The multiplier effect: when AI stops working alone

Alright. Well, good afternoon, everyone, and thank you so much for joining us here this afternoon. My name is Dan Overgag, and I'm joined on stage by my colleague, Aaron Farrell, and we're with Spur Reply. And every day, we help enterprises with the hard part of AI, and that's realizing true business value.

We're gonna start with something that might be true for a few folks in the room. You've deployed AI in the last year, maybe multiple tools.

How's that investment working out? Maybe you're not sure.

I wanna point your attention to the big number on the screen here. 6%.

6%. That's the share that McKinsey classifies as AI high performers. Those are the organizations that are getting measurable results from their AI investments.

Everyone today has AI in some form or another, whether that's an enterprise, a ChatGPT license, or a Claude license, or, you know, AI embedded in native applications that you may be running. The problem is not everyone has seen a positive return and positive business value from those AI investments.

So I wanna take a moment and just think about how many AI tools your team uses today. Not your organization, but just your team. Three, five, seven, maybe? Now think about how many of those tools interact with and collaborate, connect with one another.

Probably not very many at the end of the day.

The other thing is everything. All the applications that you use today have AI embedded in some form or fashion. You know, sales has it. Think Salesforce and Cloudforce. You know, your finance applications. Think SAP and Juul. You know, marketing has it.

And AI is genuinely good at its job within those applications.

Yet none of them are really talking to one another. Handoffs between tools and agents and humans is really, really clunky. So a connected human agent experience within an organization is relatively rare.

Now organizations everywhere are continuing to invest in AI, but the value breaks down when humans, workflows, and agents operate without that shared context, ownership and visibility.

That problem happens at large scale, in public, with organizations that we all recognize today.

You might have seen a couple of these. So Klarna, about two thirds of customer service conversations were handled by AI. Great. Until the conversation needed a human and then the context didn't transfer. And so customers were frustrated starting from scratch. Customer satisfaction went way down in those interactions.

Another coordination challenge was McDonald's. So over a 100 locations were, testing an AI, ordering pilot in the drive through, which was great. Right? Until it didn't really notify a human when an order was unusual, and so was going out on ice cream and hundreds of chicken nuggets were showing up in orders. Not exactly what they were hoping to go viral for.

Not at all. Yeah. And AI really did its job in both of these cases, truly. You know, the failure happened when work crossed the boundaries between AI, people, decisions, and exceptions. And neither organization, you know, Klarna or McDonald's, had a system in place that was designed to coordinate the work end to end across applications and people.

Speaking of coordination, picture this. You're in a sauna, you're totally in flow state, you're working on a campaign, and and suddenly you need to pop over to your CRM to get some information about a couple of deals. No problem. Right?

So then your agent and and your CRM pops up. You need to update some deals here. Alright. Well, now you need to pop over to Outlook and look up a piece of information, and then your copilot pops up and tells you what else you probably should be working on that maybe wasn't what you were doing before.

And you're so deep in this distraction spiral by the time you get back to Asana, back to your campaign, you don't even remember what you were working on in the first place.

Yeah. Yeah. Thirty minutes, you know, lost to just answer one question, to tackle one particular task. And imagine what that adds up to for everyone in this room every day across your entire team, across your entire organization. It's significant. Now thirty minutes, you know, that's easy to joke about, but it's reality. And somewhere within your organization right now, there's a person that is manually reconciling data across your tools because your systems and your agents aren't connecting and allow that exchange of information.

AI is truly making individual tasks and individual productivity significantly better, significantly faster. You know, copy gets written faster. Reports can be created much more quickly. But revenue, margin, customer experience, you know, those are largely unchanged because people are still continue to be that integration layer across the different applications.

Faster tasks at the individual level don't always compound into enterprise value. If a human is still has to carry the handoff between every tool and every application that you're running. So the question remains, if more point AI solutions aren't the answer, you know, what is?

It's agentified workflows.

AI, people, and data working from one shared context instead of many disconnected ones. You've heard about it throughout the day, it's the whole unlock to getting more value out of your AI investments. Not another model, not another copilot, your team, your tools, your data, all working in the same place together. And Asana provides that layer that connects all of these things.

