Gartner's Top Tech Trends for 2026 | Live From IT Symposium/Xpo

0:00Good morning. I'm Gene Alvarez. And I'm Tori Paulman. And I think you'll agree, Gene,

0:07it's been a hell of a year. Oh, how many of you feel that 2025 has been a year filled with chaos,

0:14right? We've had geopolitical disruptions. We've had economic disruptions. You've had talent

0:21challenges running around trying to find these prompt engineers, people who can work with AI.

0:26We've had more emerging technology come at us this year than we ever have before. You've also

0:33had to deal with the changing cyber security environment and all the challenges it has

0:38brought. This has been a year filled with chaos. Now to do that you needed to be a superhero. And

0:49in 2026 your superhero journey continues. And if you think about it, you're probably thinking, "Oh,

0:58Superman or other superheroes like that." Well, my superhero is Frodo. Frodo in Lord of the Rings

1:05has to go to Mount Doom. But Frodo doesn't do it alone. Frodo has others with them. And

1:12you're going to find others who help you drive responsible innovation, operational excellence,

1:18and digital trust. And you're going to meet three new companions. The first one is the architect.

1:26The architect is going to help you with AI platforms and infrastructure. Then you're

1:32going to meet the a the synthesist who's going to help you with AI applications and architecture.

1:40And finally, you'll meet the vanguard who's going to help you with security, trust, and governance.

1:47Now, I'm going to answer the most popular question we get about this research every year. So this way

1:53everyone gets the answer all at once and that answer is how'd you come up with the list right

2:00now. Gartner tracks over 650 emerging markets. We follow where the VC funding is going. We do

2:09an analysis of all the patents submitted and then we have over 130 hype cycles and 2,000 innovation

2:18profiles that we go through. We also do an open call for submissions to our 2,500 plus analysts

2:26and ask them to submit so we can find out the ones in their desk drawers that they haven't even

2:31started to write about. And then finally, we pick the list. Well, our desk drawers are full, aren't

2:38they? Yes. Now, we're going to tell you not just about the trends, but really how long we think

2:44it'll take for them to mature. in the along the way we're going to like highlight some real world

2:48stories but you need to know that these are early days for these trends and there aren't hundreds of

2:54examples of other organizations getting it right but that gives you an enormous opportunity to be

3:01the first to tackle these trends. Now the time horizon for this year's trends include now like

3:08right now in the next 1 to 3 years and then we have near which is trends emerging and maturing

3:15in the next 3 to 5 years and then we have the far out future trends that won't mature for probably

3:215 years or more. Now each of these trends are represented by one of our superheroes. Now you

3:29may have noticed something. There are no trends in the far out future category. Well, this is because

3:37investors have noticed that there isn't a single day that happens where we're not talking about AI

3:43and the invisible hand of economics is out there driving these markets at full speed. In fact,

3:49the pace is so intense that Gene had to update the trends from last year just because of geopolitical

3:58shifts. Now, what does this mean for you? The most important thing for you to know is that yes,

4:04market conditions may speed these trends up, but they may also slow these trends down. Now, as Gene

4:14and I walk you through the trends, we're going to give you three key pieces of information. First,

4:21what is it? Mhm. Second, why is it important to you now and also in the future? And third,

4:30what should you watch for and do? Now, this is more important than ever before because time is

4:37changing. The pace of innovation is changing. And in fact, if you think about the next wave

4:43of of innovation, it's not going to happen next year. It's going to happen here this week. So,

4:50let's get to know our first superhero. So our first superhero is the architect and in the

4:55architect we're going to see AI development platforms and AI supercomputing platforms.

5:01So let's take a look at our first trend AI native development platforms where people and AI team up.

5:09Now here is where we're going to see that AI now will join programmers. Each of your programmers or

5:17developers is going to have a Jarvis to work with them. And what this is going to do is it's going

5:23to give you a platform creating software where AI is a member of your team. Now these agents

5:30will help build alongside your teammates. So now think of this. You have 10 developers working on

5:37one project. Imagine now if we can have five teams of two partnered with AI and these tiny teams now

5:48can deliver five projects instead of one. Now why is this important? Well, this is important

5:56because we've already seen this change the startup culture. One of the reasons why we had so many new

6:02technologies this year to go through was that we were seeing startups with very small teams using

6:07AI to create these new offerings for you. We're also seeing that this is going to be the solution

6:15to your developer productivity problem. Think about it. How many of you in this room do not have

6:21an application backlog? Right? No one. Everybody does. So this is going to be solution for that

6:28productivity problem. It's also going to help in terms of bringing non- tech developers or business

6:36users into these teams for development creating applications specific for your organization. Now,

6:44what are some of the things to watch for and do here? Well, when it comes to those things that

6:50you need to watch for and do, right, first off, we have platform teams now. So you may have those two

6:58developers working with their Jarvis who work on a specific software platform and they develop all

7:03the applications on that platforms. So you'll see them organized in this way. But now because you're

7:09producing more and more applications, you're going to have to make sure that security guard worlds

7:15are also built into this development process. And then now when you look at your application

7:21backlog, many of us think, well, I'm going to get an application from the cloud. You may start to

7:26think first, can I have a tiny team build this application for us so that it can differentiate

7:34our organization in our industry? Okay, so we have supercharged tiny teams, but they still

7:41need to find AI resources in order to develop and innovate. That's where AI supercomputing

7:48platforms come in. Now, where I'm from, when I grew up, this is what directions sound like.

