What Made the 2022 Gartner Top Strategic Technology Trends List
welcome to thinkcast i'm casey panetta i'm joined today by arun chandrachukhan vp analyst to talk about this year's top strategic technology trends this is one of our most popular research pieces and symposium sessions so i definitely recommend visiting gartner.com or checking the show notes for a link to download the ebook on today's episode we're going to talk about six of the 12 trends plus the big three themes and how executives can use this research in their planning thanks so much for joining us arun thanks for having me so this is one of my favorite pieces of research i feel like i say that about a lot but i really truly mean it this one is really interesting every year this year there's 12 trends how do you select those 12 trends well there are several inputs we consider to arrive at these trends you know there are more than 2 000 analysts at gartner they all have an opportunity to nominate these trends we discuss them widely in our research communities and they vote on them we also look at a lot of quantitative data we look at inquiries we look at survey data particularly our cio cto and board of director surveys and we also look at published research you know such as hype cycles and technology runners we're also humble enough to admit that we don't know everything we also talk to a lot of external ecosystem participants you know for example academic institutions research labs and large vendors venture capitalists startups so this effort is a cumulative effort that triangulates all of this the one thing that i want to add here quickly is this year was truly unique in many ways the past 18 months have triggered faster change than ever before as you know hence we had to do some tightrope walking which is to ensure that the trends reflect the fast changing priorities of cios but at the same time we are actually picking trends that are long-term technology bets that organizations need to make so the 12 trends actually fall under three bigger themes can you talk about those three themes and how they all work together yeah you're right we've categorized the trends across three themes engineering trust sculpting change and accelerating growth the obvious question is why these themes we believe that these themes closely align with the ceo priorities and as outlined in the ceo survey that we do annually and i'll talk about a little bit about those you know priorities now ceos want operational efficiency so there are trends in this research that align with those goals of enabling a secure scalable and a very efficient digital foundation ceos are clearly interested in investing in digitalization hence i believe that cios and ctos need to invest in disruptive and emerging technologies to sculpt the change finally ceos want to accelerate growth maybe you can even argue that they want to recoup some of the lost growth over the last 18 months hence chief information officers and technology officers should be investing in trends outlined in this research that can accelerate top line growth through product innovation and better customer experiences i know one question that we've gotten a few times when we talk about this research is that some of the trends on this year's are completely new to the list and some of them have been featured maybe once or twice before why do some of them appear more than once and why do some only kind of pop in and out for a year yeah that's a good question these trends reflect long-term disruptive bits that we believe customers should be paying attention to hence i believe this is about the future it's not about trends that are mainstream today we revisit these trends every year you know some mature and become mainstream so we drop them while there are some rare ones we drop since they may have failed to deliver on the promise hence the trends appearing consecutively is by design you know however we do update our guidance to clients based on new use cases or new technologies or new ecosystem developments that we observe in the market i want to take a quick break to remind you that you can access all 12 trends plus real world examples and next steps for executives in our ebook the link is available in the show notes or at gartner.com one of my favorite things about doing this podcast is i always get to ask the analyst questions that i have always been wondering but do you have a trend on this year's out of the 12 that you think is particularly interesting or is your favorite well i believe all of them are interesting but there are trends that are interesting to me as an analyst and there are trends that i believe clients should be interested in due to the transmitter potential that they have maybe there's also a bias inside me towards you know trends associated with cloud and ai since i covered that as an analyst but putting my bias aside i think composable apps is an interesting trend because it closely aligns with the cio priority of business composability business composibility is this mindset and you can even argue a set of operating capabilities that enable organizations to innovate and adapt rapidly changing business needs which is something that we saw a lot in the last 18 months so you could argue that business apps and business composibility are an antidote for volatility if you want to build long-term strategic successful companies i think having business composability is very critical as you said there's a lot of trends and they're all obviously very interesting so i want to get into a few of them cloud is obviously a really big topic of conversation for everyone now and one of the challenges there is that sort of lift and shift mentality so i want to talk about cloud native platforms and how they help with that and what they are and and what executives need to do with them yeah absolutely um i truly believe that software is eating the world and cloud native platforms allow organizations to build and deliver software faster cloud native platforms sit at the intersection of cloud and devops in my opinion which is they enable organizations to build software faster and hopefully by the virtue of that they can deliver better customer experiences cloud native platforms enhance developer productivity and finally they allow enterprises to build highly elastic and resilient applications you