So think about what this impacts.

That handoff that hurt hurt the customer experience like in the Klarna example doesn't happen because the context travels with the case from agent to humans. The distraction spiral that Aaron walked us through where you went from Asana to CRM to Outlook and back again doesn't happen because the information that you need is already sitting within the context of your work. So Sperry Apply, we are a human centered AI accelerated go to market consulting firm and a very proud Asana partner. And we believe that technology can truly be enabler of agents and humans working together.

But realizing that full value requires organizations to not only redesign their work, but establish clear ways of working, prepare their workforce, and coordinate execution across the enterprise.

You know, transformation, that's our daily work with enterprise customers, and it centers really on four key elements. The first being, what work matters most? Not everything that you do is worth the cost or it gets true value from AI. It's like that old saying, not every problem requires a sledgehammer.

So before we touch a workflow, we really need to figure out, you know, which work is gonna drive the most value and which, which work efforts are gonna drive the business forward in a more productive way.

Secondly, it's important to understand, you know, who does what, human or agent. You know, we've watched teams save, you know, a lot of money and a lot of budget by deploying countless agents into their organization, but without keeping the right governance and the right humans in the loop.

Therefore, it's very hard to know when something goes wrong with your process or if there's an exception, who owns the process, who's in charge of the different workflows that you have. That's why it's so critical to think about enterprise work execution before instead of after the fact when you're deploying agents.

The third one is how do we make it stick? This is one that we see a lot of customers skip over. They think it might not be very important. We've worked with hundreds of Asana customers, and we see similar patterns.

Leadership approves the spend, the new tools implemented. Six months later though, people are back to their old ways of doing things. Maybe there's a new workaround that's popped up. And stickiness requires the work for...

Workforce being brought along with the change, not just being informed about it.

And then finally, how do we connect it broadly? The momentum has to connect across the enterprise or you're right back to those isolated wins and the disconnected co pilots that we've talked about today. Most partners do a couple of these things well. We do all four.

If you've joined us here before, potentially last year at WIS or even the year before, you may... This may look familiar.

This is our change methodology that we've built, and we didn't design it just to have something clever to say around Land It, but really, it's a best practice pulled from... Or it's a methodology pulled from best practices we've gained with countless customers doing, you know, change implementations and change management processes.

Land it isn't about a launch date or a go live date. Land it's truly a change bar. Did an initiative change the way people work? Did it change the way people, behave? Or did it go live and then slowly behavior slowly started to kind of go back to where it was six months ago?

So I want you to pause again for a second and think about maybe your most recent AI pilot you've done. And it might have been a great success for one one particular team as a pilot. What happened when you tried to scale that pilot, you know, more organization wide?

Chances are it stalled out, didn't go anywhere. It's still maybe alive, maybe a little bit dead. You're not really sure, and that often is the case. And that's really what our land methodology is designed to prevent. Make sure that the change that you're making is integrated and adopted by your workforce.

So I'm gonna start with the l in LAND IT, leadership.

Leadership is around aligning your executive and your leadership team around a change and being a sponsor and a face of the change, not just approving a budget line item.

Assessment then is understanding where each of your stakeholder groups and individual stakeholders are on that change curve. You know, where their pain points gonna be, where they're gonna be friction or resistance to change, particularly around AI.

Navigation then is very role specific, and this is kind of your classic change management section. You know, good change management is not just deploying, you know, maybe a broad webinar to the entire organization and a wiki page, but it's very role specific training, enablement, ensuring there's strong readiness for the change and the adoption to happen.

Data runs underneath it all. So this is not just one step on the the journey. Data has pulse surveys. It has adoption metrics.

It has usage data. And this is what helps you understand who is using the new tool, whether it's sticky or not. And the integration is when AI stops being something that, you know, was just sort of thrown into the mix and and really starts to be something that is part of how your company and your team get work done. The transition, at the end is when we're finished and you've done a great job and your team has has implemented this change and then there's a hand off.

And you continue on without a partner right next to you and and make it part of how your company works moving forward.

This is our land up method. And it's one thing for us to explain it. It's another thing to hear it from a special guest.

