Agentic Automation | Tiffany Treacy from Microsoft and Keith Kirkpatrick

Hey everyone, it's Alan Hummel from Techstrong. Welcome to our next session in our dynamic series of conversations between the select thought leaders at Microsoft, as well as the some of the analysts from the Futurum group. In this session, we have Tiffany Tracy, VP of product management for the power platform at Microsoft, and as well as analyst from Futurum Group, Keith Kirkpatrick, the session.

This ti this session is titled Agentic Automation. In this session, Tiffany is gonna lead us on a deep dive into the operational realities of agentic automation. It's world where apps, agents and chat are converging to reshape enterprise execution.

We hope you'll discover how AI empowers everyone with a special focus on those who need accessibility and disability support. You're gonna learn how business users supervise autonomous agents that execute, escalate, assist driving inclusive productivity, expect insights into multi-agent orchestration, human in the loop governments, and chat led transformation across support and product activation. So another great session.

Here's Tiffany and Keith. Thanks, Alan. I'm Keith Kirkpatrick, research director with the Future Home Group covering enterprise software and digital workflows.

Today we're gonna be talking about agen automation and how it is reshaping enterprise execution where apps, agents and chat functionalities are conversing to assist across workflows driving the external engagement through the delivery of personalized intelligent experiences and streamlining interactions. And, hello, my name is Tiffany Tracy and I'm the VP of product management for the Power Platform core, which covers our power apps, power automate power pages, RPA, and process mining. I've been with Microsoft for 25 years in a variety of product roles and looking forward to the conversation today, As we're both aware, we really can't get away from a discussion about today's technology without talking about ag agentic ai.

And I wanted to first start off by asking you about some of the ways in which AG agentic AI is changing the way customers are engaging with businesses on a day-to-day basis. Yeah, so I think it's great if we first start with the fact that a agentic AI is going to change the way we work, right? We're moving much more into these human led agent operated environments.

And some of the big changes that come with that are, we're gonna move much more from this very task-based focus to a more intent and goal-driven focus. And we're gonna move from working like in a particular app to really working across apps with that we'll see this synergy of humans that are, uh, you know, driving what we're gonna do. They're adding business intelligence, they're guiding, we're gonna have agents that really do a lot of the, the execution work.

We're gonna have intelligent apps where these agents and humans can dock in to manage everything. And we're still gonna have automations like we have today for very deterministic workflows. When we put all of that together, what we get from a customer experience is they're going to get much more personalized and contextually relevant experiences, uh, much faster and with a lot less effort on their part.

And in fact, in many cases, we see that customers or organizations will able to expand the audiences that they can actually serve with this technology. So like a simple example that, that might be, I'm on a flight, turns out I'm gonna miss my connecting flight. You know, today when I land I might get a, a text message that I've missed my connecting flight.

But you see, very quickly I'll land, the airlines has already rebooked me with an agent. They're gonna let me know what my new flight is, and then if that doesn't work for me, they're gonna give a human to escalate. That's going to change in these kind of customer experiences.

Can you talk to me a little bit about how we're going to see all of this automation, uh, intelligent automation be managed? So one of the powers of this agentic transformation is you begin to get intelligence on tap. So you have these different agents that you can leverage for different business functions.

A level one agent, I think most of us have probably experienced in this point, and that is AI, is maybe we're asking it questions or it's giving us a set of information. And then you have level two where the human is actually directing the agent to conduct some sort of task. And then the business rules, um, dictate when the, the human will get involved and maybe just giving the human information so they can make a better decision.

And then level three is where you see these agents actually taking action aligned to the business rules and the human being in the loop aligned to whatever business rules you set. So what you'll find is that the goal of how we're thinking about agentic AI is we want humans to continue to work in the way they do today. We want them to have a personal assistant that transcends with them throughout their day, whether in their business data, their productivity data, whatever tasks they're doing, and then they will have intelligent apps that let them manage some of these autonomous agents, but those agents can dock into their personal assistant, they can dock into their agents.

So we really want that humans continue to work the way they do today, that this AI will sort of collaborate seamlessly with them. And that's why you see that using both intelligent apps and kind of copilot in this chat interface have their place depending on what the human's trying to accomplish. And so we want this all to kind of slot in more seamlessly versus thinking about it as like they, they have to change as much the way they work.

