From AI Strategy to Execution: How Forward Deployed Engineering Is Closing the Enterprise Gap

Enterprise knowledge management isn’t a new idea. Companies have tried wikis, enterprise search, and intranets for years. But most of those systems were built to store information, not support how people actually work. The result is a familiar problem: employees are flooded with information, yet still spend too much time searching for answers instead of making decisions.

Ryan Shrout and Mitch Lewis sit down with Sarah Lundgren, Director of Technical Enablement for Hybrid Cloud and AI at Lenovo, to walk through Signal65's independent validation of the Lenovo Knowledge Superagent, built on the Lenovo XIQ agent platform. The conversation covers what makes agentic AI a structural improvement over prior knowledge tools, how Lenovo deployed the platform internally before taking it to customers, and what Signal65's testing found across deployment speed, retrieval time, employee adoption, and productivity impact.

Key Takeaways Include:

• Most enterprise knowledge projects fail long before employees ever use them. Signal65 found that the Lenovo Knowledge Superagent can be deployed and operational in approximately two weeks. Custom knowledge management builds run six months to a year or more on the low end, with no guarantee of the governance, feedback loops, or lifecycle management that come built into XIQ.
• Enterprise knowledge overload is costing more time than most companies realize. Signal65 validated a 30% reduction in search time, while employees relied less on subject matter experts just to find answers, with 81% of employees reporting measurable time savings. 94% of users said the agent helped them overcome blank-page syndrome, and subject-matter expert outreach dropped by 35%, freeing up high-value time across the organization.
• The ROI of enterprise knowledge management gets very large, very fast. At scale, the productivity impact translates to approximately 120 hours saved per employee annually, with up to $17 million in potential productivity value in a 3,000-person organization.
• Most AI pilots fail because adoption is treated as a rollout problem instead of a product problem. Lenovo deployed the Knowledge Superagent internally first, building onboarding, feedback loops, and change management into the platform from day one. Clear use cases, content owners, continuous feedback routing to IT, and a diverse early user group allowed the platform to scale across business units organically rather than stalling in pilot.
  
  

Sarah Lundgren's read on where this goes: people will no longer start from scratch. Agents handle retrieval and synthesis so humans can focus on judgment and decisions. The Knowledge Superagent is the first in Lenovo's AI library of domain-specific agents, with retail, operations, and services agents designed to work as a connected system on the same shared platform.

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View the full Signal65 validation report at https://bit.ly/3S22IZo 

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MIKU JHA:

There's no way I can take it and deploy it across my entire enterprise or risk my external customer base with them. So trust is not even a feature anymore. It is the architecture that needs to happen.

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DAVID KANTER:

Yeah, well said. Well said.

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MELODY BRUE:

Hello, welcome to Six Five On The Road. I'm Melody Brue. Today, I'm going to be diving into how organizations are moving AI from concept into real innovation. And I'm here with David Kanter and Miku Jha. We're going to get talking about, you guys had a big announcement today. You want to share a little bit about the big news, a lot of excitement.

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DAVID KANTER:

Miku and I are very excited about our announcement just hours earlier. It's something that Miku and I, and our teams have been working on for many months. And you think the time is perfect right now. This is the moment. I mean, one of the things we're finding in all of our conversations right now is all of our joint customers will go so far beyond talking about automation, get the latest technology, but really getting the impact at scale across their enterprise. And to do that, we believe that we need to reinvent the underlying work. To reinvent the underlying work, you need to think about going that differently. So our announcement today was our joint forward point engineering program that is bringing together experts, AI native, platform native on ServiceNow at DEU's with Accenture industry for deployed engineers to go reshape that underlying work. And that's why this mole is the biggest solo core and why we're so energized to be here and talk to you about it.

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MELODY BRUE:

This is great. So tell me though, why is it now? Now, we're kind of forward in AI deployment and how people are thinking about AI, so why is this the moment?

