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Retail Resurrection: David’s Bridal bets its future on AI after double bankruptcy

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Inside a new David’s Bridal store in Delray Beach, Florida, a bride-to-be carefully taps images on a 65-inch touchscreen, curating a vision board for her wedding. Behind the scenes, an AI system […]

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more


Inside a new David’s Bridal store in Delray Beach, Florida, a bride-to-be carefully taps images on a 65-inch touchscreen, curating a vision board for her wedding. Behind the scenes, an AI system automatically analyzes her selections, building a knowledge graph that will match her with vendors, recommend products and generate a personalized wedding plan.

For the overwhelmed bride facing 300-plus wedding planning tasks, this AI assistant promises to automate the process: suggesting what to do next, reorganizing timelines when plans change and eliminating the need to manually update spreadsheets that inevitably break when wedding plans evolve.

That’s the vision David’s Bridal is racing to fully implement with Pearl Planner, its new beta AI-powered wedding planning platform. For the twice-bankrupt retailer, this technology-driven transformation represents a high-stakes bet that AI can accomplish what traditional retail strategies couldn’t: Survival in an industry where 15,000 stores are projected to close this year alone.

David’s Bridal is hardly alone in the dramatic and ongoing wave of store closures, bankruptcies and disruptions sweeping through the U.S. retail industry since the mid-2010s. Dubbed the “retail apocalypse,” there were at least 133 major retail bankruptcies and 57,000 store closures between 2018 and 2024. The company narrowly survived liquidation in its second bankruptcy in 2023 when business development company CION Investment Corporation — which has more than $6.1 billion in assets and a portfolio of 100 companies — acquired substantially all of its assets and invested $20 million in new funding.

David’s AI-led transformation is driven from the top down by new CEO Kelly Cook, who originally joined the company as CMO in 2019. Her vision of taking the company from “aisle to algorithm” led her to make an unconventional choice for her leadership team.

Rather than recruiting from within the bridal or retail industries, Cook tapped Elina Vilk, a Silicon Valley tech veteran with 25 years of experience in payments and digital technology, to lead the execution as president. “I’m probably not the first choice, but that’s by design” Vilk told VentureBeat in an exclusive interview.

Vilk’s background couldn’t be more different from traditional retail leadership: A decade at eBay and PayPal where she served as CMO, experience running small business marketing at Meta with “200 million businesses” and being among “the first digital marketers, ever.” This fresh outsider perspective was precisely what Cook needed to reimagine how a 75-year-old bridal retailer could use AI to create an entirely new business model.

What’s driving David’s Bridal’s transformation 

AI was not part of the DNA of David’s Bridal, so Vilk first faced the challenge of building a team from scratch. Her first call was to Mike Bal, a seasoned product leader and technologist, who she worked with as CMO of WooCommerce. Bal, who had spent his career in technology companies like Automattic (parent company of WordPress.com) and various agencies specializing in AI development, was initially reluctant.

“I’ve been married for almost 15 years, and my wife’s a marriage and family therapist… she doesn’t like weddings.” Despite his reservations about the wedding industry, though, Vilk’s comprehensive vision convinced him. “Elina had this end-to-end plan,” he explains, highlighting the media network, the acquisition of Love Stories TV and the opportunity to use AI for wedding planning. 

With a technical leader in place, Vilk faced a key decision. “I could have a whole team and have everybody report to me. That was an option. Or I could have a couple of people report to me to start, and then everyone else dotted-line to me, but put them in other organizations, which is exactly what I did.”

By distributing expertise throughout the company rather than creating a siloed AI team, Vilk says the strategy paid immediate dividends because technological transformation became everyone’s responsibility rather than an isolated initiative. Their accomplishments:

  • Resource multiplication: Without increasing headcount, Vilk effectively doubled her available talent by accessing developers and resources from multiple departments.
  • Cross-company influence: With team members embedded in every leader’s organization, the AI initiative gained strategic representation at all levels.
  • Accelerated development: The team functioned like a startup within the established company, moving quickly by working across traditional departmental boundaries.
  • Collaborative engagement: Department heads became natural stakeholders through their team members’ involvement, creating organic buy-in across the organization.

This distributed approach accelerated the company-wide identity shift, transforming David’s from a traditional retailer to a technology-enabled wedding platform in less than a year.

Building the technical foundation

When Mike Bal arrived at David’s Bridal last December, he faced a daunting technical challenge. The company needed to build a sophisticated AI system with limited resources, a tight timeline and no AI experts. Looking at the wedding industry’s reliance on spreadsheets and the communication barriers between brides and vendors, Bal saw an opportunity for a fundamentally different approach.

“The biggest problem brides have throughout the entire planning process is getting people to understand their vision,” Bal explained. Brides could communicate visually through platforms like Pinterest, but struggled to translate those images into words that vendors, family members and even wedding planners could understand.

