How AI Transforms the Talent Lifecycle: Inside the 'Infinite Workforce'

Dana Gardner: Welcome to Tech Transformed, the podcast that explores how technology is reshaping the enterprise landscape. I'm your host, Dana Gardner, principal analyst at Interarbor Solutions.

In this episode, we're examining how AI impacts the entire talent lifecycle, from hiring and skills development to workforce readiness and organizational agility. Joining me is Meghna Punhani, Chief People Officer at Eightfold AI. With leadership experience across both HR and IT, Meghna brings a unique perspective on AI as both a technology enabler and a talent enhancer. Welcome to the show, Meghna.

[Listen to the discussion or watch it.]

Meghna Punhani: Thank you for having me, Dana.

Dana Gardner: It's great to have you with us. To begin, please tell us about yourself, your career journey, and the role you play at Eightfold AI as Chief People Officer.

Meghna Punhani: I would be happy to. Throughout my career, I've been fortunate to experience high-growth companies at very different stages of development. I've had a non-traditional path to becoming a Chief People Officer. I spent nearly two decades at Google, where I worked across people technology and later led strategy and operations for the CIO. During this time, I watched Google scale from a small company of 3,000 employees to more than 160,000 people, experiencing rapid growth in both headcount and revenue.

Following my time at Google, I joined Palo Alto Networks during another rapid scaling phase, where I led employee experience. Eventually, I felt that a Silicon Valley career isn't complete without working with startups. Now, as Chief People Officer at Eightfold AI, I have the opportunity to help build an AI-native company and deeply consider how organizations must evolve as technology changes, especially in today's environment. Because I am the exact persona this company builds for, I play a dual role here: acting as Chief People Officer while enabling our teams to build impactful products for the rest of the world.

Dana Gardner: You have certainly witnessed an incredible rate of change. Twenty years ago, we thought we were working in 'internet time,' but today things move even faster. Congratulations on that journey.

AI is transforming far more than software development. It is fundamentally reshaping the entire talent lifecycle—from recruiting and skills development to workforce planning and organizational agility.

Given your experience managing transformation across both IT environments and human resources, you understand how to align leadership, talent acquisition, and employee experience to maximize individual potential. When organizations seek AI advancements today, what do you think they misunderstand or miss entirely regarding how AI impacts talent acquisition, management, and enhancement?

Meghna Punhani: For generations, work was designed exclusively for humans, not for automated agents. Today, we have humans and digital agents working together, creating what I view as an 'infinite workforce' where agents execute tasks and humans apply the specialized skills they excel at. However, most legacy enterprise systems were designed solely for people.

Right now, there is immense focus on automation, but it is often executed via bolt-on solutions pasted onto existing, outdated processes. The companies truly succeeding are not just automating old workflows or stacking agent upon agent; they are taking a step back to redesign and re-engineer the nature of work itself. This involves evaluating roles, workflows, and organizational structures holistically to prepare for and take full advantage of this technology.

Technology is evolving faster than people can absorb it, which reshapes roles and naturally induces anxiety or a lack of trust regarding job security. This fear leads to inconsistent adoption across organizations. Ultimately, implementing AI is not fundamentally a technology problem—the technology will inevitably evolve. It is a leadership and mindset challenge. Leaders, particularly in HR, must demonstrate technical curiosity, learn how these tools operate under the hood, and foster adoption through transparency and trust. Re-engineering processes is step number one, and building trust to drive adoption is step number two.

Dana Gardner: When people think about AI in hiring and talent acquisition, they often focus strictly on recruitment, scale, processing applications, and candidate triage. But that feels like yesterday's news. How do you see AI fundamentally re-engineering the entire talent lifecycle—not just during the initial hiring phase, but across workforce planning, continuous skills development, and shaping the workforce of tomorrow?

Meghna Punhani: Talent acquisition is simply where the lifecycle begins, so you must get it right. But the broader narrative centers on how you discover the right people, foster their development, and successfully retain them over time. AI serves as a thread woven across all these elements.