7:54Okay, you're going to want to turn left where the old country store used to be. Right, this

8:01is a map drawn from memories and legacy knowledge. Now, AI supercomputing platforms are a little bit

8:07like GPS, right? They combine accelerators, orchestration, and high-speed infrastructure

8:13to help your developers develop in real time. So these things make realtime decisions routing your

8:21um your AI development to the right platform at the right time. This then hides all that

8:27AI complexity from the developer. So why is this important? Well, we all want our AI running in the

8:36most optimal uh you know compute environment. One where we can think about cost and um and efficacy,

8:43right? But I like to think about trying to find a parking spot in a crowded city. It's often

8:50very frustrating and very time consuming. These platforms boost that speed and efficiency. And

8:58the examples that we're seeing uh Gene are things like biotech companies using AI supercomputing

9:04platforms to model vaccines and therapies in weeks instead of years. We're seeing financial services

9:10companies use these to model risk portfolios so that they can de-risk their portfolio management

9:17process. And we're seeing energy companies use these models to to map extreme weather so that

9:24they can optimize their grids. So what could you watch for and do? We'll start by pinpointing

9:32where your high AI compute traffic jam is and focus on those areas because that will enable

9:38your developers to develop faster. You should explore how hybrid architectures and modular

9:44infrastructure will support your most important use cases. But remember, this isn't autopilot.

9:51And I think we all we all have good, you know, GPS experiences today, but you all remember the

9:57people who drove into the lake, right? By the way, that happened three times. So, you need to keep

10:04yourself out of the lake by building new skills for securing and governing composable platforms.

10:12So, our next superhero that you'll meet on your journey is the synthesis. And here we

10:18have multiagent systems, domain specific language models, and physical AI. So let's take a look at

10:27multiagent systems. Now, I know this one may not be a surprise to you. Some of you may already have

10:32one agent in your organization or two. If you have two, you're into the world of multi-agent systems.

10:38But one of the things about multi-agent systems is that they're modular and that they handle specific

10:44tests t tests. So, for example, an F1 team, the pit crew, everyone handles a specific task when

10:53that car comes in. I handle the left front tire, Tori handles the right front tire. We specialize

11:00in it and we are very good at it which means we can reduce hallucinations but yet support

11:07a complex workflow and we're orchestrated in a dynamic model because this car is going through

11:13different things in the race and when it comes in there are different things we may have to do to

11:17improve its performance and that orchestration is what happens with multiagent systems. Now

11:24we're going to see this on single platforms. So you may have a single platform provider that has

11:29multiagent capabilities, but then we're going to go to cross-platform. So we're going to have model

11:35context protocols to look at agent protocols to look at and then we may even see the dawn

11:43of an internet of agents. Now what to watch for and do here? Well, one of the things that you'll

11:51need to do is start building multiagent systems now. But build your agents small,

11:57specific. Think of that F1 team and how specific they are in their test. Don't build these large

12:06monolithic agents. They become too hard to manage and they bring in the ch the challenge of having

12:12hallucinations or other problems occur. You want to also make sure that you don't think of these

12:20multi-agent systems as a human. They augment a human. They work alongside with them. They

12:28will be dynamic and be able to call each other as needed to handle your organization that F1

12:35formula car. I'm sure glad you're human. Now, you heard about context in the keynote this morning,

12:41and we know that most large language models have gobbled up everything. They know everything about

12:47Taylor Swift, and they know everything about finance. They're a little bit like the Library

12:52of Congress in my mind. They're packed with every single book that's ever been written. But domain

12:57specific large language models are a little bit more like the the New York University law library,

13:03right? They're specifically focused on the task that you need to do. Now, think about

13:09the vast amount of clinical trial data out there for those of you in healthcare. Just to put this

13:14in perspective, last year alone, there were more than 350,000 clinical studies produced. Wow. And

13:21I did a little math. Uh, sorry, there were 50,000. And the math is going to make more sense now. So,

13:26did a little math on this, right? If there were 50,000 clinical studies produced last year,

13:31how many full-time employees would it take to read those and inform your scientists? Well, that is

13:37that number is 350. Now when we think about what the value is to organizations, well it's obvious

13:45they spend less time searching. They get better results. So what do you you know what do you

13:50need to think about in terms of its importance to you? Even if you're not in healthcare, let's think

13:55about those of you in government. When do builders find out that they're out of code compliance? At

14:03the inspection, right? Or we could say just way too late. CIOS are sitting on a value gold mine.

