Intro: 0:01

Welcome to the Actionable Futurist podcast, a show all about the near-term future, with practical and actionable advice from a range of global experts to help you stay ahead of the curve. Every episode answers the question what's the future on, with voices and opinions that need to be heard. Your host is international keynote speaker and Actionable Futurist, andrew Grill.

Andrew Grill: 0:30

AI, and, in particular, generative AI, is all over the media and now companies are looking at how they should be introducing AI into the enterprise. While generative AI platforms such as ChatGPT have been trained on publicly available data, they may not be suitable for always-on and mission-critical systems. So what's the opportunity for enterprise-grade AI? To answer this question, I'm delighted to have on today's show Umesh Sashdev, the CEO of Unifor, an enterprise-class AI native company that has sent out to transform businesses, delivering compelling and engaging customer and employee experiences. Welcome, umesh.

Umesh Sashdev: 1:07

Andrew, it's great to be on your podcast. Thank you for having me.

Andrew Grill: 1:09

I had the privilege of sitting down with you on a trip to London earlier this year where we talked about the current state of AI, and you told me the fascinating story of how you started Unifor Next. You could share this story with our audience.

Umesh Sashdev: 1:21

I founded Unifor in the year 2008 and the first thing that we set out to do or build or develop in Unifor was a speech recognition engine which could understand multiple globally spoken languages, along with its associated natural language understanding programs, etc. Over the years, what that has transformed into is what started with an AI company focusing on the human voice is today a truly a multi-modal AI platform, where today we cover the aspects of video through computer vision of voice, which is what we originally started to do with speech recognition, etc. One text which engages areas like chatbots, which some of us are getting very used to right now, email conversations, etc. So Unifor, over the last 16 years, has dedicated itself to creating AI models that help machines understand various forms of human engagement, human communication, which span across the modes of video, voice and text.

Andrew Grill: 2:37

I think that's really important because we're obviously playing a lot with chatbots, but humans also speak. We see vision and I think being able to have that multi-modal is a very important part going forward. But perhaps you could define what an AI native company is.

Umesh Sashdev: 2:53

Well, ai native company is a company that was born doing AI, as different from a traditional enterprise software company, which there are several of today who are now bringing in AI into the technology stack. So, at Unifor, the very first program we wrote, the very first technology we built was a speech recognition with natural language understanding, which was around voice AI. So a company that was born doing AI and that's the only thing they've done from the beginning and we are a prime example. Today we are also, from an enterprise, b2b standpoint, one of the world's largest AI native companies. We service over 1500 enterprises in 20 different countries and in those enterprises we have 750,000 users, which happen to be the employees of the organization, who, every day, actively, are benefiting from some form of AI that Unifor deliver to them, either on their voice calls, video meetings or emails and chatbots, etc. And that, from an adoption, from a scale, from a ability to integrate within the enterprise, makes Unifor the largest B2B AI native company in the world today.

Andrew Grill: 4:14

So I talk a lot about the challenges that companies will face when they introduce AI into the enterprise and I think people are discovering what sort of public generative AI like ChatGPT, what they can do, but what are the unique challenges that the enterprises will face when it comes to generative AI?

Umesh Sashdev: 4:30

Andrew, this is a very fascinating topic and, as you would imagine, very close to my heart right now. It's one where we've been calling ourselves conversational AI and AI native company for 16 years and in the last eight, nine months, the world has finally met us where we've been standing for a long period of time. I have not felt this level of curiosity, engagement from Fortune 100, fortune 500 CEOs and their board members, where, in the last few months, a bunch of folks from the C-suite, including CEOs of these Fortune 500 companies, have reached out with the very same question. You just asked me that what should we consider when we think about announcing our AI strategy at our next earnings meetings to the world, etc. And the considerations are very wide and deep, which is why it's creating such discomfort with folks who are not used to this subject. Let me try to unclutter or demystify at least the big boulders that people ought to think about. First, the pace at which this technology has evolved is unprecedented. Every 48 hours, you and I, andrew, are reading something new about transformers or diffusion, a new model, a net, new innovation that we were thinking two days ago. That has not occurred even in the era of the internet revolution or the cloud, which also means that regulation around the world is lagging the pace of innovation, and that's truly important for an enterprise leader to take into account. We can rest assured, given the demands of the industry and given where the governments have indicated already they are whether it's the US, european Union, parts of Asia there is imminent regulation around the corner which very likely is going to put a guardrails around usage of public data to train these models the need to give credit to content providers whose content is going to be used in these models which will then be serviced, servicing many use cases. As an example, if you're developing an AI model like chat, gpd and you're going to use publicly available data sources, then some of the upcoming regulation is likely to ask that the people whose content was taken in to train these models be given credit. Now, that's a very hard task because one of the new things with Generative AI is these models have become large, which is why they're called large language models, and when you're dealing with that size of data, it almost becomes impossible to pinpoint the source of that data. So upcoming regulation is one key area.

