Deloitte’s Harry Datwani On Agentic AI: Orchestrating ‘Human Plus Machine’

‘We view agentic AI as augmentation and orchestration. Where can you eliminate tasks instead of eliminating roles? We start by reimagining what the experience and the process might look like, how to think about the combination of humans plus AI agents, and how to orchestrate that to drive the types of behaviors in so many organizations that we spend time with,’ says Harry Datwani, principal and partner at Deloitte Digital.

Business adoption of AI and agentic AI continues to grow, with global management consulting firm McKinsey in March estimating that over three-quarters of $500-million-plus annual revenue companies now use AI in at least one business function.

However, as Forbes reported in August, MIT estimates that 95 percent of GenAI pilots deliver no return on investment.

AI, and agentic AI in particular, has the potential to take over the work currently done by many human employees. But that doesn’t have to be the case, and it won’t be if businesses go about deploying agentic AI in a thoughtful fashion, said Harry Datwani, principal and partner at Deloitte Digital.

[Related: Cognizant CEO: ‘Still At The Early Innings Of Capturing The AI Opportunity’]

Datwani, in an exclusive conversation with CRN, said his company’s successful approach to agentic AI is to not think about replacing human workers, but orchestrating how humans and AI agents work together to expand employees’ opportunities.

“It’s really about orchestrating what you can use AI to do and what you use humans to do when you reimagine how work gets done,” he said. “So rather than just minimal human supervision, it’s about orchestrating humans and agents as you reimagine how work gets done in a given industry, sector, or function.”

Datwani also said the future looks bright for organizations that look at agentic AI as a way to improve productivity and not as a way to reduce headcount.

“We will see new levels of productivity out of organizations such as new ways to think about how one person can handle this many sales calls, or one person can handle this many finance transactions,” he said. “I think we will start to see productivity growth that will drive enterprise value.”

There’s a lot going on with agentic AI and its impact on business. To learn more, read CRN’s full conversation with Datwani, which has been lightly edited for clarity.

How does Deloitte define agentic AI?

We think of agentic AI as systems, versus a single tool or application or use case, focused on driving key outcomes and metrics with intelligence from large language models and insights gleaned from data. Where we differ from others is, often in the market it’s about no human supervision, or minimal human supervision. One of the hallmarks of the way we think about it is this notion of human plus machine. It’s really about orchestrating what you can use AI to do and what you use humans to do when you reimagine how work gets done. So rather than just minimal human supervision, it’s about orchestrating humans and agents as you reimagine how work gets done in a given industry, sector, or function.

How does that definition change how Deloitte looks at the future of agentic AI in the workplace?

When we think about the future of the workplace, we see a lot of organizations repeating their less-than-ideal methods of the past. If you think about RPA (robotic process automation) a few years back, I guess it’s more than a few years. Often the idea was to apply RPA to a current business process. And the way it was often done was like pouring asphalt over an old road. It looks nice. It’s great on top. It looks perfect. But you didn’t address the underlying issues, the foundations, cracks, etc. You just poured another layer of asphalt on top. And you effectively hardened the bad business process or suboptimal business process that you automated.

We believe agentic AI requires taking a step back to reimagine the process, the function, and the jobs to be done with AI using the customer’s lens. I grew up doing a lot of customer experience projects, and we would talk about personas and journeys. You never had a persona of an AI agent before. You solved business problems. Your employees, customers, perhaps a partner, perhaps a sales manager, a service manager, those were your personas. Now you have AI agents as additional personas. You can’t just replicate the process. You have to take a step back and reimagine how work gets done. So I think the first thing is to reimagine versus try to automate or identify an existing business process.