In recruitment, AI fundamentally transforms speed, consistency, and reach. It can evaluate every candidate against the exact same criteria, eliminating recruiter fatigue and mitigating unconscious bias. Because recruiting agents are operational 24/7, the scope of candidate sourcing expands drastically.

When it comes to ongoing talent management, continuous learning and skills acquisition are top of mind for both employers and employees who want to remain relevant. AI elevates this by shifting the organizational focus from rigid job titles to dynamic underlying skills. It uncovers non-traditional career pathways that traditional screening methods miss. For example, given my own non-traditional career trajectory, a conventional system focusing only on past titles would never have predicted my current role as a Chief People Officer. AI evaluates future potential based on skills rather than historical trajectories.

Internal mobility is highly valuable. Employees in one quadrant of an organization often possess skills that make them incredibly valuable elsewhere. AI facilitates skills-based internal mobility, continuous development, and holistic employee feedback by unifying data into a single pane of glass.

Furthermore, concepts like 'digital twins' preserve institutional knowledge. Even if an employee leaves the company, their digital twin remains accessible, allowing the organization to tap into foundational knowledge that might never have been formally documented. Because technical skills now have a significantly shorter half-life, shifting from static job descriptions to dynamic, skill-based decision-making is essential. AI enhances every facet of finding, growing, and keeping talent.

Dana Gardner: Historically, a person's past performance was used to gauge their future capability, leading managers to search exclusively for candidates who had already performed the exact same role. That approach falls short when job descriptions are changing dynamically, or when an open role has never existed before. You mentioned trust earlier. Will AI help candidates trust the matching process more, and will it help hiring managers trust HR to find the right fit? It seems AI can act as a more precise matchmaker than traditional methods allow.

[Listen to the discussion or watch it.]

Meghna Punhani: It must be a combination of advanced technology and human judgment; human insight never disappears from this equation. AI excels at systematically bridging data gaps that humans might overlook. However, to build trust, organizations must deploy AI responsibly and with complete transparency. It is vital to show people how the technology functions behind the scenes.

When we first rolled out digital twins internally, our employees expressed anxiety about what personal data might be revealed. We overcame that hesitation by bringing employees into the conversation, explaining our privacy-first design framework, and demonstrating how the technology works. Crucially, our executive leadership team adopted the tools first. When employees saw their leaders putting their own digital twins forward to answer organizational questions, it neutralized the fear and established deep trust. Leaders must roll up their sleeves, use the technology themselves, and demonstrate its value to naturally earn employee trust.

Dana Gardner: You are in a unique position as an HR leader inside a company that designs talent AI platforms. How do you leverage your own platform internally? How do you 'drink your own champagne' to optimize the employee lifecycle and refine the product based on internal insights?

Meghna Punhani: Living on both sides of this equation is one of the most rewarding aspects of my role. We are an AI-native technology company building products for global HR organizations, but we are also an employer that applies those exact operational principles internally. This dual position allows us to act as both teachers and practical practitioners, directly using our own experiences to improve the software.

Our recruiting organization has completely embraced our native technology. For example, Eightfold offers an AI Interviewer that is currently used by major enterprises worldwide. Within our own company, 90% of our interviews are conducted by this AI, allowing us to manage thousands of concurrent conversations. Sourcing elite AI talent in today's tech market is highly competitive. Historically, our university and internship recruitment efforts were physically constrained; our team could only visit seven or eight universities globally, interviewing perhaps 150 candidates to secure 10 interns.

Historically, work was designed entirely for humans, not AI agents. Today, we are entering an era where humans and agents work side-by-side. I view this as an infinite workforce—where agents execute specialized tasks, and humans apply the high-level cognitive and emotional skills they excel at.

By deploying our 24/7 AI Interviewer, we expanded our reach from 8 universities to over 150, scaling our application volume from 5,000 to over 15,000 applicants. This vastly broadened our talent pool without draining our engineers' schedules. Sourcing talent is vital, but traveling around the world to conduct initial technical screenings takes engineers away from core development. With the AI Interviewer handling the initial evaluations, our engineers continue coding while maintaining a highly rigorous, objective, and consistent hiring standard.