Umesh Sashdev: 7:15

The next key area is we have to have people educated enough to demystify the total cost of ownership, of Deploying a full solution, not just a model, in a proof-of-concept type fashion in an enterprise. So if you think about what are the components you you start with, these models required a lot of compute, silicon chipsets. There's cost associated with that is infrastructure, cloud or a premise. Then there's the cost of the AI model in itself, either API calls or some form of licensing, which even in the beginning could feel like it's free or available off the shelf. The minute it hits enterprise grade, that becomes important. And Then it's finally the layer of software around that AI model. So the whole stack of cost needs to be demystified and fully understood, especially at the scales up with these enterprises want to operate.

Umesh Sashdev: 8:12

The next area is Security, infosick, data security. These enterprises are usually in a regulated environment financial services, health care vs Consumers do not want our personal information to be going anywhere except the place that we allow the enterprise to use it for. And you know these enterprise need to be extremely careful via integrations, cyber security measures, infosec, to make sure these AI models that are coming in to their infrastructure Are not going to take in or misuse personally identifiable data for their customers. Finally, a bunch of CEOs have asked me this question in the last several months and which is very fascinating, and these are CEOs sometimes who have Workforces of in the hundreds of thousands of employees and they've asked me you mesh, what about culture, ship? What kind of org design change do I bring about? What this technology, what AI, is likely to do is cause major disruption in the way we operate the business and I don't think we have a choice in the matter anymore. The world is moving towards that direction. Efficiency Driven by some of these technology innovations, especially AI, is imminent. Now we cannot wish it away, but to get my entire workforce on board To move them from worried about will my job last to this is exciting. Let me partner with my CEO in making this transformation. That is truly fascinating, and I'll close this topic with one anecdote.

Umesh Sashdev: 9:50

One of the CEOs I quote very often is one of the largest companies in France and they're in the government public services department and I was sitting down with him and he was picking my mind on you know what we are seeing elsewhere and our thousand plus customers how our people applied AI, one of the pitfalls.

Umesh Sashdev: 10:09

And Then he told me, even before bringing his executive team on board with the topic.

Umesh Sashdev: 10:17

He first went to the frontline workers, the folks who deliver posts and mail To residents in France. He went to those tens of thousands of employees To bring them on board and when he met them, short enough, their first question was are our jobs at risk because of AI? And his answer was Well, that's not the right way to think about it. The right way to think about it is let's think about the power of what AI can be doing for all of us making our lives easier as we go about delivering mail and costs, etc. And if you partner with me, you will have a seat at the table and picking which AI tools this company brings on board, etc. And he says that changed the complexion of the whole organization Embracing AI. So it's a bunch of involved topics that many different CEOs are being very Involved in and, given the experience we have with our vantage point of 20 different countries and thousands of customers, we're doing everything we can to share what we've learned over these years.

Andrew Grill: 11:21

Lots of different threads, in the end one pack. And I recall when we had breakfast in London I talked about the fact that the chat dpt launch in November 2022 was a watershed moment because it removed the friction. My parents in Adelaide, australia, are talking about chat GPT and I said where did you hear about that? Oh, it was on the news.

Andrew Grill: 11:39

I stood on stage in Abu Dhabi a few months ago with the CTI of Amazon and he said we've been using AI for 25 years. Now it's become popular. Which brings me to the point that I think what it's done and that's why the CEOs are asking you these questions it's removed the friction. A CEO, a C-suite person, can actually play with the tool without having to learn Python or run scripts or set up APIs. They can very quickly see the power of something like a generative AI platform. But I think what that's also done is it's raised the visibility of this Technology to the regulators. What do we do about regulation? Do we regulate the tech or we regulate the use of the tech? And how do we get the regulators up to speed to understand what the power of this is and when the guard rails should be?