Bal’s first breakthrough came in his architectural approach. While many companies were implementing AI through traditional retrieval-augmented generation (RAG) on vector databases — which essentially functions as a search that finds information matching a query — Bal recognized that this wouldn’t capture the nuanced relationships in wedding planning.

Instead, he designed a knowledge graph architecture using Neo4j that still leverages RAG but in a fundamentally different way. Rather than limited search-for-a-match logic, the knowledge graph allows the AI to follow a map to the details that make up the most relevant answer, trace connections between elements, understand that a preference for lace might indicate a bohemian style or that tropical flowers suggest a beach theme.

Working with a single “but sharp” engineer, Bal introduced Replit for rapid prototyping to start building experiences immediately. “We can’t really wait,” he recalls thinking as they faced a potential April launch date while starting work in January. The team soon partnered with dotkonnekt, whose containerized components based on open-source tools like Neo4j and Langflow aligned perfectly with Bal’s architectural vision.

A key innovation was their approach to “memory”, or how the AI system would maintain context across interactions. Rather than using a single large language model (LLM), Pearl Planner orchestrates multiple specialized AI models working in concert, each handling different aspects of the planning experience.

“Humans still technically process faster than AI,” Bal notes, explaining how they designed the system to trace relationships through the knowledge graph, similar to how people make intuitive connections. In testing, he found this approach performed “10x better in getting the details in the right place” compared to standard RAG methods.

Rather than overwhelming the team with complexity, Bal focused on simplicity, building a clean interface that hid the AI’s sophisticated tool use capabilities. He systematically extracted expertise from David’s staff, codifying how elements like dress details and venue preferences relate to wedding styles. This expert knowledge was transformed into a vision analysis pipeline that could process images and generate both user-facing aesthetics and detailed backend JSON representations capturing granular preferences.

The result was a system that leverages a proprietary style quiz, taking a gamified approach, allowing users to select images across multiple categories, from dresses and venues to entertainment and color palettes. This analysis extracts not just surface preferences but deeper style insights, creating both a user-facing experience with an external vision board and a detailed backend profile. 

This profile informs every subsequent interaction, from content recommendations to task prioritization, all without requiring the bride to manually explain her vision repeatedly. Additionally, the system curates the bride’s aesthetic, which can sometimes be hard to communicate when it might be a mixture of trends and themes, using that information to create a personalized experience from the colors of her Pearl Planner dashboard to recommended bridal party colors and soon, wedding website design and invitation themes.

“A lot of these brides don’t feel like anybody’s actually listening to them and what they want because everybody has an opinion,” Bal reflected. “Part of it is they just need somebody to listen and remember what’s important to them. That’s a different problem that we’re solving, like a very human problem, but if we got that right and the interactions reflect that, then we did the right thing as a starting point.”

By prioritizing the most emotionally resonant features first and gradually expanding capabilities, Bal’s team created an AI assistant with sophisticated function calling that could perform actions like marking tasks complete, reorganizing milestones or generating recommendations. The technology was impressive, but the real achievement was translating technical capabilities into emotional benefits, making brides feel heard and understood in a process typically filled with anxiety and miscommunication.

Getting the ROI on AI 

Vilk mused: “One thing I’ve learned as a tech person coming into retail is that it’s very efficient, more efficient than the world of tech, ironically. Every penny is watched in retail. There’s not a lot of room to say, ‘Oh, I just wanna play with this AI.’ It really has to have one of the equations on the P&L, do we see a line of sight to even more savings, or do we see a line of sight to even higher growth?”

For David’s, that means building entirely new lines of business with their own P&L. The Pearl Planner platform is set up as a distinct business unit with its own revenue projections and cost allocations, essentially functioning as a startup within the established company.

“It’s all about the EBITDA” (earnings before interest, taxes, depreciation and amortization), Vilk explains. “We’ve created a separate business unit around Pearl by David’s, which includes Pearl Planner as the cornerstone. It’s pretty much a startup within the existing company.”

This approach allows the company to measure the platform’s success independently of the core retail business while creating a “very favorable flow through compared to retail,” according to Vilk. The revenue model diverges significantly from traditional retail: Instead of focusing solely on dress sales, Pearl Planner generates income through vendor subscriptions, with photographers, venues and other wedding service providers paying monthly fees ranging from $20 to $300 for preferential placement and access to brides at the exact moment they need those services.

For AI initiatives elsewhere in the company, Vilk employs a phased, risk-managed approach. “For me, it’s a very simple ROI in terms of what are we paying today versus what we’re going to be paying with AI,” she said. In areas like marketing, where the company spends significant funds on photography and image retouching, AI implementation begins with partial replacement, perhaps 30% of the workload, to validate both cost savings and quality maintenance before expanding.

This cautious approach extends to customer service AI and other implementations. “The cost of the tool cannot exceed the percentage that you’re using it for,” Vilk insisted, which establishes a clear threshold for initial investment. “You can’t just replace it all and be like, ‘Great, I saved the company a million bucks,’ and then lose customers because you haven’t checked the quality first.”