Right, that makes sense. But I guess one thing that I'm, I'm particularly curious about is as we move into this world where we have agents that work alongside of humans, and there are obviously gonna be agents that work sort of autonomously, obviously still with a human in the loop to make sure that they, that they don't go off the rails. How do you actually coordinate multiple AI agents across a platform to make sure that, you know, the agents do what they're supposed to do when they're supposed to do it?

Yeah, it's an excellent question. It, it's very inherent in, in the platform we're building, uh, uh, across both copilot studio and power platform, and of course some of the pieces in Azure. But it is very straightforward to design for a particular agent, what its rules are, what it's allowed to do, what knowledge it has, what memory it has, what kind of guardrails it needs to follow.

And what we see as customers are moving to these level three agents is they're really thinking through their business processes and chunking those up into reusable components. So maybe for instance, you, you interact to gather information from an external company, and you do that for several business processes. You might build a dedicated agent that does that and and gathers that information that will have a set of business rules that you set for that agent.

It will have a set of points where you escalate to a human or where the agent can actually take action and then that agent may talk to another agent. Again, you define what that communication is and the business rules. So it's very configurable to what your business policies are, what your risk tolerance is, depending on the, on the impact.

The other piece is it's quite straightforward to evolve those business rules. So maybe for instance, you start with an agent that makes recommendations on approving insurance claims or approving purchase orders. You might say that when you start, every single one of those has to be validated by a human.

Then maybe you say, wow, that's going really well. If it's, you know, under such amount a thousand dollars, the agent can auto approve if it's over that the human still has to make that decision. And then you keep ratcheting that up as you build confidence in the, the agentic system you've created.

And those things are very straightforward to configure and continuing to evolve Actually. How does power platform help to sort of manage that, that, as you're talking about multi-agent orchestration across different modalities, whether we're talking about chats, uh, applications and backend systems, because that seems like that's gonna be a core sort of, uh, requirement as organizations, whether they're dealing with regulated industries or not. Absolutely.

So when you think about the power platform one, we, we have a tremendous amount of line of business, large scale apps running on the platform today. And I think it's really important to note for those customers, we are going to bring AI to where they're working today and let them use AI to add even more value to the, the applications they have today. Then we're introducing new tools, uh, for building agents and some of these intelligent apps that will, will dock the agents in.

All of that will still run on the power platform managed environments. So all of the governance that you're used to in the power platform will extend to this agentic transformation so that customers have confidence that they are running in a managed environment, that they have the ability to set the policies to manage it, to audit it, to understand RAI, all of the different components they need. But that will be within the, the core platform that they have come to, to trust in in managed environments.

Yeah. Tiffany, you just mentioned something that's really interesting and, and you've been talking about it throughout our conversation about the idea of human in the loop governance. I'm curious, how do you actually embed that into agentic workflows without sort of slowing down automations or creating unnecessary bottlenecks?

So human in the loop can be orchestrated at any milestone in the process that makes sense for that process or that business. This is one of the places that we think intelligent power apps is going to play a large role. So you can imagine that I might have, you know, a thousand automations or a thousand agents that are running, and I have this intelligent app that lets me go through and quickly approve, guide, change, whatever needs to happen to ensure that the human is guiding but not slowing down the process.

And I think this is one of the roles we see for intelligent apps as we go forward. What about, you know, the other thing I've heard about is the use of adaptive risk models and how that might help ensure that agents just remain compliant with any kind of regulatory or even indu or even, uh, business guidelines. Can you talk to me a little bit about that?

So for every agentic solution, the organization really needs to think through a concept we call evals. And those evals are what are letting you know that the quality, the functionality, the reliability is all within your guidelines. And so it depends on the solution, but you are going to have metrics that tell you the functionality and the reliability.

It's gonna let you know the quality of the response. If it's a agent that's creating some sort of UX or interface, you're gonna have metrics that let you test if that is, is high quality and functional. Um, and then of course you're going to have evals around responsible ai.

And so depending on the solution, one of the first things you want to do as you get started is define for the type of solution you have, what are the areas that will be key and what are the metrics and tests you want to use? And then there'll be multiple ways to ensure that those metrics are on track. So we've heard a lot about HI agent ai, but one of the things that I hear from talking with companies is that there's still a little bit of fuzziness or confusion around what sets, uh, agentic AI apart from some of the chat bots or assistance that we become, become accustomed to dealing with in our everyday lives.