Umesh Sashdev: 12:23

if you think about all the time that you and I have been around. We've seen the internet revolution, we've seen the cloud and the mobile era. I Don't remember a time when the creators of technology, typically Silicon Valley folks, were the first to come out and ask for regulation. Usually, the creators of technology are very bullish on the outcome that technology delivers and folks in government, whether it's Washington or 10 Downing Street or Paris, etc. Are the ones pushing for regulation. This is the first time, to my recollection, that Silicon Valley, simultaneous to announcing the innovation, has also started to ask for Guard rails and regulation, and we have to understand why. There have been many theories about why this is happening. People want to create, you know, competitive Pressures, etc. I think the ask for regulation is for a different reason, and that is for the first time we've, as human kind, created a technology that is capable of making decisions like the human brain. In the past, we've created technology that could automate repetitive tasks, take away some things that we used to do, mimic human behavior, but never make decisions like Generative AI can. And so the examples to understand how profound the power of this technology is you could instruct a Generative AI technology bot like a chat GPD, and there are versions of chat GPD today which are called auto-GPT, where you can ask it to book me a restaurant in Oxford Street for two on Saturday night and I'd like to try an American meal. Without saying anything more, auto-gpt takes in the instruction and then starts adding new tasks to its list until it gets the desired outcome. So it will go to a website like Yelp figure out restaurants which match the description, then go send emails to that restaurant asking for availability. Once it's confirmed, use the card on file to go hit a payment gateway, finish the payment until it finally receives confirmation and emails the confirmation back to the actual requestor, the person who made that request. Nobody told it how to go about these tasks. It discovered these tasks as you went about it. Now another version of auto-GPT came about which is called Chaos GPD. So somebody took the exact same program and to show the world the negative effects of this profound technology, they created Chaos GPD and the instruction given to it was the programmer created five fictitious individuals who don't exist and said go find all publicly available records of these five individuals on the World Wide Web, use any means necessary to hack into their credentials on the World Wide Web and, once done, erase all known records of them from World Wide Web.

Umesh Sashdev: 15:37

Much like the example of Find Me restaurant, the machine now is relentless. It keeps adding tasks to itself until the desired outcome is reached. So it's truly important for us to understand that this is a relentless task machine. It does not stop until what it's being asked to do is actually achieved. And the thing it's lacking today, which is the area of necessary research for a lot of AI companies, including Unifor, is it lacks judgment.

Umesh Sashdev: 16:12

We, as human beings, have been gifted by nature. Not only can we make decisions, but, as making decisions, we know this is good, this is bad. Even if we decide to do something bad, we're conscious about it. Ai is, today not conscious. It's not sentient, it lacks judgment, and that makes it a relentless task machine. Therefore, in the wrong hands, it has the potential to create serious damage to sovereigns, to security apparatuses of governments, to cyber risks, to us citizens and humankind, which is why there has been an ask for regulation.

Umesh Sashdev: 16:51

That said, given how fast the innovation is, regulation is easier said than done.

Umesh Sashdev: 16:59

How do you regulate something that's evolving every 40 hours without curtailing the power of innovation?

Umesh Sashdev: 17:04

You want a teenager now, like you said, your parents in Australia, a teenager in India.

Umesh Sashdev: 17:10

You want all those folks who are now beginning to enjoy the technology to come up with new ideas. You want them to innovate. At the same time, you want to put guardrails on. Anyone putting out new models should almost go through a certification process, and the pharmaceutical industry has created a model for this, where nothing comes out in the pharmaceutical industry without clinical trials, blind studies. So we probably need some form of regulation like that for AI, where a new model will not hit the world without some sort of blind studies and clinical trials. At the same time, unlike pharma, we can't take years for a model to be approved. So it's one where I don't envy the role of administrators who are simultaneously trying to learn the technology, learn the implications, come up with very thoughtful approaches of putting the guardrails, which makes it imperative for us in the industry to partner with the government to show them what we know, the power, the pitfalls, etc. And then help them come up with meaningful regulation on the subject.