Candidate experience improved significantly too. We found that when interview invitations were issued on a Friday, the majority were completed by Monday morning because candidates love the flexibility to interview at their own convenience without scheduling friction. It removes the anxiety of trying to establish immediate personal chemistry or worrying if an interviewer likes them. It levels the playing field.

We have also seen deeply human moments. For example, during an AI-driven technical interview, a candidate who was a mother picked up her crying baby from a bassinet, pacified the child, put them back to sleep, and smoothly completed her interview. In a traditional corporate interview with human panels, many working mothers would feel intense anxiety or vulnerability doing that. Seeing our technology empower people in that manner warms my heart. These details matter immensely and guide how we refine our product for the market.

Dana Gardner: By utilizing your own platform for high-stakes hiring and talent cultivation, what concrete business outcomes and return on investment (ROI) have you realized? Is the benefit primarily driven by operational scale, or is it a mix of quantitative and qualitative advantages?

Meghna Punhani: It is a combination of both. Quantitatively, productivity within my team has skyrocketed because coordination bottlenecks are gone. The speed with which we move from initial contact to formal job offers is remarkable. Since our evaluation framework is entirely standardized and continuous, we frequently complete the entire interview cycle over a weekend and extend offers by Wednesday. In technical roles, we successfully compressed the average hiring cycle from six weeks down to just four days.

Beyond recruitment, we launched an internal initiative called Project Andromeda to systematically re-engineer existing processes across finance, sales, and operations with an agent-and-human paradigm in mind. Through this initiative, we successfully reclaimed over 4,500 operational hours across our global workforce in just nine months—a highly significant metric for a company of our scale. We also run regular internal hackathons; during our last event, our teams developed 48 distinct AI-driven solutions, all of which successfully entered production because AI accelerated our deployment pipeline.

Qualitatively, the impact is equally profound but harder to capture in a simple spreadsheet. When employees realize the company values their core skills over their nominal titles, it unlocks incredible internal mobility. For instance, we had an immigration specialist on my team who possessed exceptional latent communication and engagement skills that traditional HR software would never catalog. Our platform surfaced those strengths, and today, she serves as our company's corporate Social Media Manager. Transitioning from immigration compliance to creative social media management is unprecedented in legacy environments. When you can discover unique capabilities in one sector of your business and deploy them effectively in another, the organizational value is phenomenal.

Dana Gardner: As organizations increasingly integrate AI agents and assistants, it seems clear this elevates not only efficiency but the overall human experience. In a hyper-competitive market where elite candidates hold multiple offers, their impression of a company is heavily shaped by the fluidity of the onboarding and interviewing process. A company that is sharp, highly responsive, and structured stands out against an organization that feels disjointed and clunky. Providing a seamless, intelligent candidate journey seems to offer a major competitive edge.

Meghna Punhani: Absolutely. For instance, we transitioned a recruiter from our team into a growth specialist role within the company. Because our team members are daily practitioners of the talent acquisition and management platforms we sell, they can connect deeply with our enterprise clients and provide authentic advice on streamlining workflows.

Technology will inevitably automate routine operations, but individuals who possess the cross-functional capability to connect dots across different business units will truly thrive in this new environment. Providing an elegant, tech-forward experience benefits both the talent pipeline and internal organizational agility.

Dana Gardner: Looking ahead, where do you see the features and capabilities of your platform evolving? How do we move beyond matching candidates to roles, and instead use AI to build an adaptable workforce capable of navigating rapid market shifts and structural complexity?

Meghna Punhani: Advanced tools and digital agents are becoming universally accessible, meaning the baseline technological playing field will equalize across industries. Different divisions—whether engineering, sales, or finance—will leverage these tools tailored to their unique functions, such as writing code or generating account plans. However, the consistent underlying paradigm is that AI delivers data-driven recommendations, and humans make the final strategic decisions.