14:12You have the ability to build domain specific large language models as a digital service.

14:18Now imagine a building code compliance model that both inspectors, employees, and consumers,

14:26builders can use. That would be very valuable. And in turn, that's going to improve the trust,

14:30security, and adoption that you're after. Mhm. All right. What should you watch for and do?

14:36Well, the most important thing you need to do here is be transparent about what your model

14:41knows and doesn't know. Like for example, who Travis Kelsey just proposed to. Now, you're not

14:49going to have to hire 350 clinical study readers. That's good news. But you will need new roles for

14:55context and machine learning. For example, you'll need a context engineer who is constantly feeding

15:02your model with the most appropriate sources and ensuring they're always up to date. And you'll

15:07need a machine learning specialist who monitors for what's called catastrophic forgetting. And

15:12this is where the machine forgets when they learn something new just the way that humans

15:17do. So our next trend here is physical AI. And this is where digital rubber meets the road.

15:25Now, a popular question that I get about physical AI is, "How do I know if it's physical AI?" Well,

15:31here's the very complicated test you will do. If you can pick it up and throw it out the window,

15:38it's physical AI. It's that simple. Physical AI is AI designed to interact with the physical world.

15:49Your Roomba, it's designed to interact with the physical world and it senses what's around it. It

15:56can act in that environment. It works alongside you while you're living there with your Roomba.

16:02It shares that physical world with you. Now, why is this important? Well, it's important

16:09because as we move into the physical world, there are challenges moving into the physical world,

16:15whether we're using robots, drones, or we're using devices. So, in the case of robots,

16:22I can have a robot that will do one of the things that my family hates to do, clean the table after

16:28dinner, right? So it can decide well what stays on the table like the salt and pepper shaker,

16:35what goes in the dishwasher, hopefully the dishes, and then things that go in the trash. It's going

16:41to have to work in that physical environment. But at the same time, I could be an energy company

16:45and I'm using drones to maintain clear lines. So what it's going to have to decide is what's the

16:51difference between a power line and a tree branch. And hopefully it only cuts the tree branch.

16:57And then lastly, my Roomba. My Roomba can tell whether if it's cleaning a tile floor, a rug, it

17:04can sense that there are cords, stairs, it might go down, and it can even discover the surprise

17:11package my new puppy has left me. All right. So, as we move into the physical world, we're going to

17:18deal with unpredictability. And that means that these models are going to have to learn in what

17:25they do. Which means occasionally a salt paper pepper salt and pepper shaker might wind up in

17:31the trash. Hopefully we don't cut any power lines. But testing and learning what we have to do in the

17:37physical world requires that learning process. So in physical AI, remember if you can throw it out

17:45the window, it's physical AI. One thing you don't know is that Gene's wife has a very special salt

17:51and pepper shaker set. And it would be devastating if that got thrown away. Something you learn when

17:56you're working with your colleagues on a project. Yes. Yes. It's a pair of roosters that we got in

18:01Spain and I'm not allowed to touch them. Amazing. All right. So, let's meet our last hero. It's we

18:08need a secret hero uh for the salt and pepper shaker. This one. The Vanguard's job is to keep

18:14you ahead of the curve through uh preemptive cyber security, digital provenance, and geopatriation.

18:21These are exactly the skills your organization is going to need to verify authenticity and safeguard

18:27your assets in, let's face it, a rapidly shifting world. Now, let's start with preemptive cyber

18:34security. Who remembers the Minority Report? Probably everyone. Now, in case you haven't

18:40seen it, and if you haven't seen it, I'm going to spoil it here, but uh in case you haven't seen it,

18:45Tom Cruise plays a police detective in what's called the pre-rime unit. He works with humans

18:50who are gifted with precognition, who predict violent crimes just moments before they occur.

18:58This giving the police the opportunity to arrest the criminal even before they do the crime. Now,

19:04I don't know what's more terrifying, what I have just described, the fact that this movie is 23

19:10years old or the fact that Tom Cruise has not changed at all. Don't know. It's an avatar. Yes,

19:19exactly. He's an avatar. Using preemptive cyber security means using AI powered sec ops. And what

19:27I'm talking about here is a world where prediction is protection. Now, why is this important? Well,

19:35I think we all know that most cyber security tools are reactive in nature. In fact,

19:40almost 80% of the market today is buying tools that help you react as quickly as possible.

19:48But that mean that means that you've only you'd only know you've been breached once the the damage

19:52has begun. Right now your threat actors are already using AI to attack you more, you know,

19:59with more intelligence and more precision. Preemptive cyber security is about using

20:05that same power against them to anticipate, deny, disrupt, and deceive. Now imagine this. You get an

20:15alert on your phone that there's a possibility of an attack on one of your servers. Automatically,

20:21your AI SecOps is deployed, creates a new honeypot server, lures that attacker in, and and

20:27keeps them safe. Now, they think that they were successful, and you have contained the threat.