Andrew Grill: 18:24

I've always said it's got to be a partnership. I've had a couple of experiences where I've been able to present to the regulators, to the lawmakers of governments here in the UK, and what I say to them is you need to think like a startup, you need to partner yourself with people generating this technology this is way before chat, gpt and understand why they want to in my term break some rules. Why do they need some freedom to operate? And I think when industry and regulators get together you made a good point the regulators need to almost learn faster than the developers of this technology how it all works, to be able to have the guardrails. But generally, a regulator isn't going to go and work for a government organization that is an AI expert. So I think that's a real issue, but probably not something we can solve. On the podcast today, I wanted to turn more to your own solutions. At Unifor, you've got several solutions conversation AI, emotion AI, generative AI and knowledge AI. Perhaps you could give us a flavor for each of these and what problems they solve.

Umesh Sashdev: 19:20

We think of these as Lego blocks of technology and AI models which we've architected as a platform. What does that mean? One of the things we realized very early on working with even our early customers and that has remained true till day Now that we have over 1,500 enterprises using our technology is that when we approach any of these enterprises, they get very excited about the potential of AI and we go okay, we're bringing our full prowess of the platform. Please give us access to your data, because we don't want to bring in publicly trained models. We want to use enterprise approved data. Then we realized that these enterprises have never arranged their data in a way that's ready for AI. A lot of the data we get are unstructured forms of data FAQ documents, standard operating procedure documents, historic recordings of calls from the call center, etc. Which are all unstructured forms of data.

Umesh Sashdev: 20:24

Several years ago, we started investing in this field of AI called Knowledge AI, which takes in all forms of unstructured data, creates a knowledge graph from whatever it's seen in those documents and those call recordings and outputs a very structured form of what it learned from that data. We then take this as a pipeline and feed the structured output into our Generative AI models. In Generative AI we use all T-shirt sizes. These models come in a small, medium, large and extra large size, depending on what we want them to achieve. But the data that's going into Generative AI is being filtered through our Knowledge AI models. We then discovered, like I was mentioning in the previous question, that to add a layer of judgment, to add a layer of understanding of human emotion, our state of mind, empathy, sarcasm, anger, even especially folks when they're calling into the call center with the complain, and so on and so forth, it's not enough to have Generative AI become a task machine. Uniform is also known pretty widely in the academic circles for the work we've done in Emotion AI, when we've taken the changes of tone via voice. We measure using computer vision if it's a video meeting, facial emotions, gestures, body language, etc. To train the AI model on understanding human emotional states. We make all these three forms of AI Knowledge AI, generative AI, emotion AI work in tandem. They work in real time. What do they actually do in the enterprise?

Umesh Sashdev: 22:14

We have four types of benefits that enterprise derived from our technology. First is in the contact centers where, for the most part, uniforms technology is a co-pilot to the men and women who work in these call centers. Dan and I are receiving your and my calls when we have a complaint, we have an issue, we need urgent help. There's somebody on the other side of a 1-800 number that picks up the phone. Uniforms AI is working as a co-pilot on that call, understanding the issue that the customer is making or verbalizing, then, through automation, finding very quick answers to that problem. Even before the human agent could have put the customer on hold and tried to find the solution, the AI finds it and delivers a great outcome. The call is shorter, it's more efficient and it's a better customer experience.

Umesh Sashdev: 23:05

We, then, have applied this co-pilot technology in sales automation. We found salespeople when they're trying to sell to their customers on Webex and Zoom and Teams meetings. It's very hard for them to read the room on this virtual setup. You have a presentation in the middle, you have 10 people who are attending the call. You can't watch if people are receiving your message or they're still confused about the topic, etc. Once again, uniforms AI acts as a co-pilot to those salespeople on these virtual meetings. We, then, are servicing lots of government departments. We're servicing police departments, ambulance services in the UK, nhs etc. Are using us in their phone lines as an AI technology. Finally, we have customers in trading desk terminals, folks who are constantly on their trading computer screen along with phone lines. A multitude of places where the enterprise has either a phone conversation or a video meeting going on in their frontlines is all places where Uniforms technology is aiding and assisting those departments and those employees to be more efficient and deliver better customer experiences.