The individuals who thrive tomorrow will not necessarily be defined solely by rigid functional expertise, because automated tools can supplement technical knowledge. The real shift is that AI implementation is no longer just an isolated IT project; it is an evolution of the corporate operating model. Organizations must redesign workflows around a framework where digital agents execute tasks and humans orchestrate strategy. Moving away from a rigid, one-size-fits-all model toward this collaborative architecture is what yields a sustainable competitive advantage.

Dana Gardner: For business leaders who are intrigued by the concept of integrating AI across the entire talent lifecycle but have historically viewed AI primarily through the lens of software engineering or product R&D, what immediate steps should they take to prepare themselves and their organizations?

Meghna Punhani: No organization will ever be completely 'ready' in a traditional planning sense. You can spend years waiting for complete alignment between your board, your CEO, and your executive team regarding precise ROI projections. Success requires a proactive shift in mindset rather than solving a technical problem. Leaders must lead with curiosity rather than fear, transforming apprehension into functional fluency.

My first piece of advice is simple: start before you feel fully ready. The enterprises leading the market today are not those that planned better; they are the ones that began experimenting sooner. Lean in and get your hands dirty. Second, the narrative you build around technology directly dictates its adoption rate. You must humanize the technology. When we deployed our AI Interviewer, if we simply sent a message saying, 'An automated agent is here to screen you,' response rates plummeted. But when we humanized the communication—explaining that an AI assistant named Mira or Eva was working on behalf of our recruiting team to make the process faster—engagement soared. Transparency eliminates trust barriers.

To my fellow HR leaders, I want to emphasize that HR has a historic seat at the executive table right now. Because this is fundamentally a leadership and organizational design challenge rather than an IT issue, HR must lead from the front. If HR abdicates this responsibility, organizations will evolve purely through a technical lens, leaving human capital as an afterthought. We excel at people decisions, and we must keep human capital front and center. Partner closely with your CIO and CTO, treat AI adoption as a leadership muscle rather than a compliance obligation, and lead by example.

The mistake many companies make is treating AI as a "bolt-on" solution to automate legacy, outdated processes. They implement agent after agent without changing the underlying workflows. The organizations truly succeeding with AI are those taking a step back to fundamentally re-engineer work itself. This involves looking at roles, workflows, and organizational structures holistically.

Finally, for job seekers, students, and early-career professionals wondering how to position themselves for a future where job descriptions remain volatile: focus relentlessly on learning agility. Develop deep curiosity, learn how to ask the right questions, and master the ability to acquire new skills quickly. The capacity to learn efficiently and adapt continuously will be the single most critical skill for the future workforce.

Dana Gardner: Meghna, thank you so much for joining us on Tech Transformed and sharing your insights on how AI is reshaping the entire talent lifecycle. To our audience, you can discover more information regarding today's discussion by visiting eightfold.ai.

We will return next week with another episode exploring enterprise technology transformation. Until then, make sure to subscribe to this podcast on your preferred media platform, follow our social conversations at EM360 Tech on X and LinkedIn, and visit em360tech.com for daily enterprise tech insights. Thank you for listening, and goodbye for now.

[Listen to the discussion or watch it.] 

How To Scale AI in Digital Commerce Effectively

Dana Gardner: Welcome to Don't Panic, It's Just Data, the podcast that explores how organizations turn data into a business advantage. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, and I'll be your host for this discussion.

Joining me are Jürgen Obermann, Senior Go-To-Market Leader for EMEA at Vespa.ai, and Piotr Kobziakowski, Senior Principal Solutions Architect also at Vespa.ai. They've worked with some of the world's largest brands to move from static search experiences to more powerful and dynamic personalized customer journeys.

In this episode, we'll tackle one of the biggest challenges facing digital commerce teams, how to unlock the full potential of AI-driven search ranking, and personalization. And we'll explore how technical leaders can design platforms that deliver meaningful impact for their customers. Jorgen and Pietro, welcome.

Jürgen Obermann: Thank you for having us.