This disciplined approach to AI ROI reflects the reality of implementing cutting-edge technology in a traditionally thin-margin industry. While tech companies might have the luxury of experimental AI initiatives with long-term payoffs, David’s Bridal’s transformation must demonstrate tangible returns at each step.

Where they plan to take it

While Pearl Planner is currently in closed beta, David’s Bridal has an ambitious rollout timeline. The public platform launch is scheduled for “early this summer,” with updates on new features and integrations with partners like MyRegistry, Dynadot, Shutterfly, Google and wedding vendors across the nation.

Vilk’s vision extends beyond just the initial launch, with plans to expand the platform’s capabilities and reach. “In the future, we’re going to make that better and better,” she said, outlining plans to add more sophisticated preference matching based on colors and other wedding elements. Her goal is to create “embedded workflows” that simplify both the bride and vendor experience.

A key upcoming milestone is the launch of Pearl Planner Pro this fall, a separate platform designed specifically for professional wedding planners. “They have so much knowledge and so much experience,” Bal explained. Rather than replacing these professionals, the platform aims to streamline their work and improve client collaboration.

“What we’re going to do for planners is give them this entire workflow, but it’s going to look very similar to your real estate listings,” Vilk said, drawing a parallel to how technology enhanced rather than eliminated real estate agents. The planner version will allow professionals to curate vendor recommendations for their clients rather than having brides sort through the entire marketplace.

Bal has his sights set on even more natural interactions with the platform. “My ideal is to give everybody the ability to call and go on a walk,” he said, envisioning brides managing their planning through voice conversations with their AI assistant. “They can call and talk to their assistant and ask, ‘Hey, what tests do I have about this? Do I have anything that covers this? Actually, I want to do this in July, not in August.’ Let’s move it up. You can push that back or get rid of those things.”

Beyond the wedding planner, Bal sees the core platform they’ve built as a foundation for solving other problems across the company. “Once you have all that set up and you have your observability for your agents, you have your API keys in there, the lift to spin up a new workflow is pretty low,” he noted. “If we want to personalize our lifecycle emails now, it’s pretty minimal effort. We’ve already put the foundation in.”

A blueprint for retail revival

David’s Bridal’s approach offers three insights that could reshape how retailers approach AI transformation. First, their pivot from product to platform demonstrates that business model reinvention, not merely technology adoption, is essential for retail survival. By fundamentally changing what they sell, from dresses to vendor connections, they’re creating new revenue streams that pure e-commerce players can’t easily replicate.

Second, they’re leveraging a frequently overlooked asset of brick-and-mortar retail: The rich, high-intent data generated through in-person customer interactions. As Bal noted, “not a lot of companies can start with a growth channel at that volume, with that level of intelligence and intent.” While conventional wisdom suggests physical stores are liabilities in the digital age, David’s shows how they can become strategic data advantages when properly harnessed.

Finally, their focus on addressing emotional needs, helping brides feel heard and understood in a process where “everybody has an opinion,” represents a fundamentally different approach to AI implementation. Rather than merely automating functional tasks, they’re using technology to fulfill emotional needs that have always existed but never been adequately served.

As retail continues its painful transformation, with tens of thousands of stores closing annually, these insights suggest that survival may depend less on competing with e-commerce giants on their terms, and more on reimagining what business traditional retailers are truly in, what unique data advantages they possess and what emotional needs they can uniquely address. For David’s Bridal, a company that has twice faced extinction, their transformation represents not just a technological upgrade but a complete reimagining of their role in the customer journey, a lesson that may prove invaluable for retailers across all categories.

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Can Intel cut its way to profit with factory layoffs?

Matt Kimball, principal analyst at Moor Insights & Strategy, said, “While I’m sure tariffs have some impact on Intel’s layoffs, this is actually pretty simple — these layoffs are largely due to the financial challenges Intel is facing in terms of declining revenues.” The move, he said, “aligns with what the company had announced some time back, to bring expenses in line with revenues. While it is painful, I am confident that Intel will be able to meet these demands, as being able to produce quality chips in a timely fashion is critical to their comeback in the market.”  Intel, said Kimball, “started its turnaround a few years back when ex-CEO Pat Gelsinger announced its five nodes in four years plan. While this was an impressive vision to articulate, its purpose was to rebuild trust with customers, and to rebuild an execution discipline. I think the company has largely succeeded, but of course the results trail a bit.” Asked if a combination of layoffs and the moving around of jobs will affect the cost of importing chips, Kimball predicted it will likely not have an impact: “Intel (like any responsible company) is extremely focused on cost and supply chain management. They have this down to a science and it is so critical to margins. Also, while I don’t have insights, I would expect Intel is employing AI and/or analytics to help drive supply chain and manufacturing optimization.” The company’s number one job, he said, “is to deliver the highest quality chips to its customers — from the client to the data center. I have every confidence it will not put this mandate at risk as it considers where/how to make the appropriate resourcing decisions. I think everybody who has been through corporate restructuring (I’ve been through too many to count)

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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