Andrew Grill: 24:19

You make a great point there. In my AI talks, I labour the point about having data that's AI ready. I ask my audiences three questions what's the data you want, what's the data you need and what's the data that you'd like? What format is in? Who owns the rights to it? How do you access it? I see the audience pausing and going. We hadn't thought about that. We just thought it was all magic and that the data we've got in the company can actually be ready to be ingested. I want to talk about training. Once you've got the data, you've got to start training it. Ai training bias is becoming more evident as these public large language models are trained on, I would say, questionable data. What do you think should be done in this area to ensure that training data is less susceptible to bias?

Umesh Sashdev: 25:01

Well, you lead me to a very important subject. This is one that we'll have to be very mindful of as long as AI is going to be in our life and for posterity. Ai bias and training bias is a very real threatened issue to the efficacies of these models. Once again, we spoke about the largeness of these data sets large language models, large multimodal models. Those are what are powering modern day generated AI technologies. When you have billions and billions of parameters training a single AI model, very likely that some undesirable form of data enters your models. If it were a consumer app, the risks are you could train it on pornographic materials. You could train it on age and appropriate materials. In the enterprise, the risks are even deeper. They're about biases towards diversity, inclusiveness, etc. Let me give you an example.

Umesh Sashdev: 26:11

One of the models that Jennifer focuses on is Emotion AI. We use computer vision models on video meetings, on Webex or Zoom, etc. To pay attention to human facial emotions as being conveyed by the participants on that meeting. Now, as we were training these models, if our researchers did not pay attention to being very inclusive in their data sets, right then to include faces and facial emotions of people with different races, genders, skin color types, size of eyes, etc. If those variations were not taken into the training model, very likely that the model, when it started to work within the enterprise, would start to give a biased output when, if it's encountering a type of face with a skin color type or size of eye that it was not trained to look at, it might misread their emotional state during a meeting and that could cause major problems, potentially even legal issues. Similarly, these generative AI models you're teaching them language, human language, human communication, and if you're not paying attention, they can start to pick up, for example, cus words. Now, when people are angry, they speak to each other in a certain way, the AI model starts to learn how to be angry at us, etc. And so what is becoming a very fascinating subject?

Umesh Sashdev: 27:42

As you and I know, the social media companies met up or Twitter and so on and so forth.

Umesh Sashdev: 27:48

We've known them to have very large teams of trust and safety executives, armies of people whose job was it to try and catch these undesirable forms of data and untrain it from the AI model, and these concepts were used in social media companies.

Umesh Sashdev: 28:06

The very same concept is now coming into AI companies, where someone like us or a chat, gpt or anyone creating their own proprietary models now need to have a trust and safety department within the organization whose core job should be to find improprietaries, to find undesirable data that's inadvertently entering the model and take it out as soon as it's found or cited, so that the bias is controlled. So it starts with all of us being very conscious. If we are creating a model, we have the responsibility to be very conscious that our models could have biases of different types. And once you're conscious, we have to make the investment, whether people investment or technology investment, to make sure that those biases can be minimized as much as possible. And yet again, because of the nature of this technology, the pace of innovation, it's almost impossible to completely make do away from it. But as long as there's consciousness on the part of the provider of the model, many measures can be taken to minimize the risk of biases in these AI models.

Andrew Grill: 29:27

Gartner's latest hype cycle for AI that's just been released puts Genitive AI at peak hype no surprises. So when do you think it will move to the plateau of productivity and what will it take to get there?

Umesh Sashdev: 29:38

If you think about what's happening in today's world. Unifor is the first company with examples of over 1,500 enterprises using us for productivity gains in their call centers, et cetera. Our largest customer, which happens to be a financial services organization in North America, has 65,000 of their call center agents using our technology. Our second largest customer is a large telecom service provider and 26,000 of their users use us in their call center, and we have many such examples. Why do I bring these up? You and I both know that when these enterprises adopt any new technology, not just AI, they first start with a small proof of concept which becomes a pilot, and then a different department is added to that pilot. By the time, a technology is hitting scales of 50,000, 60,000 users, like the examples I just cited, which means the technology is delivering proven return on investment. It's delivering on business outcomes and Unifor is one of the first companies proving it at its enterprise scale that this technology is ready for prime time. It is delivering efficiency gains, et cetera, and we're going to see an accelerated trend in the coming months of more and more enterprises, more and more vendors, beginning to find ways to make it happen within the enterprise.