The second one is, we see a lot of organizations focused on where they can replace a human or replace job roles. We view agentic AI as augmentation and orchestration. Where can you eliminate tasks instead of eliminating roles? We start by reimagining what the experience and the process might look like, how to think about the combination of humans plus AI agents, and how to orchestrate that to drive the types of behaviors in so many organizations that we spend time with. There’s always that proverbial backlog that you never get to. You do your annual planning process. You identify your big strategic initiatives, prioritize them, sequence them. But inevitably, there’s a ton of things that you never get to because of prioritization. .... There’s typically another tier or two of things that if you could, you would get to. We believe a lot of enterprise value and productivity will be unlocked when you reimagine work and look at where you can apply agentic AI to those tasks. It’s not that I necessarily want to replace you. I want to give you time to talk to more clients, to deal with more complex transactions, to take on more opportunities.

AI has been in the news recently with a couple of studies by Capgemini and MIT saying that up to 95 percent of AI pilots don’t reach production or don’t deliver measurable benefits. So why should we believe moving forward now with agentic AI will have any better chance of success?

This is a little bit of Harry’s view, plus Deloitte’s view. Change for organizations has always been hard, and I suspect it will always be hard. And it’s particularly hard for the large, complex companies that we deal with, because there are organizational dynamics you have to manage. Often there’s awareness and fluency gaps that you must manage. Data, we all know, is challenging. The regulatory landscape, particularly in AI, is evolving rapidly. We believe we’re still in the very early stages of this transformation curve. If you think of a baseball game, we still think it’s early innings. Clients started out by saying, ‘Does this technology work? What are the use cases that I could apply these use cases to? Let me quickly go prove the technology works. Let me prove that I can extract value.’ What they haven’t necessarily done is, well, ‘Did I get all the underlying data right? I probably will never get to all the data, but did I get to a sufficient level of the data being where I needed it? Did I build enough organizational buy-in and consensus around this being the right thing to do?’

It’s going to take time. And I think as you see particular sectors or examples start to reimagine key business processes, you start to hit a tipping point. I was talking to the head of AI and transformation at a large insurance company, and they are fully committed to a multi-year roadmap. We are doing some of the work, but they are doing a lot of it themselves, reimagining the insurance underwriting process with AI agents. One of the examples she gave me was that if you have a certain number of pictures of a house, you can predict with a fairly high level of accuracy what the next claim is going to be. The water heater? The roof? A leak? And then with generative AI, you can start to make recommendations about what the insurance underwriter can do during the sales process to drive repairs within some period of time to improve the quality of the risk that you’re underwriting and therefore be able to give beneficial pricing. That’s a huge advantage if you can offer a cheaper insurance policy because you’ve now figured this out.

What are some other use cases where agentic AI can be applied more quickly than, say, mainstream use cases?

Because of our size, Deloitte tends to do work in almost every industry and sector from tech and media to health and human services, in the government, and from banking to healthcare. Each of our teams consist of people who’ve spent two or three decades working on the business processes and technologies unique to that sector. The set of technologies and processes at a commercial bank are very different from those of a healthcare company. So we’ve spent time with them to identify a whole library of use cases across the functions of an organization: sales, service, finance, marketing, HR. And we’re seeing there are many ways to categorize them. Maybe a very simple way is autonomous use cases versus [human] assist use cases. Autonomous is where complexity is low, transaction volume is high, predictability is high, and you apply AI agents to do all of that business process without a human in the loop, freeing up human time to do other transactions. And assist is where the AI serves potential recommendations or answers for a human to apply criteria to. And often the human is the one delivering the answers. The area and functions that we see the most conversation around and the most things moving to production is broadly around customer service, and largely assist customer service. For instance, how do I help the person that’s either engaging on chat or on the phone have a quicker, more insightful, less frictional customer service interaction.

Given that customer service has traditionally been a people-heavy process, how do you employ agentic AI without pushing customer service agents out of a job? In other words, doesn’t the adoption of agentic AI lead to a need for fewer customer services representatives?