know and they also enable customers to build services digital services using newer application architectures such as microservices so cloud native platforms is fundamentally changing the way customers are building and delivering digital services in our opinion the other topic that is sort of top of mind and i i think it's not hard to imagine this that consumers are concerned about privacy there's different legislation from a variety of governments organizations are trying to figure out how to both adhere to those new legislative pieces but also still get the information they need to market and one of our trends is privacy enhancing computation which is a piece of this can you talk about why and what that is yeah enterprises today face two opposing goals on one hand they want to exchange more information you know between teams or even with partners you know for analytics but on the other hand they're facing increasing global and local compliance regulations so the question is how do you reconcile kind of these two opposing goals and we believe the answer is through privacy enhancing computation because it allows you to securely share pool and analyze personal data without compromising privacy and confidentiality let me give you an example here for example there is deliver fund which is a us-based nonprofit organization with a mission to tackle human trafficking so it platforms use homomorphic encryption which is one of the privacy enhancing computation technique so that its partners can conduct data searches against extremely sensitive data such as human trafficking data with search as well as the results being encrypted there are other techniques here as well such as differential privacy or even hardware level system protection that are part of this expansive trend so we believe that privacy is going to be a very critical need in the enterprise in the future and collaboration and analytics are going to be an equally important need and privacy enhancing computation really allows organizations to reconcile both of those key imperatives together yeah i think it's really interesting and it's easy to see how that trend is gonna change the world as we mentioned trends do appear on the list sometimes a couple years in a row and one of those is hyper automation but why does it remain so important and how has it evolved from maybe the first time it was on the list until now yeah sure let me first maybe define hyper automation in a in a simple sentence hyperautomation is the automation of business and i.t processes we do believe that everything that can be automated will be automated in this decade and vr hyper automation has increased in importance to answer your question because of several reasons i think the first and the foremost reason is the increasing business focus on growth digitalization and more importantly operational efficiency and operational excellence the second reason i would argue is the broader industrialization of i.t that we are seeing where the focus is on automating i.t processes particularly trying to
automate iit processes that are viewed as undifferentiated heavy lifting with an enterprise yesterday in terms of how hyper automation has evolved it's evolved in many ways you know we are seeing increasing power and ubiquity of technology tools such as robotic process automation or rpa low code new code solutions and we're also seeing increasing maturity in even driven architecture that really really led themselves very well to the automation initiatives within the enterprise i want to pause quickly to invite our listeners to learn how to create an action plan to master business composability the 2022 cio agenda analyzes data from more than 2 300 cios and technology executives to figure out how organizations developed high business composability and pushed ahead of their peers while creating an organization designed to deal with today's volatility download the ebook in the show notes so can you give an example of sort of how that thinking on hyper automation has evolved from maybe the first time we saw it on the list like an example of what that would have meant a couple years ago versus what it means now yeah it's a great question i think on one hand there's technology advancements that will allow you to analyze the processes mind the processes suggest improvements to the processes very well so the technology itself has evolved to be a lot more intelligent than it was but when i think about the use cases i clearly see some patterns but the first pattern that i see is a lot of the focus on automation was much more back office oriented right it was not really you know partner focused or customer focused i think that's a change that we are starting to see now where a lot of this is customer facing applications you know a simple example of this could be things like conversational ai or chatbots where you're actually putting a machine or a software in front of your customer and it's a software and and obviously the analytics that's underpinning it that's interacting directly with the customer so one major shift i would argue essentially is the confidence that customers and organizations are having today to use automation more and more for customer facing initiatives as well not just for back office use cases the next one i want to talk about is ai engineering which i've spent a lot of times with these trend and i don't fully understand it yet so if you could explain what ai engineering is let's start there yeah let me start off by saying the obvious which is ai is the most transmitted opportunity in business and i.t today right in this decade and beyond however the challenge for a lot of organizations today is the ai projects are also characterized by very high failure rates so they're trying to kind of reconcile these realities which is on one hand they're super excited by the potential that ai has but at the same time they're also trying to mitigate some of the the failures that they're experiencing in their environments and we believe ai engineering is the answer to it in the sense that ai engineering is a discipline of operationalizing ai models using you know better integrated data pipelines in a more automated model development with very strong governance built into it and also automating the deployment of these models into production environments i know that was a