The Era of Workforce Intelligence: Unlocking the future where humans and AI work as one

Tushar Makhija: [00:00:00] All right. Good morning. Hi, Johnny. Yes, I know it's, uh, we were supposed to start at nine 30, then we sent out a message 9 45. Um, I was having this discussion with my wife that, how is November 13th gonna bite me? And she's like, control the controllables. Uh, everybody should have a partner like mine who will wake you up and say, Hey, don't worry.

Just do your best, do the best for the people who actually showed up, which is you. So a big round of applause for everyone who showed up in the rain. And, um, you know, this is, uh. Start first. We have spoken to many of you over Zoom calls. Uh, we've spoken to many, uh, at other events like HR, transform or other finance events.

Um, I was introduced to Arvind our, our, um, keynote speaker over email. Some, I think couple of years back when they were, when Roblox [00:01:00] was evaluating a workforce planning situation. And, uh, now we get to do this in person. With all of you guys. So I'm really excited. Uh, I'm also really excited to tell you what really we are planning.

Um, I think the, how so? Team Ohana is a workforce planning software. Our whole idea was how do we make workforce planning less painful? Um, it's been an incredible four years, but what we realized, uh, at the beginning of 2021, that the way companies are going to manage headcount, allocate capital, as well as, uh, you know, make workforce decisions is changing fast.

And, uh, we are gonna share what that future looks like. With you here today. Uh, so let's talk about Tim Mohana as wave one. Uh, what we've done in the last four years, October, [00:02:00] 2021, was, uh, when Tim Mohana was founded. Uh, it was me and my co-founder, BJI. Uh, he's not here today. He lives in India. Uh, and you know, the visa situation is very hard, but one day we'll bring him soon, uh, uh, in America we trust.

So, um. Mm-hmm. You know, we have built the comprehensive platform for workforce planning. How do you plan, manage, hire, compensate, and most importantly. Reconcile and forecast headcount In that process. We also built the first workforce orchestration system, bringing data from all your disconnected systems, like the finance system, the HR system, the talent system into one, into one place.

Um, but as I said, right, recently, we realized we were staring at something much bigger. It is no longer a planning problem. Um, Tim Mohana has replaced the spreadsheet and has become the place where headcount decisions are being [00:03:00] executed. But if we go deeper, we've realized that the headcount decisions are not being made in Tim Mohana.

They are still being made in hallway conversations on Zoom calls, and, uh. In people's heads, the leaders is making the decisions in their head. So what we've realized is that now is the opportunity to take the next leap in. Tim Mohana move ourselves from being the headcount execution tool to actually an agentic dec dec decisioning system.

I practice that a lot. It's still got it wrong, uh, agentic decisioning system. Uh, and, uh, you know, before I talk about and explain what we are really building and what we really mean by an age agentic decisioning system, uh, let's talk about something that we all relate to [00:04:00] growth, right? Um, what you see here is that when the revenue increases.

Your workload also increases. The more you grow, the more work you have, which is a good problem. But how do we solve that problem? The way we are tackling that problem today is we hire more people to do that work. We worked hard on that animation too. Uh, so new customers. We need more CSMs. Team Mohana definitely needs more CSMs, so if you have any, please send them over to us.

Um, new product, we need more engineers. You raise a big round, you have more budget, then you need to hire a finance person, right? You can meet Keith. He's moved from every company that raises a lot of money and they hire Keith MDA to come and become their head of [00:05:00] finance. Uh, and uh. Bigger growth target.

Let's not forget, we all need more AEs. Um, max is our VP of sales and he'll tell you how many a he's hiring right now. Um, so we, we always talk about growth in terms of headcount. Um, it is all about getting this ratio of people right hit for so many, for so much a RR. With this much quota, we need to hire so many AEs.

Um, and basically our frame of mind has now completely changed to growth, means more people to hire. Um. I think that model works when scaling our business was human driven. I think the model is gonna completely fail and it does not exist in this new world where

we have to go and find more productivity in what we are doing and not just hire more people. Right. [00:06:00] So here is our assertion, right? Um, we are living through one of the most significant shifts in history. AI is redefining not just work, but AI is also redefining who or what is doing that work. Here is a very good example.