So I don’t have a number to share that we think X percent will get redeployed. But what we’re finding is that, because we’re still in the early stages, the conversations are much more about, ‘How do I get them to take on more customer interactions? How do I get them to support more of our customer segments,’ and less of, ‘How do I reduce headcount.’ We were doing work for a large B2B organization. I can’t talk about the actual client, but they sell their products to big box retailers like Costco, Target, Walmart. We helped them build a set of agents and capabilities to support this, it’s a cute name, we call it ‘WisMO,’ or ‘where is my order.’ Inevitably, there are multiple steps that have to get checked. For instance, when you and I order something from Amazon, it’s one specific product to one specific household. When a big box retailer buys from this place, they could be buying multiple different things that come from multiple different shipping places, and then go to multiple distribution centers, so lots of variability around part of the orders, but not all of the orders. Are we shipping them under one invoice? Are we shipping them separately? There’s lots of manual steps and checks. The business case was not around, ‘Can we fire these people and let them go.’ It was, ‘Our business is growing, and can we do this without growing our headcount exponentially. We will still grow our headcount, but can we grow it at a slower rate? And can we grow it in the regions we want to cover the time zones?’ That was the business problem we’re solving, and I suspect we’re going to continue to see that, particularly while we’re still in early innings of this.

Is Deloitte involved in bringing agentic AI to coding?

Yes. We have built our own set of capabilities using what’s in the marketplace plus some of our own IP to help address productivity, quality, and consistency of code generation using AI. We’re finding that it helps benefit implementation timelines and implementation costs. But there is still a human element needed from a design perspective and code review and quality perspectives as well.

I ask because several large organizations have actually laid off coders because of the increased use of AI in coding. Does Deloitte see that as a potential issue?

What we’re seeing in our conversations is that many of our clients, particularly in IT because it’s the coding question, have massive amounts of backlog that they’re never able to get to. The conversations we’re having with clients are much more about how to get to all that stuff in the backlog? Can we help accelerate how they get there?

Looking forward, what does Deloitte expect to see in terms of agentic AI and how agentic AI will impact the workforce in the future?

One thing I think we’re going to continue to see is the need for enterprises to embed agentic AI and agentic AI tools in the way their employees work. If you look at the adoption rates, and they’re publicly available for all these consumer-facing generative AI tools, the adoption rates are ridiculously high, and it happened ridiculously fast. I don’t think we’ve ever seen that kind of adoption before.

Take [me, for example]. We’re actually planning our holiday. And we said [to ChatGPT], ‘Give us a five-day itinerary for Mexico City. We have a 12-year-old and a 9-year-old. We are pretty adventurous eaters. We have these food allergies, and we’d like to stay in a safe neighborhood.’ We got a pretty good itinerary. That’s a common use case people are doing outside of work. I’m sure there’s many others. But then you come to work, and many organizations don’t have such tools available for their employees to use in many places. They have not yet opened their firewalls to these types of tools. And so I think we’re going to see organizations invest in building tools to help their employees be more productive in the things they do. Interdependent with that will be programs to increase fluency, awareness, and comfortability with these tools, because there’s a whole plethora of questions: Is it safe? Is my data okay? Is this going to replace my job? You also have people just afraid to learn how to use these things. So I think the broad enablement of these tools, as well as the broad enablement of the workforce, will be areas that that we see. Additionally, we will see new levels of productivity out of organizations such as new ways to think about how one person can handle this many sales calls, or one person can handle this many finance transactions. I think we will start to see productivity growth that will drive enterprise value.

Is there anything else you think we need to know?

Our role as a trusted advisor to our clients is to not only think about use cases but bring them a point of view. It is easy to walk into somebody and say, ‘Well, you should reimagine your entire finance function.’ But we bring a point of view of what we think that re-imagined future looks like for a bank, a healthcare company, human resources, or finance. One of the reasons we believe we’re well suited is that no single technology is the answer. It’s not an AWS or a Salesforce or a Google or a ServiceNow answer in the complex enterprises that we play. It’s how does this whole thing come together? Where should I use Google? Where should I use Salesforce? When should I build it myself? Those are tough, complicated questions. And because we are [often] the most strategic partner, we can play not only the trusted advisor role, but also the ecosystem orchestrator role to bring the pieces together. Often a tech platform will come in and aggressively say, ‘We’re the only answer, just buy all of our stuff.’ We come in and say, ‘Yes, but you also need this and this, and here’s what we should use each for.’ We’re finding that clients really need that perspective.