes.

My team and I focus on business outcomes and results, then help apply the best processes and technology to drive business value.

I’ve spent most of my career shaping and managing the execution of complex transformation programs that help organizations understand the combination of process design and digital technologies to spur their relevance in an increasingly data-driven digital marketplace. I’ve led global strategy, sales, marketing, communications, customer success, analyst relations, advisory boards, alliances, delivery and strategic account organizations throughout my digital and technology focused career.

Read More: SalesTechStar Interview: Featuring Gong’s Latest Product – Engage: with Eilon Reshef, Co-Founder and CPO

What are your thoughts about the emerging use of generative AI and how you’ve been seeing brands use it to drive brand engagements?

Generative AI is a great opportunity for brands to enhance productivity and create operational efficiencies that impact the way consumers engage with the brand. Right now, customer service tools are one example of an area where AI has a strong use case for disruption. Harnessing the power of conversational AI helps enable brands to deliver quick and accurate responses to queries, which ultimately has a positive impact on how consumers engage with or perceive the brand.

However, amplifying the customer experience holistically with consumer-facing generative AI solutions is not within reach for many brands today. Generative AI holds the potential to enhance the customer experience in search tools, personalized offers, ad campaigns, and more – but for small and mid-sized brands, ensuring the IT infrastructure in place can support this type of deployment is a critical first step. Many brands are grappling with segmented data across marketing, finance, supply chain, and merchandising. Within that construct, it is extremely challenging to tap the full power of generative AI without effective unification of data and systems.

In what ways can B2B sales and marketing teams use generative AI to further their personalization efforts?

Beyond automation in customer service, there is a significant opportunity to leverage generative AI to enhance product descriptions and customize language to ensure products are searchable and presented in a personalized way. A product description written by the manufacturer for one retailer might not be appropriate for another, and generative AI provides teams with an efficient way to tailor language and ensure the message resonates with the intended audience. Increasingly, brands are juggling more unstructured data, and it creates challenges in how they build the search capabilities on their commerce sites. Generative AI can enable brands to not only build individual spec descriptions for product, but it can also help the merchant use the natural language of the audience to optimize search results and put the right products in front of the customer.

Can you discuss some of the prominent ways in which retail today is using generative AI to drive experiences?

Across every retail segment, generative AI is poised to disrupt digital commerce. Brands are now under pressure from customers to deliver personalized and compelling online experiences, and some retailers are exploring ways to enhance operations to meet that demand. So far, we are seeing retailers create efficiencies in processes that drive experiences but occur behind the curtain.

However, within the grocery segment, some data-first brands stand out as early movers in creating experiences that integrate generative AI. For example, tools with predictive capabilities are making an impact on the digital experience. E-commerce is growing in grocery and to create a personalized shopping experience, grocers can capitalize on the opportunity to use generative AI to predict what shoppers will put in their carts based on previous behavior. For instance, the platform might understand that one shopper has a family with certain dietary restrictions. From there, the platform may deliver an ad or suggest a quick-add item that makes it faster for the customer to get everything they need – providing convenience for the shopper, and profits for the retailer.

If there are five things that retailers and end users should keep in mind when integrating AI to enhance the overall CX, what should they be?

The unification of data and systems should be a top priority for retailers exploring the integration of generative AI. During the pandemic, retailers were forced to go digital, but many did not have the infrastructure in place to do so. Often, the result of this was to deploy multiple solutions to band-aid points of friction and stand up an e-commerce ecosystem quickly. Now, to properly train and maintain generative AI-powered technology, brands must have their data house in order.

There are several other considerations that brands should keep top of mind if they are pursuing generative AI:

• Large-scale data storage: generative AI requires massive datasets for training capabilities, and as the tools learn, they may require even larger sets of data. Retailers need to be mindful of this as they look to scale AI-powered tools.
• Scalability: rapid scaling to accommodate the resource demands of generative AI applications puts strain on infrastructure and can increase the points of friction in the customer experience. Ultimately, if not managed properly, it can lead to cost inefficiencies and a poor customer experience.
• Talent: training large-language models to power generative AI demands a team of highly skilled specialists, including infrastructure engineers, data engineers, solution architects and data scientists. This is important for retailers to consider as they may need to invest in talent before deploying more tools.
• Expanding demand: the more retailers integrate generative AI into their customer experience, the higher customer demand will rise. Retailers must be cautious as they deploy generative AI-powered tools as attaching them to the experience without considering the IT implications will likely lead to challenges down the road. Both business and IT teams need close alignment so that expectations of the experiences to be delivered are in line with the technology and skills capabilities of the organization.

Read More: Why Merchants Should Embrace Omnichannel Payments Now