Um, ServiceNow, one of the PI pioneers in the enterprise era. With the introduction of AI agents has doubled the capacity of their hrps. Again, a ratio that we all know that n number of employees can be managed by one, HR BP at ServiceNow, it went from around 440 to 880. Now almost double the productivity.

I, I don't think that such, uh, traditional software or SaaS ever could deliver on this promise. We tried, but we were not there. Um, and I think [00:07:00] what ServiceNow's experiment tells us that the real breakthrough isn't just bolting AI onto old systems. It's basically rethinking that entire model itself. It's about this, the, I would say the new world of agent AI is technically building systems that.

Are going to act autonomously, they're gonna learn continuously and they're gonna compound value over time. Very similar to how the expectations that we have from our teams and the people we hire as well. But now we can have those with ai. Um, so, you know, at some level what I just said, I saw some folks nodding their head.

I think companies understand what the promise of AI is, but are we really implementing it? What is stopping us from actually realizing the true potential of ai? Uh, we are still, we still hire, we still organize [00:08:00] and we deploy people as if nothing has changed. Team Mohana is a workforce planning platform, and I do, we do look at a lot of companies workforce plans.

Every quarter we wake up and we fund yesterday's org chart instead of thinking, what are the skills that we need to execute the work that needs to get done? So basically we are just investing more and more in headcount to respond to the growth and to fund any growth initiatives. We have 70%, that's why 70% of everyone's operating budget today is tied up in headcount, so, so if your growth is slowing or stalling.

It's not really because of, uh, you don't have enough capital or you don't have enough people. It's because how you are allocating that capital. Um, it is basically getting trapped in a legacy org chart and in also the outdated systems that we have. It's a wake up [00:09:00] call for us as well. Before Team Mohana gets added to that outdated systems, we have to implement that change.

And I think that is fundamentally why I'm up on stage today, sharing that vision with you. Um, we call this problem the capital crisis, basically a structural failure in how companies are investing in their success. Uh, we can all see record budgets, lofty growth targets, lots of money flowing through venture capital.

We all have these very ambitious goals we want to hit, but are we allocating the capital correctly to hit those goals and to generate the outcomes? I think that still remains to be the big problem to solve is. And I think this, the finance folks in the room will understand when we said we have to completely rethink in the age of ai, how we are going [00:10:00] to allocate capital.

And Gartner's CFO surveys actually validates it that 56% of the CFOs say that their capital allocation strategy needs to be completely rethought. Um.

Let's take one step deeper, right? W how did we find ourselves in this mess of capital crisis? Right? If you think traditionally in an enterprise, we have organized ourselves into beautiful, functional silos. Um, and, uh, you know, each of these silo is running its own playbook. The HR team, the finance team, the talent organization, and then what we've done is that for each of these organizations, we have deployed very specialized software.

Each of that specialized software works really well in isolation. [00:11:00] Can, can we count on our A TS to help us find the best candidate, make it, give it the best experience, and run the best recruiting operation? Sure we can. Does our HRIS help us track all the employee changes, gives us all the compliance.

Does payroll software always make sure everybody gets paid? Yes, it does. And yes, fp and a software will run all the beautiful reports for you at the end as well. Uh, but I think because each of these silos work in isolation and but don't work well. Two together, there is something which is the glue. It is the people, right?

Individuals inside each of these organizations and these silos are spending countless amount of time to make each of these systems work well together. They're reactive, they're slow, they're disconnected, and basically the software, the promise of software was, Hey, we were [00:12:00] supposed to make us all productive.

But it really turns out that we actually just need more and more people now to manage the software itself. As the, again, going back to, as the number of employees increases, your HR admins increase your talent, admins increase the number of people you need to manage, workday increases. Right? So basically the current systems were just built to record these decisions and not to really drive them.

Um, what we really need to do now is to break this cycle and to build and deploy systems that help us decide. And act, uh, this, the, the status quo. How we see it today in this age of AI is, is not gonna hold true and it must be changed. So if AI is transforming the work and how work gets done and who does the work workforce planning must transform to, and we claim that workforce planning must [00:13:00] transform to workforce intelligence.