Piotr Kobziakowski: Thank you very much.

Dana Gardner: Many teams see the potential of AI, but the practical blockers from data fragmentation to slow experimentation can make it difficult to turn ambition into real customer impact. Jorgen, let's start with a common challenge. Where do digital commerce companies most often struggle when adopting AI-driven search, ranking, and personalization?

[Listen to the discussion or watch it.]

Jürgen Obermann: We see three areas of problem areas with our e-commerce customers today. The first one is kind of an operational level. At the operational level all these e-commerce sites obviously have a history, they have a long development, they have fragmented environments, they all have architectures based on microservices, which was a good thing at the time. But today with AI and its performance needs for AI causes some performance problems, but it also causes flexibility problems. People talk to for the slightest changes. I have 90 to 180 day delivery times from the engineering teams because they have so many areas where they need to fine-tune things and if they touch one thing too much something else will break.

So, it's a very fragile infrastructure. That's challenge number one. Challenge number two is with regard to the customer experience and the search experience customers have on their websites. And that is really something where now with the newer AI technologies, people can do much more sophisticated, personalized search, particularly using our technology. And so that's where people really would like to see some improvements. And that's kind of the challenge they're facing that what they use today is not allowing them to do that.

The third area is the business area where they would love to run campaigns and check out if the campaigns actually have an impact. I like to use the example of the Netherlands where they have the king days and at king days everybody wears orange and somebody providing sneakers, hats and t-shirts should provide them in orange and should push these campaigns to the customers, which today takes three weeks, lots of involvement of data scientists in order to create a personalization and ranking to reflect it.

And what the people really want to do is to have basically more or less online within minutes and be able to configure this and then see the impact in A-B testing right away. And this is something where we get involved a lot with our customers because that's exactly where they want to go.

Dana Gardner: Are today's digital commerce search and recommendation stacks hitting a ceiling? When you look at a large e-commerce systems, what's the architectural bottleneck that you see most often?

Jürgen Obermann: It seems like that that's Lucene-based solutions and I used to work for Elastic for a long time so I know the environment pretty well. It seems like the Lucene-based kind of implementations hit the ceiling as soon as you start using vector operations.

I just talked to an analyst today and they told me that they feel like it's bolted on and therefore not really effective when it comes to be used in these environments. I'll give you an example. One of our customers, recently implemented our solution, was using one of the Lucene-based solutions and they had about four queries per second using vector operations in the background. We implemented our solution and we were using vectors and tensors to do this, not much different to what they were using before.

We could come up with 4,000 queries per second. So, you see that there's two orders of magnitude difference. And this is sort of the architectural bottleneck a lot of these people face besides being a bit too distributed. They have search and ranking personalization divided and have a network in between, which causes latency and so on and so forth. But Piotr, maybe you can give us from a technical perspective an additional view on this.

Piotr Kobziakowski: When you think actually about the multiple systems involved in an e-commerce operation, first of all, we're coming from the search, so we need to find items, that we need to rank them, we need to personalize them.

But then this requires a lot of other things as well, capturing the signals from the users, updating the feature stores, and then inferring on the information that are captured with different machine learning models in inference platforms, usually also outside of the main systems.

So those things basically need to be interconnected, right? So, the calls between platforms. That's one thing which is actually very limiting the speed and response because every connection, every call to the API causes the delay, right? But also if you have systems, let's say system for ranking, system for search, system for recommendation, they usually need to replicate the same catalog multiple times and then the information needs to be collected.

So not only you store data in multiple places, but also you slow down your actions. Then if you like to use all of these components into one single system to provide good answer to the end customer. This requires connecting calls across all of the systems. This data is not really available that fast as it will be in single system, right?

So that's definitely a big, big bottleneck in making the systems work fast. And also from the operational perspective, that's roadblock actually to update all of the systems. If you introduce just single field to add one more feature, that means that you need to update your APIs. You need to update all the things around all the system. So definitely this is really slowing down evolution and innovation.