And so we would like to invite a guest on stage with us. She has been a marketing leader for over twenty five years in marketing communications across technology, government, and b to b. Most recently, she was a director of partner digital strategy and experience at Microsoft where she led a cross functional effort to unify a fragmented partner experience from marketing, engineering, and customer service across an ecosystem of more than 500,000 partners.

We worked alongside her on part of this effort and she now advises organizations working through the same disconnect we're talking about here today.

Please welcome Sean Suthers.

Alright. Hi. Sean?

Thank you.

Hi, Sean.

Yeah. Hi. So, Sean, first, thank you so much for being here. It means a lot that you would take time out of your day to come and present on this really important topic. Thanks for having me. You You know, you've spent years at Microsoft getting marketing, engineering, customer service to all work from the same playbook across a massive Microsoft Microsoft partner ecosystem.

So I'm very curious, what was that like? And what was at stake if you hadn't tackled it the way that you had?

Yeah. Well, you know, no surprise. We had a lot of very smart and capable people at Microsoft.

And so the biggest challenge was never really the technology. It was really about getting everybody on the same page. Making sure that we're all working from the same picture and had all of the latest updates and, you know, could sort of move as one ship or one, you know... Because at that scale, especially, any piece of misalignment, any slight misalignment can just be really expensive. So a lot of my job was about getting people onto the same page and helping us move forward together.

Those small disconnects can become so expensive, and a lot of people in this room may be feeling some of that pain.

More tools, more agents. And so as a marketing leader, when you're thinking about making an investment in AI, how do you answer what you want from it? And and what should somebody want back from it?

Yeah. For me, I think it's about speed and efficiency, but ultimately, it's about better outcomes.

You know, I really want time back for my team to be able to do the things that only a human can do. And so there's also an opportunity to help with that alignment that I was talking about. So you can lose time from re...

Reworking or, you know, misalignment. And so that opportunity to stay connected to reduce multiple handoffs, you know, kind of lost messages, that sort of thing, all leads to better better savings. And ultimately, you're gonna get more bang for your buck at the end of the day. I think there's there's a lot of people using AI as a research partner, which is fantastic.

But the most successful teams that I've seen are deploying a...

Deploying agents that are focused on specific work tasks. And then they're building and designing their team with that...

Those agents and that work in mind.

So for me, it's speed first, then consistency, and then capacity.

Yeah. I I really loved what you said around time back for work that only humans can do. And secondly, like, frankly, like managing agents as part of your team or Like they're part of your team. You know, and if that's where, you know, the best marketing organizations are going, you know, managing agents is a skill that many people probably don't have. And we were talking about this yesterday, but last year, one of the speakers had said that marketing transformation in the future is really going to be about managing agents the same way that you have to learn to manage humans and manage a team. With that, not everyone is good at managing a team or managing people on day one. So what does that learning curve look like for managing agents?

Yeah. I I mean, I think it's very similar. We... And in fact, I think we are rapidly moving to a world where roles such as an individual contributor won't really exist or at least not in the way we know them today.

And everybody will need to be a manager of some sort. Because whether you're managing human or managing AI, the underlying principles are the same. You need to, you know, give context and you need to give information and you need to know when to trust and you know know when to coach.

And really, need... As the leader, you need to create that environment, where the human or the agent can do their best work. And I think you can... Or also very successful help with your success is... Would be think about what work should the agent be doing versus what work should the human be doing, and how can you build the right process and team around that, to be successful.

I'd say the other thing is, with that in mind, it's like, what is nonnegotiable? What does a human have to do? Is it, you know, maybe the AI agent does your first draft, but if there's legal implications or it's your executive director's, your CEO's communications, then absolutely, you want human checks and balances in there. Ultimately, at the end of the day, I think it's the leader's responsibility, and role to build the right environment that drives success for everyone.

Yeah. That's that's a big idea. No such thing as an individual contributor role anymore. And so for leaders who now are moving into this kind of hybrid human agent management. What's a mistake you've seen somebody make kinda similar to maybe a new people manager in month one? Yeah.

I think it's not treating that AI agent as a part of the tree team and really just throwing the work...

Essentially throwing the work at the agent and expecting them to do it faster. And really, maybe it's...