Umesh Sashdev: 31:03

You asked me a question what will it take for the technology to move from the peak of the hype cycle to the plateau of productivity? I think here's what it will take, andrew, from my vantage point. I talk to many CEOs who are very forward looking, who push us, and we think we're a fast moving Silicon Valley company. Some of my customers, who happen to be CEOs of larger businesses and traditional businesses, are pushing us to move even faster. What I know is some of them who are captains of industries, whether it's cybersecurity or health care, and these happen to be publicly listed, publicly traded companies At least some of them in the forthcoming quarters will, in their earnings meetings, earnings results, declare productivity gains because of their use aggressive use of generative AI, and all it will take is one large company in each of those segments to set the tone with the investors.

Umesh Sashdev: 32:07

We saw what happened when a social media company last year which was publicly traded, got taken private by one individual and then that individual, for right around reasons, decided to have massive headcount decreases in that organization. It forced every other peer company of theirs In social media, in the internet arena. The pressure from Wall Street was if one company can do it, you all should do it and we saw a series of headcount reductions in layoffs. Generative AI is actually more productive than that. However, once one CEO, one company, shows the potential of this technology in their earnings call in one of the upcoming quarters, wall Street and investors will be the biggest force putting pressure on every company to use such technology to drive productivity gains. And I think we are at the cusp of it. So I won't be surprised if, in less than six months from now, you and I are having a chat about this topic and we are saying look how fast we move from the peak of interest to this plateau of productivity in the hype cycle as we know it.

Andrew Grill: 33:22

So you're in a unique position because your solutions actually work deeply inside the organizations you've mentioned to solve these problems. But for others that don't have that deep connection, for example, Microsoft are launching their Microsoft 365 co-pilot AI, which integrates directly with products such as Outlook and Word right in the daily workflow. So how can enterprise AI get closer to the day-to-day activities we're already doing and become more seamless?

Umesh Sashdev: 33:47

It's really important to reinstate that the real power of this technology is truly profound. The outcomes that we can imagine are unbounded. Every facet of an enterprise job is likely to benefit, with productivity gains, automation, efficiency increases, and it could be as simple as how do we type emails to our customers? How do we send emails to our employees? How do we communicate within the companies? How do we communicate with our customers? Do we communicate with them over phone lines and call centers? Do we communicate with them on video meetings? Or do we meet them in person at stores and so on and so forth? And so the day-to-day activity of every single employee, right from the CEO to the youngest intern who joined the company, is, in my opinion, very likely to be impacted and benefiting from the user-generated AI. If they're not already doing it, it's going to happen very shortly. Like you said, companies like Microsoft, et cetera, who work on these productivity tools like emails and other forms of communication, are bringing that technology. We play in the arena of customer experience, employee experience tools anywhere in the company where there's a phone conversation, a video meeting and so on. So far, we are bringing those tools. There are many companies who are bringing generated AI into back office operations, accounting, billing, legal documentation and then shutting down this whole tailwind like small companies, the mother of your use cases in the enterprise.

Umesh Sashdev: 35:30

You spoke about enterprises not being ready with data.

Umesh Sashdev: 35:33

Data is everywhere except it's not available when the enterprises need to access it.

Umesh Sashdev: 35:38

Enterprise search the ability to arrange enterprises data from any part of the company, whether it's in some silo in a different branch office or it's in a CRM or if it's in the billing system.

Umesh Sashdev: 35:53

The ability to put it all in a single data domain. And then, using Generative AI and all the techniques that we've spoken of today Knowledge, ai, et cetera make it available through a natural language search interface. But imagine the ease, instead of going into file rooms and trying to look for an old record, if you could chat with the bot and say I'm looking for that old file, which might be a 20 year old record of you know, something we did back then, can you please find it for me? And boom, in 60 seconds that data element is in front of your machine or your cell phone as an output of that chat you did with that interface. So I repeat, the possibilities are unbounded and as we speak right now, andrew, there are many different innovators, many companies who are working on different pieces of this puzzle, and that is the power that Generative AI has given us that we're moving with innovation at a pace that we have not experienced in any technology revolution in the past.