It. I think it's very simple that in the era of ai, how we are going to plan, allocate, and measure our workforce has to completely change. Now, I've been talking about what is the problem, what needs to change? Let's start talking about some solutions now. I think that's what we are here for in the era of workforce intelligence.

Let's set some goals. You are not. Slow down by manual processes or siloed systems. Your systems of record can finally come together seamlessly through the workforce orchestration layer, and truly give you this unified real time view of your complete organization, both your people and your processes, and every system of record must feed into these intelligence workflows.[00:14:00] 

So that finally we can make decisions and move at the speed that the business wants us to move. You will see in our demo at three o'clock today, is that it is all about finding the answers in real time, not having to wait days, not having to wait for people to reply to our Slack messages, but to get those answers from your data in real time.

So. As I said, right, we were the first solution to offer this unified workforce orchestration layer. And uh, but that was still only half the story. To achieve this true workforce intelligence, now AI must be embedded directly into the orchestration layer. It cannot be bolted on, it cannot be a separate thing that you now say, well.

Let me get some fancy AI tool and [00:15:00] put it on top. The opportunity for Team Ohana is to launch the agent layer because we launched the workforce orchestration layer, and this is how I strongly believe that we will be able to move from being just systems of record to systems of action with all of these AI agents that are built on top of this orchestration layer and.

It is now using all the inputs that we can provide, the skills, the people, the capital, in order to make sure that we make the right decisions for the organization. That brings us to wave two. This is Team Mohana wave two.

It's not just solving the problems of workforce planning. But now it is solving the bigger problem to help you succeed in the [00:16:00] workforce intelligence era. On the left hand side, you see workforce planning was SaaS to plan higher compensate, reconcile, and forecast. But workforce intelligence now has agents to deliver to help you anticipate, oh, go back, predict, align, measure, and then optimize.

Basically getting you to the decisions that you need to get in order to plan and manage your workforce. So when we think about the only system of action that is built for the workforce intelligence era, what do we really mean? Right? We are now. We are now bringing the power of AI to now transform these static processes into a more of a dynamic system of action.

Now the workflows adjust automatically. Teams rebalance in real time, and the capital is continuously deployed to the outcomes that deliver the [00:17:00] greatest value for the company. And. This is, this is not just a re-imagining of our existing stack. It is actually going back and re-architecting the entire stack.

We are moving away from pressing buttons and filling out forms to actually providing you with an assistant, a smart analyst that is operating as your best friend always on. And helping you think through the outcomes that you want to generate in your business. How do we think about hiring? I have a million dollars to spend.

I need to build a new AI team. Team Mohana already understands how you pay your people. Team Mohana already understands how long it takes you to hire people. Team Mohan understands your existing organizational structure using all of this information. Now [00:18:00] you can prompt the AI assistant or analyst to help you think through what is the most optimal decision that you wanna make, how long it would take to hire these people.

Who should they report into, in which location we should be hiring. All of those answers in real time. And once you are ready with what you feel is this good scenario that you wanna bring other people to collaborate into, you just instruct the agent to say. Let's go and now bring more people into the mix.

And that creates a more collaborative scenarios with which will people will work together with you. So where do we end up with this? What happens when we are able to unlock the true potential of AI and unleash this agent layer on top of Tim Moana's workforce orchestration layer. Finance truly becomes self-driving.

Uh, now this is not just about [00:19:00] automatically handling reconciliations and rebalancing or running different scenarios. It is also unlocking the potential that a smaller finance team can truly get more done. We, we hear this often in GNA orgs that yes, the rest of the organization is growing, but the GNA organization is not getting funded.

As much happens in hr, happens in talent, happens in finance. Can we really make finance self-driving by providing AI agents that help them get there? That's the motivation. Spreadsheets disappear. We've been trying for four years and others have been trying for many more. But I think there is this opportunity now where if we took collaboration away from a spreadsheet and made this, uh.

