Beyond chatbots: Generative AI’s potential in retail, consumer settings
As published in the CIO Journal for The Wall Street Journal
Generative AI could improve outcomes and ROI for products and services in the retail and consumer industry.
Evolving customer demographics and preferences have reshaped the retail and consumer industry over the years, but these shifts may be a prelude to more dramatic transformations. These settings are being reshaped by several major forces: diversification of consumer preferences, the redefinition of societal and cultural values, and the impact of fluctuating economic and policy landscapes on business strategies. In such circumstances where business agility and innovation can help businesses stay competitive, Generative AI could help revolutionize work processes, providing automated, enhanced, and expedited operations across business functions to support hyperpersonalization.
“In the evolving business landscape, the decisive, competitive edge will be defined by the strategic deployment of processes that are not only ‘always on’ but also autonomous and self-correcting—all at a scalable level, driven by AI,” says Sambit Dutta, the vice president of digital strategy and business transformation at Nestlé USA. “Consider consumer products and goods companies: Those that integrate AI into traditionally human-centric activities such as product innovation can significantly enhance their adaptability to shifts in consumer behavior. This technological infusion could lead to a significant increase in the generation of new product ideas, vastly accelerated time to market, and reduced product costs.”
Indeed, while chat-based applications have been at the forefront of consumer adoption for their text-based, context-aware capabilities, the breadth of Generative AI capabilities reaches far beyond, to areas such as images, audio, video, and computer code. In various business settings, these capabilities can be tactically deployed for content creation, augmentation, summarization, classification, and simplification; question-answer chat and search; and co-pilot assistance for software development.
A Fortune/Deloitte CEO survey released earlier this past fall sheds light on CEO and boardroom discussions about Generative AI: More than half (56%) of surveyed respondents rank increased efficiency or productivity and reduced costs as the top benefits they are hoping to achieve through Generative AI (GenAI), and 58% say they are already implementing GenAI to automate manual tasks. Beyond the C-suite, more than two-thirds of senior business and technical leaders responding to Deloitte’s “State of Generative AI in the Enterprise” survey say they expect the technology to drive substantial transformation within their organization and industry over the next three years. However, a recent study on the chief data officer agenda finds nearly half (46%) of respondents point to data quality and finding the right use cases as two of the biggest challenges in succeeding with enterprise GenAI.[i]
Among the pressing questions for organizations, including those in retail and consumer products, are how and where to use GenAI most effectively, and how to ensure the applications are adopted and scaled to drive business value.
The value of Generative AI
GenAI capabilities can be well suited for tasks that call for humanlike, reason-based decision-making in business functions, including customer operations, sales and marketing, personal productivity assistance and search, product R&D, and software engineering.
The following are use cases to consider for GenAI in retail and consumer products:
Personalization of marketing and product recommendations.
Generative AI can be used to create personalized marketing campaigns and product recommendations for individual customers. This can be accomplished by analyzing the customer’s purchase history, browsing behavior, and other data points. For example, a retailer could use GenAI to create a personalized email campaign for each customer, highlighting products likely to capture their interest. Related GenAI-powered use cases, including marketing content assistants, next-level market intelligence, virtual try-ons, and virtual shopping assistants, may enable organizations to automate repetitive tasks and transform generative tasks.
Creation of new products and services. Generative AI can be used to create new products and services that meet the needs of customers in new and innovative ways. For example, a clothing retailer could use GenAI to create new designs tailored to individual customer preferences. Whereas trial and error in product development can be resource-intensive, GenAI could accelerate rapid prototyping by analyzing vast data sets, deriving insights, and suggesting product iterations or entirely new concepts.
Enterprisewide data search and access. In the consumer industry, facilitating efficient querying across diverse databases can be a challenge. Generative AI can act as a powerful interface, simplifying communication between users and multiple dispersed databases and enabling more efficient querying and data democratization.
Code assists for developers and data access for all. For developers, Generative AI-based co-pilots can automate tasks such as code deployment and maintenance across multiple platforms, acting as intelligent assistants for coding, helping to ensure consistency, and adapting code to different environments. On the data front, this can democratize access, simplifying data analysis and mining with user-friendly interfaces and natural language querying, allowing stakeholders from all business functions to more effortlessly derive actionable insights. Furthermore, it can break down data silos, aggregating information from diverse sources for a holistic view of consumer behaviors, guiding decision-makers to critical areas of focus.
Enhancement of customer service. Generative AI can revolutionize customer engagement through conversational agents, providing empathetic and personalized interactions for after-sales support and complaint handling. This approach can enhance resource allocation by quickly addressing customer queries with relevant solutions, allowing human agents to concentrate on more intricate issues, leading to quicker resolutions and improved customer satisfaction.
Organizations can be well served to evaluate GenAI use cases through the lenses of value, feasibility, and risk according to the following framework:
Key considerations for GenAI
Organizations should consider evaluating their readiness before embarking on GenAI initiatives. The following considerations are especially important:
Build the business case and bring a value-realization lens. Generative AI journeys should start with a clear vision. Articulate the challenges the technologies could address and the measurable value they could add. This approach can not only help garner stakeholder buy-in but also help align goals across the organization.
Make platform and technology decisions (build, buy, or partner). This decision often determines the trajectory of GenAI integration. Developing capabilities in-house likely demands significant resources but offers customization opportunities. External solutions, meanwhile, may promise quick integration but often lack bespoke features. Partnerships can bring domain expertise while also demanding synergy and collaboration.
Enhance data capabilities. At the heart of Generative AI is data. Premium-quality data not only augments the accuracy of large language model outputs but can also dramatically curtail the associated costs. Effective data strategy and management thus emerges as the backbone of successful GenAI use case implementations.
Cultivate talent. Generative AI’s novelty in the tech world implies a dearth of seasoned professionals. This may present businesses with a dilemma: reskill their current teams or scout for talent in a competitive, resource-scarce market. Building a scalable GenAI application requires deeper understanding of business processes and intricate knowledge of organizational context. With the advent of no-code and low-code technologies, it is likely worth considering whether to reskill current teams with newer technologies.
Practice good governance. The intricacy of an emerging technology like GenAI requires strong governance with an eye on ethics, privacy, and human oversight. Businesses should ensure a balance between speed and the considerations above, especially in mitigating potential GenAI hallucinations and fostering trust among stakeholders.
As Generative AI continues its march, altering the very contours of the retail and consumer products industry business functions, businesses that can strategically align with this technology could have the opportunity to better connect with their customers and thrive, while those that falter may risk lagging behind.
—by Ed Johnson, principal, Deloitte Digital, Retail & Consumer Products; Kevin Byrne, principal, Retail & Consumer Products; and Manas Bhuyan, senior manager, AI & Data Leader, Retail & Consumer Products, all with Deloitte Consulting LLP
