Investing in GenAI: A Must-Have Strategy for Future-Ready Brands

GenAI reached its peak hype cycle in 2023. Now that the hype has cooled somewhat, it’s time for B2C and B2B brands to take it seriously. A recent BCG survey found that 71% of the respondents plan to increase their company’s tech investments in 2024 and an even larger percentage (89%) of executives rank AI and GenAI as a top-three tech priority for 2024. It's clear the companies that don’t leverage GenAI to deliver better experiences to their customers will fall behind their competitors. I do understand the hesitancy. When OpenAI first released GenAI there were problems with accuracy, security, hallucinations, and the “free will” factor which made it hard to trust and control. As a result, GenAI was relegated to the role of a friendly chatbot, without yielding tangible results or return on investment.

To achieve a higher return on investment, brands should apply GenAI to use cases that are most valuable, for example, empowering customers to ask questions as part of their product research. In this scenario, GenAI can provide responses to their questions such as, “What is the best lens for wildlife photography?”

While there’s chatter about GenAI taking the place of search, nothing could be further from the truth. GenAI is not AI search’s killer, but AI search is GenAI’s savior, so to speak. In fact, advanced AI-powered search vendors, who already used natural language, semantics, vector similarity and large language models (LLMs), are the only players with the potential to 'ground' GenAI within real business requirements and ensure hallucinations don’t happen. Retailers can harness the power of GenAI for product discovery, to directly increase RPV, conversion, AOV, LTV and brand awareness, but they cannot do this without first investing in AI search.

Simon Langevin

VP of Product
Coveo

Leveraging GenAI for Competitive Advantage

07/05/2024 17:36:49

Retailers are in the business of making money, so first and foremost they need to think about tackling real problems. There's a lot of hype around AI possibilities. Retailers should take advantage of this hype, but not get distracted from key priorities. There’s two ways to think about AI: making products easier to use, and net-new AI features for your business. On focusing on real issues, we’re excited to see Salesforce’s CoPilot making it easier for retailers to use the products they rely on daily, like Service case summaries and the ability to elegantly shorten resolution times on costly service calls. We’re equally excited to see net-new AI product features being tested and moved out of conception and into reality.

Erika McGrath

VP Salesforce Data Cloud & Martech
Astound

Speed is a critical factor when optimizing the customer experience. When a shopper feels “known” and the experience is personalized, they can get their needs met quickly, complete their order, and move on to the next task of the day. So how do we optimize speed? Automation is essential. Explicitly add items to the cart that we expect the shopper will accept, like past purchases and private label. Employ dietary edits as a filter across your experience to remove the lag of manual product edits.

Power the shopping journey with fresh shoppable recipe content to boost cart size and site visits. Leverage AI for product matching to land a perfect cart with minimal out-of-stocks. Finally, bring the need for speed in-store with shoppable recipes that instantly list ingredients and location within the store.

Srinivas Pujari

Corporate Vice President Datascience
Blue Yonder

Practical AI for Retailers

PRACTICAL AI FOR RETAILERS

07/05/2024 18:32:08

AI and Location Intelligence

The Next Level of Retail Operations: AI and Location Data in Action

Location data and AI technology in retail, whether it is within stores or warehouses, is crucial in enhancing customer experiences and also increases efficiency in operations, sales and marketing.

Indoor positioning systems (IPS) can personalize the customer shopping experience within stores. Valuable location data about customer movements, time spent in certain areas within stores can help retailers cater to customer needs better. Store apps can suggest products based on a customer’s location; frequency of store aisle visits and other related customer location data. Retailers can also position product in stores, in the most favorable positions based of customer buying tendencies. Popular products can be located in the most convenient locations, along with other products that customers may tend to purchase with these popular products. Knowing what the busiest times are within a store can also help with staffing needs. Pricing decisions, ordering the right amount of stock and predicting the demand for certain products can all be positively impacted with AI and IPS.

The other areas that retailers could leverage AI to improve customer engagement would be to provide more personalized customer support by tracking customer moods. Operational efficiency can be increased with automated checkouts for certain stores and also by increasing employee satisfaction by providing them with AI assisted tools to perform their jobs to the highest standards.

In addition to stores, warehouses benefit from accurate location data, pick walk sequences based on order and product profiles. Location data can increase picking and shipping efficiencies and hence lead to quicker delivery times to keep customers satisfied.