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MIKU JHA:

I think it's really, if you look at the data, then it's kind of interesting because enterprises are investors in autonomous agents. They are getting into the core workflows, but at the same time, 88% of those initiatives don't make it into scale production deployment. Or said other way, only 12% make it. So there is still this glaring gap of 88 to 12. And how do we get enterprises to kind of start getting the outcomes? Which is why this motion is so powerful. Because yes, I founded this FTE team eight months back. We are able to take solutions into production in less than 10 weeks. But the question is that once it is in production, now there is change management. Now it is about how do you get it adopted in the deployments from 200, how do you get to 20,000 users? And all of that is outside of just the pure tech and LLM. That's where this partnership becomes so powerful, because you need industry expertise, you need to have the knowledge to do the change management, and you need to take it to enterprise scale. which we can do together for our joint customers in a very differentiated way.

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MELODY BRUE:

One of the big challenges has been around mindset. Now there's a mindset about AI and whether people are ready for it and that change management. So how do you kind of think about sort of…you can't just create mindset, right? But you can sort of reverse engineer it. So how is this partnership helping to create and build on that mindset in the enterprise so that people really are ready for what's next that they have?

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DAVID KANTER:

I think a couple of things that we're doing together, we talked about that mindset. One of this mindset is not just automation, but reinventing the work melody. So when Mikko and our team started formulating about what to make this different, it may be impactful to the mindset is that we found to get the return on this investment, on the tokens, on the assist, we need to look at the value chain. Think about it as a major process area. If we look at it that way, we can start delivering that there's going to be a change in experience, a real business outcome we're going to drive towards. And we found that that's the important shift on the mindset. We have to tackle something that's specific to that value chain for the customer in their industry, in their environment, because they're using lots of different AI technology. And that's why we think this is so important.

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MIKU JHA:

So what they were saying, right, there is also some sort of fatigue of doing all the experimentations for enterprises. So the way the resistance comes from not yet seeing, touching and feeling that valley, from your investments. So now when we take on the projects where we can partner really well is also how do we show them the potential value? What is the business outcome? Why are you even building it? And I think that needs to be more of a science as opposed to an art. Today it's an art because you say, OK, I'm going to go build these five agents. Why? Right? Like, where is that method to say, this is what it is going to deliver me. This is what—it is going to save me $5 million because I'm able to avoid the issues which I couldn't avoid before. So that value anchor is a data-driven way in which the mindset can change, right? Which is not happening as much as it should.

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MELODY BRUE:

Yeah. So you're really reorganizing around value. And that takes several different steps to get there. So first you have to align on what is the value. And then it is a bit of an organizational reconstruct, right? So how do you help? How do you jointly help customers get there?

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DAVID KANTER:

Yeah. And what's great is about this, this announcement today is that it's beyond an idea. So me and our teams, like we are already out of customers together. We were eventually, we've been working together since January, building upon our long history. I mean, we announced our original work, uh, around Source.ai platform back in July, 2023, believe it or not, like two and a half years ago. So a couple of things we can do is one, you've worked so closely together at the product level, now at this industry level. And we're proud to be ServiceNow's number one partner globally, and in every region around the world. And what that allows us to do is bring our scale, both globally, but also locally to this, to like, feed all of our knowledge into our clients, along with Miku's teams, not in parallel, but together. And we're finding that's what our customers want, is you showing up together from the beginning. I would add to that, I would just also call out that we also go and show up as customers because as Accenture, we're an early adopter of ServiceNow's technology. We use that power, our organization. But I think that really helps the conversation of how we show up together.

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MIKU JHA:

And one thing I really want to kind of emphasize here, right, that now the term FTE is kind of used as, you know, a branding or marketing, but it's not actually about the labor. It's actually about how do you approach that bill and what is the operating principle. It's really important, this is where this partnership helps so much, because from ServiceNow, we are building best of the best AI native engineering talent, and we are embedding in customers' environment, in their world, in their workflows, in their messy data, right? We are working with them, we are going into their board meetings to say, okay, what is your board imperative, right? So we embrace that into it, and then kind of roll up our sleeves to say, We are not here to give you best practices, templates. We are here with you to actually take your solutions where you start seeing the outcomes, right? So that's a very different approach in how we are bringing the FDE motion for our mutual enterprise customers. It's not just as a top, it is really leaning in 360 with the customers to get them over the finish line.