There's a number of things. One is that an agent, if you give it to them, has memory so they can remember previous conversations with you. They can remember previous context.

The second is that the agent can learn, you can continue to train it on knowledge and it can continue to learn and help be more and more helpful as it goes along. It also has not just the initial, uh, knowledge that you trained it on, but it has generative ai, which helps it to fill in the knowledge that you've given it. So you can think it of it has all the power of the, the orchestration and the LLM or the large language model with your specific information on top to personalize it.

All of those are things that chatbots could not do. Chatbots also cannot take action. So chatbot was really, it was a great at the time, but it's really more of like a q and a with very curated answers.

When we get to LLM, it has all of these richer capabilities and so it's not only quicker to get the information back to the human, but it also can do more of that on its own because of the context, the shared memory, the knowledge, and the fact it can take actions. Well, one of the things I think that a agentic AI is really sort of building on is that chat modality where you're able to use natural language to interact with it. Uh, do you see that as being sort of, you know, another sort of real selling point for using AG agentic ai?

Because you are able to, you know, anyone can interact with it. You don't need to have, you don't need to program, you don't need to remember specific terms or anything like that. Natural language interfaces are going to have a large role in agentic AI because as humans, that's an interface that we like, we enjoy and has a much lower barrier for people to participate in.

So I think natural language and being able to, you know, type what you want an app to do or what you want an agent to do for you and be able to go create that will absolutely have a large role in that. Again, I think it will depend on the business solution. We also know that humans are more comfortable in sort of like a personal assistant, like a co-pilot realm talking back and forth because that's how they interact with their other coworkers.

And so we really want as much as possible to have the humans still work and the way that they're accustomed to working. So they might, you know, ping a coworker to ask a question. Now they might ping their, their personal assistant to ask that question.

There will be places where they'll actually go into an intelligent app because that's the best interface for them. And then they may continue to ask their personal assistant questions about that app. So they will be much quicker to learn about that app and what they're doing.

But then natural language interface is definitely gonna play a key role because of the way it lowers the barrier and allows humans to continue to interact with the technology in a way that they're most comfortable. So it sounds like what you're describing is sort of an agent first or, or assistant first, uh, approach to interacting with systems. Is that kind of what we're, we're moving toward?

I would kind of flip it around. I think it's a human first, a human led. I think that human is going to have a personal assistant like copilot that transcends their day with them, understands their productivity context, their business context, you know, how they like to communicate, how they don't like to communicate.

It's gonna be more kind of, I'll call it, connected with the human and their personality. And then I think there's gonna be a set of intelligent apps and agents that doc into those places. Agents may dock into your apps, agents may dock into your personal assistant depending on what they do.

All that together will build kind of the new tapestry of how we work and how we move forward. But I think it's the human at the center with these technologies helping to make them more productive and giving them more time to think strategically, to be creative and to think about what they can do next. We, we know from all kinds of studies that 80% of of people in organizations say they don't have enough time to do what they wanna do, to think about the things they wanna think.

So we're thinking about how we empower that human and how they now have more time for those strategic creative things. And then this technology is, is really helping them along the way. Tiffany, one thing you mentioned is that AI should be for everyone.

And I'm curious if you could talk a little bit about how ag agentic automation can help ensure that people with disabilities aren't just included, but actively empowered as they're working and using enterprise workflows. Yeah, this is an area I feel extremely passionate about, what we've seen so far with, uh, particularly co-piloting and some of the automations that have been done in, in teams and some other places. So, you know, there's lots of different situations that, that people with disabilities face.

Um, you may have someone who has hearing loss and now with the transcript on a meeting they can fill in where something wasn't quite clear to them. You may have, uh, someone who has a DHD who focusing on the meeting and the notes. Um, they feel like they miss out in both fronts.

I think. I think that's a human experience across the board now with meeting notes and the transcription, like you can stay a hundred percent focused on the conversation, the meeting, and know the rest of that is going to be there for you. You could flip this over to other environments like schools or education where the concept of meeting notes can help students take notes in lectures and they can have it all there.

So they're focused on their learning in the moment. I mean, a lot of these, uh, agentic AI pieces are gonna help humans be fully present in the moment and know all this other stuff is there for them to use later, but they're not having to multitask in the moment. And the the numbers are showing, uh, people see the real impact to that.

