That is the second article in a two-part series. In the primary article, AlixPartners shared a framework and approach to get on course with Generative AI and drive value for retailers and types. This text explores the keys to impactful implementation of AI in retail.

It’s tempting to leap on the buzzy Generative AI bandwagon amid fears of getting left behind, but in today’s competitive fashion and retail climate, diving in without proper preparation can compromise an organization’s ability to effectively integrate the technology. Recent technical solutions often prove to be a waste of money and time because of flawed or misguided implementation, poor adoption, and/or the dearth of integration with business processes.

Global management consulting firm AlixPartners has devised a self-assessment framework to assist retailers and types look before they leap into AI, nonetheless, accurately assessing needs and skills is just step one. Implementation is commonly the toughest part. Firms must begin with a well-defined strategy and clear objectives, actively measure AI’s efficacy with KPIs, then quickly and continually course-correct as needed. In other words, “fail fast and fail forward,” in response to Angela Zutavern, Partner and Managing Director and AI expert in AlixPartners’ Digital Practice.

Here, we explore basic steps firms must take while applying AI into their workflows, all with the objectives to drive conversions, reduce costs and increase profits and in addition to boost average customer value, acquire recent customers and gain market share. 

Know your AI goals

AI might be applied narrowly or broadly, but many firms make the error of putting the tech before their business concerns. “Experimentation is totally critical for any AI-generated initiative, because not all of them will work the primary time. But initiatives have to be goal-oriented,” said Zutavern.

Firms must first have a clearly defined business case to understand how the AI initiative can impact the business results. Once they’ve those financial metrics up front, they will pilot with a minimum viable product.

“In the event you expect a specific AI initiative to cut back customer churn or increase customer lifetime value, for instance, you’ve got to be measuring that along the best way,” she said. “You wish that historical baseline where you begin after which you should measure it throughout the pilot phase and the implementation phase, to ensure that that you simply are making progress in those metrics that you simply’ve defined up front.”

As a part of its self-defined digital transformation, Spanish retail chain Mango identified a broad range of touchpoints across its value chain that may benefit from AI—from pricing to personalization—and since 2018, it rolled out 15 different platforms to handle them. Midas, for instance, is used for pricing policy and in store, while Gaudí recommends products online. Mindful of its massive international clientele, Mango programmed its AI customer support bot Iris to talk to customers in 20 languages in over 60 countries.

For its most up-to-date initiative, Mango’s goal was to assist employees and partners create—or moderately, co-create. The GenAI images on the Encourage platform help design and product teams envision recent prints, fabrics, garments and more, and Mango already has 20 garments available in the market co-created by AI. On the conversational side, newly launched employee-facing AI platform Lisa generates text a la ChatGPT but is trained specifically for Mango. Developed in only nine months, Lisa utilizes each private and open-source models.

While Mango’s customers already communicate with its conversational agent, Iris, the corporate wanted to enhance its capacities by equipping it with “interactive conversational generative AI, with the intention to allow a more fluid interaction with our consumers,” said Jordi Alex Moreno, Mango director of technology, data, privacy and security. “In other words, we desired to go from a conversational assistant for specific applications to an interactive conversational assistant that may cope with multiple applications in our industrial and social media channels.”

For multi-brand beauty retailer Ulta Beauty, their AI strategy has all the time centered on the shopper.

“The speed through which we adopt AI technologies reflects our guests’ evolving needs and interests, which has only accelerated through the years as beauty enthusiasts turn to digital tools to explore the category,” said Prama Bhatt, chief digital officer at Ulta Beauty. Although the chain was already working on digital innovation, the pandemic accelerated these efforts while in-store product testing was inaccessible. Along with guest-facing solutions like a Virtual Beauty Advisor and GLAMlab virtual try-on, Ulta uses AI internally to “help speed up the speed of asset creation in addition to help cut down time spent on laborious tasks.”

Looking ahead, Bhatt also sees the potential for AI to support store worker training, noting, “In beauty, it may possibly be difficult to be an authority on all categories and products but with GenAI we may find a way to place the extent of experience at their fingertips to teach each guest and associates seamlessly.”

Construct a talented team

Artificial intelligence can learn, but it surely have to be taught fastidiously and managed with skillful oversight. This is especially crucial with consumer-facing initiatives where an offensive chatbot or social media post can sink an organization PR-wise.

To preserve brand integrity across text or images, AI must learn the brand voice for generative marketing pieces and the brand aesthetic for creative outputs, a indisputable fact that could cause concern. In Jasper’s 2023 AI in Business Trend Report, a study of 500 professionals found that “factual inaccuracy” (36 percent) was the most important concern for generative AI, followed by “generic outputs” (35.1 percent) and “outputs that lack correct tone” (26.4 percent).