Maybe you're still getting a result, but you're not getting the best result because you're not redesigning the process. You're not being thoughtful about what you give to humans and what you give to agents and how that can all work together and really embrace that agent as a member of your team.

Yeah. Yeah. Thanks, Sean. So you've just shared how managing agents the same way you'd manage a person is successful with clear expectations, some shared context, and, you know, people in the right spots where mistakes would be expensive.

It's hard to do when an agent's working somewhere you can't see. But, today, and and what we've been learning about with AI teammates and Asana is the teammates are in the same work graph as your people, as your data, as the context, the audit trail. Everything's all in one place. And it's how a marketing team redesigns the process around it that helps people and agents each do what they can do best and then helps the overall process move faster.

Yeah. And, you know, redesigning the process while still maintaining and having to run your business, It's a lot to ask of any team really. And you know, we do have certainly have a bias on this next question admittedly, so I'll just ask it plainly. You know, what difference does it make to have an outside partner working alongside you for a change of this magnitude versus trying to muddle through and kind of run it all on your own?

Yeah. I think it makes a, you know, massive difference and to have a a very trusted external partner. I mean, I think internal teams know the business best. Great partners bring additional, expertise and capability and capacity, but they're really best... It's the really best... The best partners. They also bring perspective, and that's perspective to challenge your assumptions, see your blind spots, ask the hard questions, present present the point of view that you can't present from an internal perspective. That was always a big one for me.

And really, they help you move faster because they see patterns and they see things that they've learned from, you know, multiple instances of this implementation. And so they can help you move faster. Yeah.

Yeah. I... That that perspective on challenging assumptions and spotting patterns, you... Really well said and, you know, thank you for that. You know, last one before we start to wrap up here is if someone in this room is looking to make an investment in tools, in training, or learning to manage agents, you know, what would be your recommendation on the... Kind of the first thing that they should be trying to tackle?

Well, I would say, of course, find a great external partner. That's someone that you can trust and work with. But I think, really, it's... Make sure that you have the correct understanding and alignment into... Internally of what really this change is is is. Right? Like, make sure that your leadership is fully committed, understands what problem you're trying to solve, and understands that this is not the case of just buying some technology and, you know, next week, we'll have amazing results.

I think that's the easy bit, buying the technology. So you do have the right buy in? Do you have the right training, support? Does your organization have the time and the patience? Yes. Things are moving quickly, but it's still not gonna happen overnight.

And really set people up for success. Give people space to learn about the technology, to experiment with AI, to build their own agents, and build their confidence and really embrace that new way of working so that it's not just a tool.

Yeah. Yeah. Well, Sean, really thank you for being so specific around what it takes and, you know, bringing in your really incredible experience and background.

And, you know, thank you for joining us on stage today. And please stick around while we take the next couple minutes and and wrap up.

Thanks. Thank you. Yeah. Thanks, Sean. Sean ended on people today and and so we will too.

We've talked about some missed handoffs and some lost personal focus. These things have the same same cause. Tools that do their job well but aren't talking to each other.

Adding more of them isn't gonna change that, but connection will. And that when your team and AI and data are all working together with the shared context in the same space with agents treated as new team members instead of plug ins, that's what multiplies where you've invested.

And then finally, it doesn't happen by accident. It takes intention. Someone has to design how the workflow, someone has to design how the technical aspect and the AI and humans all work together in this. And if you start there, you'll start to see some return on that.

Yeah. You know, and we could talk about this for forever. So... But we're limited to a very strict amount of time today. So there are a couple of ways that you can continue to, keep the conversation going with us. You know, first is come find our team at our booth out in the Expo Hall. We'd love to talk about your AI tooling and how you're kind of using AI and go tool by tool.

Secondly, ask us about a free workflow audit. We can take a look at a particular workflow and identify where AI may add additional value, is adding value, where you may be running into bottlenecks or aren't getting the value that you'd expect.

And then finally, follow our upcoming AI transformation series where we're gonna be really diving into this concept of AI ROI and how you start to bridge that gap a little bit.

With that, you know, thank you all so much for joining us this afternoon. Thank you for the time, and have a great rest of your, WIS experience.

Thank you.