They feel like the quality of their work is better. They feel like they are more included, they feel like they have better performance and they feel like the meaning of their work has actually gone up. We're just seeing the beginning of all the impact that this is going to have for us.

Tiffany, can you gimme an example where a agentic AI has provided an outsized impact above and beyond what you either might have expected or what we could have previously done? Yes. We see many times that the spark for starting with AI is around efficiency or productivity, but what we're hearing from customers is they're seeing a number of other vectors of impact.

Um, accessibility and inclusion has been a really strong one, which I'll talk about. Uh, being able to upskill and learn has been another one that's come up quite strongly. In fact, ey uh, Ernst and Young recently did, uh, a study where they interviewed over 300 people who had been using Microsoft Co-pilot, uh, asking them how did it impact their work.

All of these 300 people identified as having a disability. Mm-hmm. And over 75% of them said they felt like copilot had made them more productive at work.

They kind of laid that along three lines. One was removing barriers, 88% said they were doing better communications by using copilot than they had in the past. They also talked about feeling more included and feeling like the quality of their work had gone up.

That was over 85%. And they also talked about feeling like they were getting more meaning out of their work because of their productivity and the quality. So that is just a tremendous, uh, like additional benefit that we're seeing from AI where organizations are able to ensure that every team member is bringing their best selves to work and doing the best role that they can.

And I think we will just see more and more of this as we move forward because as co-pilot and some of the other AI continues to learn even more and more and becomes more personalized, it can even help in other ways that will be very valuable for people. Tiffany, I was wondering if you could share some examples about how ag agentic technology is being designed with accessibility in mind. Yeah, so as you know, Microsoft's had a a long history of thinking about accessibility features in our products, whether that's been sort of an Xbox and assistive controllers or office and, and the many accessibility features we provide there.

That same sort of mission is, is moving into ag agentic ai. So we can think about what are the new accessibility features that maybe in the past weren't as feasible that now we can bring to the forefront. Some of them are already out.

You think about teams meetings, teams, transcripts. You think about things like copilot, being able to ask questions across all of your graph data. As we move forward, we see even new opportunities.

For example, the teams team is thinking about how today in a team's transcript you have whatever has been said verbally, you know, might be another language, might be in English, might be in multiple languages, but it's what was spoken in the future. What they wanna do is include what was signed in the meeting into the transcript. So everybody has a complete transcript, whether that was spoken or whether that was signed.

And that's just one example of the many type of agentic AI features that we feel like is now feasible that we're exploring. So I was wondering if you could tell me about how ag agentic automation has really streamlined very personal or sensitive, uh, processes and procedures. One of the areas that would be a, a great example of this might be human onboarding.

So we each come to a new role or a a a new set of work with various, uh, backgrounds with strengths and places, things we know nothing about. And agentic AI can really personalize helping that human on board in a way that they feel completely comfortable. They can ask many questions, they can get access to many resources, they can get recommendations and guidance that will help them learn at a much quicker pace, but not something, whereas in the past, they would've had to share very broadly with their new team that they didn't understand a concept or they didn't have this experience.

Or maybe it's very difficult in a, a large conference room to to hear, uh, the, the voices. And so Agen AI has a opportunity to really help speed up that onboarding, personalize that onboarding, and do it in a way that is really taking the human into account and helping them do that in the best way possible in a way that's sensitive to things and very positive and productive. Thank you very much Tiffany, for a great conversation and real insight into the world of ENT technology.

Thank you, Keith. I really enjoyed our conversation today. It's always fun to talk about the transformation that's ahead of us and how agentic AI is gonna help all of us move forward.

Today. We heard a lot about agents and I think some of the things that really resonated with me was the fact that ultimately to have success, you need to start with humans looking at processes and goals and then bring in the technology. Now of course, there's a need for platforms that can really provide an orchestrated agent experience across intelligent apps, agents, and of course all of the workflows that are integral to really driving real business benefits.

And ultimately the other thing that really, really sort of, uh, resonated for me is the ability of agent technology to improve the experience of people who may have disabilities and to do it in a way that really takes into account how they're feeling and not really kind of separating them from the rest of the employee base or other customers, but to do it in a way that's empathetic and again, can really drive outcomes.