Dana Gardner: Piotr, let's talk about what AI native search architectures look like. If a technical leader were designing a digital commerce search and ranking platform today -- with those vectors, the tensors, and the real-time inference -- what fundamental design principles should they prioritize? How should this be done properly?

Piotr Kobziakowski: From my perspective, we should look at all areas where the bottlenecks are. We already discussed that, right? We need to put a processing where the data is. So, to shorten the path and enable richer and better calculations on the all signals and data which we have. So effectively, when you look at the Vespa architecture, so that will be, from my perspective, really go to platform for e-commerce.

And then from simple perspective, because you can combine standard product search, Lexi, or semantic, you can basically implement signal handling, so feature store that you actually learn from interactions from user interactions. And then you can serve recommended elements or items very nicely and quickly because this is just another element in your rankings.

And obviously, I mentioned ranking and then what does it mean ranking? Ranking, real-time ranking means that you can perform all the calculations, not just how to order the elements based on your text input. but also include in this ranking business logic, which will be prioritizing, for example, items which are better for the revenue, but not losing the element from personalization, which is making customer feel that system understands them what they like and then what they actually see. So we have very good examples across many customers we are interacting that if users are looking for the cars or mobile phones or houses, if they have let's say,100,000s of offers for different things to actually navigate even through the search, keyword search, or let's say even the semantic search, it's very hard to find those exactly things they are looking for. Let's imagine that I would like to find the car, which is specific engine, specific, let's say color.

And things and then if you enter the website and you have that personalized you see cars you are normally interested how much faster you'll find when this is really precise car you are looking at and then instead of 3,000 you see only maybe 200 which are exactly what you need. So, your decision-making process will be much faster.

Another thing another topic is obviously that a platform which does all these calculations. Let's say it has to have really proper document store. It cannot be just for the search inverted index, which will be enabling us to do lexical search or just vector search, which is a vector store which enables us the vector search or just basically the feature store which will keep the values for example for personalization, let's say profiles for the users.

It has to be all combined because when you do ranking, you need to reach out to all of these data sets at once in a very fast way. Because if you have, let's say 10,000 queries per second, you can imagine that the load on the network will be extremely high if you need to make this cost to separate platforms. We are not talking about megabytes. We are talking about even gigabytes per second when we have combined systems, meaning distributed systems. So, the network is having extremely big importance. If you can put that next to the data, let's say this access enables you to do direct calls to your data, it's much, much faster. So again, many of these things are represented today in tensors.

We hear a lot about vector databases and vectors, but vectors are just small subset of tensors. Tensors can represent map of vectors, scalars, matrices, or maps. And then now when you think about personalization items, how you represent user, user can be represented as a no single vector, because if you have multiple categories, obviously we don't like the same cars as we like t-shirts.

So, we need to have multiple elements that represent different categories. And then we can put that in single tensor in Vespa and then use it in our ranking to calculate simple dot product operation and then really have accurate representation of what user likes. So again, now when we think about tensors, we should actually think a lot about machine learning models. So obviously inference and whether the inference is happening is also extremely important.

When you have access to all of this data, we have tensor representations, then it's natural to run different models like GPT models or any ONNX model, which you can download and you can experiment and use this data immediately in your ranking process.

I didn't mention yet about Vespa ranking. Vespa ranking is not as people understand traditional system where you do let's say, hybrid ranking, and then you'll find the documents which are lexical and semantics in combination. Vespa ranking is really the big, let's say, system which enables you to divide your calculations into three different phases per node and then per global cluster. You'll be able to execute any type of mathematical operation there.

Also, use any signals from typical lexical and semantic world, combine it with business logic with if conditions, let's say, full conditional structures, and then you can really build nicely all of the logic. Also, ranking enables you to expose all the calculated values, which can be used later on to optimize and then use it for training models which will, for example, fine tune weights for each segment of the ranking to be the most accurate possible.

Dana Gardner: Let's drill into the personalization a bit. I should think that the digital commerce systems of the future need to adapt. They can't be static and rule driven. They need to be more adaptive in real time.