Andrew Grill: 37:00

You quite rightly say that the real power of Enterprise AI is being able to find that needle in the haystack. And I think back to chat GPT, the fact that people are saying we can use this on public data. And I say to my clients imagine if you could do that with your own data and the light bulb goes off. But to your point before. When people then scramble and say, okay, let's do an Enterprise AI project, I then warn them about the cost, that it's probably a 10X in terms of compute power. The data's got to be in the right place. You need data governance. So what would be your advice to someone listening to the podcast that's seen the power of Generative AI through a chat GPT wants to move this into the Enterprise. Where should they start and what are the pitfalls to avoid?

Umesh Sashdev: 37:41

Several of the CEOs have come to me with the same question. It's first, it's really important to get a core group of people, almost a Tiger team, formulated around the CEO, who are both empowered and excited to drive the change, drive the transformation within the organization with this initiative. It's not just a technology initiative, it's a change management issue within these enterprises. It then becomes important to paint a big, hairy or decious goal, almost a big vision, which it can excite the whole workforce to drive the not star, to say, in the next few years, using AI, we're gonna be a company, we're gonna be an AI company, we're gonna be a company that will be very efficient, we're gonna be a company that delivers the best customer experiences, and so on and so forth. Then break that vision down into smaller chunks of projects. What's achievable in the next six months? What are the low hanging fruits?

Umesh Sashdev: 38:39

There are areas like call centers and customer service which are extremely ripe. The employees there are crying for tools. The employees don't like putting customers on long holes only to search for the answer to their questions. The customers don't like that long experience when they're in a hurry and all they need is a quick answer to a question of when's the next slide, etc. Or why was I overcharged on the bailing? So there are departments and there are low hanging fruits of efficiency gains.

Umesh Sashdev: 39:10

And so, having created a team, having defined a vision, having found the first high impact but easy to understand area to deploy this at example, customer service it then becomes imperative for these companies and CEOs to over communicate at every step of the way, to over communicate with the customers, with their shareholders and with their employees.

Umesh Sashdev: 39:35

In my opinion, it takes six to seven quarters and seven repetitions of the same message by the CEO For everyone in the company and the community to understand and buy into the shift that's been caused you. And so not only doing it one time, not only saying we have an intention, but showing it repeatedly over six or seven different quarters, six or seven different earnings, six or seven different messages, et cetera, is what's needed to land the message. And then the flywheel rotates very fast If done right. The idea generation of where to apply this next can be crowdsourced from within the enterprise. But for that to happen, it's important to first sell the message really strongly and show and lead by example, show with a couple of true points in one or two areas and deliver the results.

Andrew Grill: 40:30

You say the future of enterprise, ai, is human. What do you mean by this?

Umesh Sashdev: 40:35

Well, this is the first time that we have the ability to imagine a world where we, as human beings usually we are told to learn a new skill and when a new technology has been released, we have to retrain ourselves. We have to unlearn our past habits, retrain ourselves on a new technology and your tool. And we are just so. If you think about philosophically that phenomenon, that phenomenon is us human beings adjusting to technology. We actually think that to unlock the real power of AI within enterprises, it will truly have to be that the technology adapts to the human beings who work in those enterprises. That is when we will have the full buy-in of the human workforce in every company, every department, everyone cherishing and, you know, being enthusiastic about the technology. And that is why, as our vision, unifor, we've said the future of enterprise AI has to be human. What does that mean? We spoke a lot about Generative AI becoming a relentless task machine. A relentless task machine that today does not appreciate human emotions, does not appreciate every nuance of how human beings communicate with each other.

Umesh Sashdev: 42:01

You and I don't just send each other chat messages. When you and I speak on phone or a video meeting, we change our tone. We can be excited, andrew, have you seen this new release by its own company? Or we say, hey, that is so dangerous, somebody should be paying attention. Just by changing our tone, we communicate with each other in addition to our words.

Umesh Sashdev: 42:21

When we meet in person, like we did in London and we sat down for breakfast, or we're meeting on a video meeting, we're looking at each other and we're responding to each other's energies and facial emotions, and it's truly important that AI meets us where we are as human beings, as opposed to asking us to adjust our habits. We want the AI to start with appreciating human emotions along with the words, along with our language, and then, hopefully, there'll be a day where, using Generative AI, we can teach AI to also generate its own emotions and thereby being a great companion, a great personal assistant, whether in our daily lives or in the enterprises. And so our vision of Enterprise AI is human Really means that we will continue to push boundaries, push the envelope, not settle with the fact that AI is a great productivity tool right now. But AI, for AI to be a companion for each of us in the enterprise or personal lives, it has to fully understand the nuances Of human communication.