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MELODY BRUE:

And as you help customers deploy thousands of agents across their enterprise, how do you then help them to understand the governance and the security and risk that comes with that?

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MIKU JHA:

That's a very powerful question because I was reading the data that the number one blocker for AI solutions not to be adopted is the consistency. But the number one blocker for it to not scale at enterprise level is trust. Because if I don't trust it as a CIO, as a CFO, as a CTO, as a decision maker, there's no way I can take it and deploy it across my entire enterprise or risk my external customer base with that. So trust is not even a feature anymore. It is the architecture that needs to happen.

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DAVID KANTER:

Yeah, well said, well said.

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MELODY BRUE:

Yeah. And as you start to architect this, actually, as you start to build it out and you continue on this code about development path, what does that look like for you? And what are you looking at towards, you know, what's the next phase of this and what does success look like for you in this?

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DAVID KANTER:

Yeah, it's a great question. We're at Knowledge 2027 now and we're having this conversation again.

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MELODY BRUE:

So next year, a year from now.

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DAVID KANTER:

A year we're going to be from now. Our teams are driven by this, really this vision, by making it real, like we're going to re-embed work, industry by industry. And this combination of our skills is unmatched. And that's why it gives us the confidence, like where we're going to be in a year and even beyond, is we're truly going to re-embed the work, industry by industry.

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MIKU JHA:

I'm not even going to 27 like now, right? Now, the way we want to work is that as we get into our mutual customers, right? We lead from our AI native teams. We do all that we need to do correctly to get it to the build phase. And then we kind of have no gap in anything called knowledge transfer. Our teams work together. We're bringing the expertise from industry. Accenture takes it to scale. So it's from zero to one, one to N, and it's done in one motion. 0 to 1, we will take it on. 1 to N, Accenture FD does it, and then enterprises are successful.

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MELODY BRUE:

As we talk about AI in the enterprise, there's always the discussion of what does this do to jobs? How do people sort of address not just working now with their co-worker, their human co-workers, but their agent co-workers? Accenture has always led with human at the lead, not human in the loop. So for this project, and as you move forward, what does that look like to you? What's the importance of that?

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DAVID KANTER:

Yeah, I mean, I think that's going to be your point of your question here. That's going to continue to be our operating mindset, too, is that we need to have humans in the lead. So now we're talking about putting AI to work for people. That is how we think about this. The work is we're thinking about that value chain and rethinking about that. It still requires very specialized people to resolve certain types of issues. We want to free up some of the administrative work so that we can have the humans that are driving this spending their time on the most important issues affecting a customer at Elko. So I think that's just going to be inherent in everything.

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MIKU JHA:

Yeah, and a lot of it is, the way we say it is more that you have autonomous actors in your equation for core workflows. You will have more human on the loop as opposed to in the loop. But that time that gets freed up is for more complicated tasks. So it's always a mapping, but it is like the things which the autonomous system can do, it still needs to be supervised. It still needs to be that somebody overseeing that fleet of agents, for example. So I think it's about what are the right handover points in the context of a given workflow between the agent and the human. That's what we need to decide correctly for workflows to become efficient.

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MELODY BRUE:

Well, this will be exciting to watch. Anything else you want to share before we wrap up?

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DAVID KANTER:

We're, we're so excited to get going nowadays. We talked about, we have this in plate to a couple of customers and we're looking forward now with the announcements out there is bring this Ravni around the world.

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MELODY BRUE:

Yeah. Thank you. Well, Dave, Mika, thank you so much for joining us for this episode of Six Five On The Road. Please check out all of our other videos on Six Five .com. Like and subscribe, and we will see you next time.

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