So how do these systems, the personalization systems that rely on nightly batches and static segmentation, manual tuning, how do we move them now to a world where ranking adapts instantly to user behavior?

Piotr Kobziakowski: Yes, because of the topics we already discussed in the previous question, as we got that in separate systems, we needed to collect information. We've been not able to run quickly operations on, let's say, millions of the users easily, right? Because the system got their own limitations, and then it was pretty hard actually to do it.

When we move to Vespa, we can shift some of the operations from complex models into just tensor operations because it turns out that actually when we do tensor representation for profiles, we can basically update these models in the update the models based on the user signals and then updating in Vespa is possible. After all, Vespa is enabling a partial update.

So, you can update not just single fields in the document with really high efficiency, but also you can update even single cell in the tensors. So that enables you to manipulate every factor of the personalization and quickly do the dot product calculations. So that's similar to vector search, which enables you to find quickly documents, are, let's say the closest to what users like. And then when you have this ability, you can easily combine that in ranking with your text search.

We can actually start from the text search. You have some results. And then in the second stage, which I mentioned, you apply reordering based on the user preferences and then users will see what he was looking for but in his own preferred, let's say, colors, shapes and everything. So that also gives this feeling of a really good search because then we are not getting things which we don't like upfront, right?

Dana Gardner: And let's look at the impact on the resources needed as we make these advances. So from an engineering and operations perspective, what's the real cost of running search and vector and recommendation stacks as separate systems? What inefficiencies arise when organizations spread their search and vector retrieval inference across different services and databases?

Piotr Kobziakowski: When we have everything actually separate, even lexical semantics are separate, then another re-ranker which will be there. Then another ranking platform which will be putting business weights into every single document. And then you have recommender component that will be learning from user behaviors, doing the nightly batches or let's say hourly or any other period of time.

Then you will have feature store which will keep users. or let's say models, the model server and inference platform. When you think about that in many of these, many of these systems will have replicated data, right? So first of all, they might be actually data-driven inconsistency between these data sets because when we do ranking, we may update maybe later our catalog, not in every systems or something will happen that inconsistency will be there. So that's heavy risk of actually having broken results. Then obviously latency. So we already spoke about this latency and calls. We are not looking at single calls. We call, we look at the thousands of calls per second. So that generates a lot of a lot of data transfers across network. And then it's heavily impacting P95, P99 latency on the system, which is very important for the user experience. So platform complexity.

You already mentioned at the beginning that innovation is a key today because the world accelerated heavily, right? We see models every day. We see innovation every day. So, if we are not able to modify our path from to compete with other systems, which are now built in, as startups, that's really bad thing because we may lose a position from the leader and then be the last one if we'll be not competing.

We need to think about how to make this complex ecosystem much simpler to be able to introduce changes and modifications every day. So again, there is also aspect of testing. Testing is not trivial, right? You need to have really ability to run the, let's say one schema of ranking, how you'll be doing this and performing your operations. And then Vespa enables you also to run multiple different ways, how you will be running different ranking profiles just by setting the parameter so you can create almost unlimited number of ranking profiles and do the selection of the ranking profile to make it actually comparable across different sets of those.

Dana Gardner: It certainly sounds like the implications of AI-driven commerce is forcing a reckoning of search almost from top to bottom. Let's talk about the migration of how you get from current state to the next state. If you were leading a move from a legacy search stack to an AI native platform, how would you phase it? How would you go the crawl, walk, run in order to get there?

What do technical leaders need to modernize search and recommendation? You just can't rip and replace legacy systems. What are the practical steps to make this transition?

Piotr Kobziakowski: The biggest challenge is to provide the personalization to the category pages. When you visit your websites, you will see the products which will be, ordering will be driven by business and recommendation and personalization features, right? And this is usually not heavily implemented in e-commerce space. I would start from the personalization component.

This personalization component requires copy of the full catalog. as we already discussed, because these catalogs live in all of these components, right? So, you start doing the category pages, you start building the catalog, and you start at the same time thinking about, can I use the same catalog in the same platform for semantic and lexical search, right?