Smart output comes from smart input, and the growing expertise of “prompt engineering” helps users write prompts for essentially the most meaningful GenAI results. Fashion, which has notoriously lagged in technology, can look outside the industry to the tech industry or college grads expert on this area.

Once teams are in place to show and monitor AI, others have to stay abreast of AI’s fast-moving advances so systems might be adapted as needed. “In all of the projects created in Mango’s Technology Department, we [always took] into consideration the constant innovations being developed within the sector in addition to the speed at which this area is advancing,” said Moreno. “Specifically, inside the sphere of GenAI, recent implementations appear in a short time, and we’ve got to develop solutions which might be scalable to the worth chain, flexible, and permit us to adapt to potential improvements.”

To make this possible, Mango “assembled a multi-disciplinary team with different profiles of user experience, architecture, back/front-end development and data science, along with the involvement of functional areas of the corporate.”

Bhatt also noted the necessity for the proper talent. “To interchange an experience that’s so uniquely personal to beauty enthusiasts like physically trying on makeup with a digital app in your phone was a challenge in itself, but having the ability to staff such an undertaking could be one other hurdle,” she said. Ulta gathered the teams it needed through its acquisitions of GlamSt and QST, which gave it “access to the technical expertise required together with the talented data scientists.”

All this, nonetheless, requires buy-in from the highest. Zutavern said she often gets asked by data science teams how they will have more influence on the business. “I feel it’s just ensuring that the business and technology initiatives are really integrated and that we don’t have a disconnect between what’s happening in technology and what’s happening within the day-to-day business,” she said.

Make processes AI-ready

Maher Masri, president of AI software company NAX Group, noted that one among the largest challenges in implementing AI into workflows is the speed of experimentation. “Innovation in AI will likely be measured in weeks and months, not years. Corporates must create cultures which might be maniacally focused on rapid experimentation at-scale to unlock data in recent ways,” he said.

So while AI has great capabilities, firms must often re-engineer existing systems and protocols to make the most of them. “It’s just like the within the early days of automation,” said Zutavern. “In the event you just automate an old process, you’ll get some but not all the advantages you’ll if you happen to rethink the whole process. Same with AI.”  

And not using a blank check, nonetheless, fashion firms have to be strategic. So where to start?

Masri suggests starting with a view of impact potential across the worth chain and key processes. “The most important economic and strategic opportunities must be prioritized by where proprietary data sets exist that provide an unfair competitive advantage,” he said. “Enterprise valuations will increasingly be centered around an organization’s data story and it will be significant to display growth and productivity impact across a variety of areas across the enterprise,” he said. Data might be used to drive value across several areas reminiscent of supply chain, product development, merchandising, service, etc.

Experimentation is nice, but the bottom line is to measure results along the best way. “Some firms have tons of of AI initiatives in flight but aren’t really sure where the advantages are coming from,” said Zutavern, who recommends quarterly portfolio reviews/adjustments to investigate value and gaps for firms spending thousands and thousands of dollars on AI initiatives, and annual reviews for the remainder.

Mango checked out its 15 AI platforms through multiple impact lenses, depending on each use case, and created “financial KPIs, industrial KPIs, customer experience KPIs and adoption KPIs, amongst other parameters.”

To measure its efforts, Ulta uses an Innovation Success Experimentation framework that appears at each quantitative and qualitative aspects. “For us, success has many faces. We all know this isn’t a race to the finish line, moderately a marathon that may have loads of learnings along the best way,” explained Bhatt. “Currently, we’re focused on understanding how GenAI technologies might help us successfully deliver value in supporting our goals to hurry time-consuming tasks, speed up the asset creation process, unleash greater power of information, strengthen beauty advisory, and personalize digital experiences.” Amongst Ulta’s efficiency boosters has been the low-code AI environment Interplay, which improved development speed.

Communicate, Learn, Iterate

With regards to integrating AI into workflows, firms must know it won’t be a one-and-done scenario. The cycle of assessment and implementation should repeat often in a series of continuous improvements, sharing wins along the best way and never allowing perfectionism to dam progress.

And until AI is an element of each company’s vernacular, firms must stress how the tech will enhance employees, not replace them. “Generative artificial intelligence is an prolonged intelligence, in other words, a technology that may act as a co-pilot for our employees and stakeholders, and help us extend our capabilities,” said Mango’s Moreno. “Technology will either make us more human or be of no use.”

AlixPartners will likely be speaking on this topic at WWD’s Apparel & Retail CEO Summit on October 24. “Retail Disrupted: Unlock AI Value,” will feature speakersRJ Cilley, Chief Operating Officer, Saks, and Maher Masri, President, NAX Group with Sonia Lapinsky, Partner & Managing Director, AlixPartners.

Sonia Lapinsky – Partners & Managing Director, AlixPartners (moderator)

Danielle Schmelkin – Chief Information Officer, J.Crew Group

RJ Cilley – Chief Operating Officer, Saks

Maher Masri – Global President, NAX Group