Made this platform where people with the right access control, with the right permissioning can come in and make, make comments, suggest things, and then everything [00:20:00] goes through an approval. But we, when we saw that if I individually want to do something, people were taking down, downloading information out of Team Mohana because they trusted the orchestration layer to collect all of this information.

But they were like, I wanna do more with the data myself. And that part was happening in a spreadsheet. But now with the, with our AI analyst, you can have those conversations with the AI directly. You don't have to download the spreadsheet. The organization must gain a live pulse because headcount is dynamic.

Things are changing very quickly in your organization. You can't keep up with everything. But ti Mohana is now synchronizing data. And the AI agent is giving you that live information of what is changing, where things are slowing down, where things are going over budget. So when you come and log in, you are able to go directly [00:21:00] to where the decision needs to be made with the people that you wanna make this decision with.

And I think most importantly, gone are the days of. I am going to plan once a quarter. I'm gonna forecast at the end of the month and I'm gonna run this annual planning process. The those,

I would say, processes don't go away, but we are not waiting and relying on those processes or those times in the year to really take action. Planning truly becomes this continuous process where. It is more proactive than you going into make reactive decisions. So what is our vision?

The number one insight that we first have is that the way people are going to plan, [00:22:00] allocate, and measure their workforce is fundamentally changing. And the companies that build their workforce as a system of humans in ai. Will own the next decade of productivity. And you know, because I truly believe that the potential gains to productivity are actually in finite.

And Team Mohana has the full potential and we as builders of Team Mohana have the will and the grit to go make that happen. The team Mohana is the system of action that will get help you get there. Um, so you know that. Yeah, that was all about what we are building. But actually I'm super excited to also tell you more about who else we have invited today.

Um, first I talked a lot about what we are doing, but this is, we are not just all talk and no show. Uh, Virginia, our Director of Solutions engineering [00:23:00] is actually going to run a live demo. An interactive demo where you can use the AI analyst agent, ask it questions and see how it gives you answers in real time.

We have actually built a real system that is connected into real data from an HRIS, from an applicant tracking system, from an fp and a system making us, uh, ready to come and show you the power of what we've built. Um. There's also a very incredible panel of folks that are going to join us. Um, they've all texted me that they are coming.

It's, it's real. Um, you know, so, um, Kenny, I, I know Kenny is coming from San Jose, so, uh, Kenny is the head of business transformation@scale.ai, and Kenny is going to walk you through scale. AI has three x uh. Headcount on Team Mohana in the last two [00:24:00] years. What is that entire finance stack that they are using?

Why did they make those decisions and how does Team Mohana fit into it? Um, we have Ali Yusef from Ion Energy. Ali's already here. Thank you Ali, for being on time. Uh, who's gonna talk about 2.5 x growth and he said, uh, zero time spent on finding what is the company's headcount. Uh, so same, how did Healon Energy get 2.5 x growth?

What is the talent stack he's using and also how Timana fits into that equation? Um, we, we have two focused, I would say, discussions around how AI is going to help scale organizations. So first is scaling finance with ai. Uh, we've got three CFOs, uh, from one from Seed Geek. One from Versal and one from Brex talking about exactly in many [00:25:00] cases how especially Versal and Brex are building AI internally for their customers, but also how they are using AI to find productivity gains in their own individual organizations.

Asians. And then super, super, super excited to introduce Arvin Casey. Who is the chief people and systems officer at Roblox as our main keynote speaker? Arvin. Arvin is already here. A big round of applause for Arvin. Uh, Arvin will tell you about himself, but I did want to add, Arvind is an engineer turned chief people officer.

So basically I think when I, when the amount of, and. I'm an engineer myself, so I have this, uh, I, I love engineering and I love how engineers solve problems in different, uh, in different functions. But basically being CIO of Palantir, VP of engineering at Google, VP of Engineering at Meta, I think Arvind really brings a very [00:26:00] unique perspective to how technology gets deployed within an organization.

Um, and I think what you'll see in this next presentation is a real balance of what is real. Where you should be careful of and how you should basically change your mindset of looking at AI or you know, as you says, the state of AI in your organization. So I don't wanna spend, I don't wanna take the limelight away.

Arvind welcome.

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