Andrew Grill: 43:27

We're almost out of time. We're up to my favorite part of the show, the quickfire round, when we learn more about our guests iPhone or Android, iphone Window or aisle.

Umesh Sashdev: 43:35

I'm an aisle person.

Andrew Grill: 43:36

In the room or in the metaverse, always in the room. What's the first thing you ask? Chat GPT.

Umesh Sashdev: 43:41

I ask it about what does it know about me?

Andrew Grill: 43:43

Your biggest hope for this year and next.

Umesh Sashdev: 43:45

That we meaningfully put guardrails around AI and safely introduce this profound power on citizens, on employees in different parts of the world.

Andrew Grill: 43:54

I wish that AI could do all of my.

Umesh Sashdev: 43:57

Well, it could follow up all my emails and tell me the ones that really need my attention.

Andrew Grill: 44:00

The app you use most on your phone.

Umesh Sashdev: 44:02

I'm constantly looking at the weather app.

Andrew Grill: 44:04

The best piece of advice you've ever received.

Umesh Sashdev: 44:06

It was from a mentor of mine who taught me early on that as important as it is to build a business and lead a company with IQ, EQ is equally important. So intelligence and emotional intelligence go hand in hand.

Andrew Grill: 44:20

What are you reading at the moment?

Umesh Sashdev: 44:21

Well, I'm reading this book. It's called the Jolt Effect and it's a very fascinating book for all of us who sell to somebody, and it's about what causes a customer's indecision or the inertia to maintain status quo, even if they know that it's right for them to adopt something new or change to something new. It's very pertinent in this era where we're pushing Generative AI and a new concept to our customers and you see this every day where the customer will tell you I'm sold, I'm really excited, let's move. And then they go on a radio silence. And this book, the Jolt Effect, talks about how do you jolt your customers if you truly believe in the topic how you jolt them into realizing it's important for them to make a decision now.

Andrew Grill: 45:14

Who should I invite next onto the podcast?

Umesh Sashdev: 45:16

Well, I'm a big fan of your podcast. You've had many interesting people you cover. Your style is very interesting to me. You cover a range of topics but, given the world we live in, there are many fascinating people. I'd love to hear from Elon Musk. Would be on top of that list.

Andrew Grill: 45:31

It's not the first time someone suggested that. So, elon, if you are listening, please return my calls. How do you want to be remembered?

Umesh Sashdev: 45:37

I want to be remembered for somebody who is passionate about making technology work to make the world a better place.

Andrew Grill: 45:44

As this is the actionable futures podcast, what three actionable things should our audience do today to prepare for a world of enterprise grade AI?

Umesh Sashdev: 45:54

First, it's really important to understand any subject and move from being anxious about it to being very comfortable and excited about it is to read, to educate yourself. Andrew Gill's podcasts are one great area to learn about the subject. There are many books and information out there about generative AI out there. So first it's really important to educate ourselves. Next, it's important to understand our rights data protection rights, etc. Even before any new regulation is released. Most countries give tremendous protection to us as citizens, employees and so on and so forth, so it's very important to feel comfortable that we are protected. And third then, just be curious. Be willing to learn something new every day, be willing to try a new technology that's put out. I love the story you just told me, Andrew, that your parents in Australia, at a late stage in life, are willing to be curious, learn something new and change the world.

Andrew Grill: 47:01

As you know, my next book is going to be called Digitally Curious, and I hope people are curious enough to look at it and hear from leaders such as yourself, umesh, a fascinating discussion. How can we find out more about you and your work?

Umesh Sashdev: 47:12

We try to be very public. The company I run as CEO is Unifor. We have a simple website, wwwuniforcom. On LinkedIn, if you put my first and last name, pumir, to such day, you'll find my LinkedIn. I'm on Twitter, which is now called X, and my email is available on several of these sources, so I'm always open to listening and receiving messages from people. If somebody has a question, I'll be happy to hear from them and be very quick in answering those messages.

Andrew Grill: 47:45

Umesh, always a delight to talk to you in person or online. Thank you so much for your time today.

Umesh Sashdev: 47:50

Andrew, it was a pleasure, thank you.

Intro: 48:27

Thank you.