By successfully moving the personalization, you can realize that adding the search component to that is not really complex. It's a trivial task because you can build just new ranking profile, which will be responsible for search. And you already have the personalization component, which was built for the category page. If you fuse those two, you have now personalized search for the user.

So step by step, using this kind of approach, you'll have really easy move and of course when you do category pages you can start from single categories as well and then see how they perform measure the results and then add more categories once finalized then you move to a lexical and semantic search but then you don't start from the lexical and semantic search because you don't like to change things which work today well right you will move that later when you have implemented personalization components And then you will see that you can save on the same, reusing the same data in the same platform. You will not on that over the time, you will not need additional platforms to actually run around this.

Dana Gardner: Before we close out from a go-to-market perspective, Jürgen do you think some of the business leaders are underappreciating the impact on search that moves towards AI involved? What we've been hearing from Piotr sounds fairly involved. But do you think it's under-appreciated on the business side of what it takes to make search evolve along with AI?

Jürgen Obermann: Yes, we need to move to AI, but nobody really knows what the impact is of doing AI is because AI is a very, very wide kind of expression. What we see is that there is a push to do AI, but almost from a very high level management perspective for the sake of AI, not realizing what the impact is on the existing infrastructure.

For example, the effect of this by using AI technology for e-commerce is some of our customers in a one-on-one change of infrastructure from whatever they had before to Vespa, they had in certain categories up to 20-25 percent increase of revenue because of the better representation of their products, the easier access to their products and the more personalized delivery of the information to the customer.

The impact is profound. And I think where the gap is today is to understand from a business perspective. Yes, they want to do AI. But how do they get AI implemented in a way that is actually useful for the company? And I think this is the challenge today, where we also sometimes struggle because AI is such a wide experience that you really need to clarify that, but while it's done, I have not seen any manager or product owner who would not be excited to have this type of implementation.

Dana Gardner: Piotr, regardless of the level of AI adoption, it certainly sounds like the usability and detail availability for digital commerce is in itself a force to change and improve your search capabilities.

What advice would you suggest for technical leaders who want to deliver those usability and commerce benefits to the business? How should they start rethinking the digital commerce and architectures? How do you get the technical people to be able to deliver on these promises?

Piotr Kobziakowski: I advise that they look at the AI itself, right? AI is very broad topic. We have obviously large language models (LLMs) that everybody is evaluating now with ChatGPT, Perplexity and other systems, which do one thing. They understand and answer the questions.

But there is a hidden fact about those LLMs. For example, they can be extremely good at extracting features or information from the documents. So they can be used to improve the understanding by the system itself ,what the items are, and what they are for or how they can be represented.

When you have these representations, obviously those mostly will be generated into tensors. So you need to have platform which will handle that very well. The whole ecosystem which will be around those AI systems, LLMs, and other models. It has to be really cohesive and working in tandem with every required features together.

I suggest that they need to look in all of the parameters that we mentioned, such as latency, data availability, how updates can be done, and then how we can combine it all together into one single response to the user in the fastest possible way. They need to think about all of these aspects, and then when they look at Vespa they will realize and understand that Vespa is not the search engine, is not just vector database, but it's a platform that delivers all of these components into one single system.

Dana Gardner: Thank you so much, Jürgen and Pietra, for uncovering some of the details and complexity involved with transitioning digital commerce. It was a pleasure to have you on the show.

Jürgen Obermann: Thank you.

Piotr Kobziakowski: Thank you.

Dana Gardner: For our audience, if you would like to learn more about what we covered today, please visit www.vespa.ai. If you enjoyed this discussion, we'll be back next week with another episode in our ongoing podcast series.

Until then, make sure you subscribe to this podcast and all major platforms, and follow the conversation on our social channels at EM360 Tech on X and LinkedIn. And for more insightful daily content, head over to em360tech.com. Thank you again.

(Vespa.ai supported the creation of this discussion).

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