mouthful in terms of what i said let me try to make it a little real with an example right for example let's take georgia pacific you know it's a manufacturer of pulp and paper products and they monitor models for drift that can erode business value so the model drift can happen because the production data often is not exactly the same as your training data so what they essentially do is they have monitoring systems in place that quantify these drifts and they apply rapid changes to the production model using automated processes you know often in real time or close to real time to maintain high degree of model accuracy so ai engineering is the discipline of automating this process so that your models are functioning with high degree of accuracy which means that you're really reaping the business value of what you set out to in the first place and are we seeing that this is making a real impact because it seems like this is making ai engineering a very scalable and approachable project for organizations who might have been intimidated by you know what can be fairly high failure rates so are they really open to trying this kind of trend i would definitely say so i think you're also hitting on another important point here which is ai engineering is of course about you know reducing uh model failures uh and ai project failures within the environment but it's also about accelerating how soon you can get models into production another data point here essentially is we think for most enterprises it takes anywhere from 9 to 12 months to get their models into production by using ai engineering best practices again to your point they can scale these processes so much more effectively that they can get these models into production in a relatively shorter span of time so it's not just about accuracy and reducing failure rates it's also about accelerating time to market which i believe is very very important for enterprises today you just brought up an interesting point that we probably should address before we talk about our remaining two trends that we're going to talk about which is what is the time frame for most of these technologies to be enacted by organizations because i know ai engineering you just gave us kind of a short time shortish time frame what are we looking for when we're talking to organizations about how they should be thinking about timelines yeah that's a great question and i'm glad that you asked that question when we look at the as the strategic technology trends and i've been part of this research for four years now we are not looking for trends that are mainstream today nor are we looking for trends that are going to be mainstream 10 years from now so the sweet spot of trends that we are looking at is typically a two to five year time frame a two to five year time frame by which these technologies will be mainstream or early mainstream for enterprises to consume and trends where we also believe that there is a commercial ecosystem that will build around it that will make it viable for enterprises to you know go ahead and deploy the underpinning technologies that are supporting these trends so to answer your question typically the time frame we are looking at is a two to five year period okay great i just wanted to set the baseline because i think with some of these depending on the maturity of your organization it might be you're taking a first step so i think setting the time frame expectation is super important you bet getting to our remaining two technologies trends that we're going to talk about one sort of i'll call it a very of the moment theme is distributed enterprise so can you talk about what that is and kind of what i'm referring to as far as why it's super applicable to the moment we're all experiencing well if there is a trend of the year award it will certainly go to distributed enterprise right with the rise in you know remote and hybrid working patterns and you know kind of the traditional office-centric environment getting completely disrupted uh distributed enterprise is a reality that a lot of organizations are waking up to with you know staff and customers and partners product all across the globe working in you know kind of these these virtual and remote environments however these new hybrid work realities mean that partners and consumers are also now remote so organization must plan for you know how this is going to impact their workers their partners and customers everywhere the less obvious aspect of this is many enterprises also have to adapt their business models you know to these remote first culture or virtual first culture which we're going to see for a long period of time so the question then is what should they be doing i think the first thing they have to do is to make sure that they have tools and processes and techniques to reduce employee fatigue and burnout and they have to fundamentally re-architect their collaboration tools their security tools their workspaces and processes to really match this new hybrid work environment secondly they need to make sure that they plan to pivot their business models to capture market share from you know customers and consumer changes that are going to happen because the world is going to move to this in a virtual first environment in the future so i try not to pick favorites but clearly i have favorites and this next one is actually i think one of the coolest trends which is generative ai can you explain a little bit about what it is but also more what the future implications of it are yeah you're not alone by the way in saying that this is my favorite trend you could add me to that list and i'm sure there are many listeners that would you know agree with us uh because generative ai is perhaps the most transformative of all trends in this research arguably let me explain what generative ai is and i'll give you some examples you know both in our consumer lives as well as in kind of enterprise setting in terms of how this is going to play out generative ai is a form of artificial intelligence that learns digital representation of artifacts from sample data and uses it to generate its own new and realistic artifacts that kind of retain a likeness to the the original data that it was exposed to let me try to make it a little real in our consumer lives right you know we've all experienced generative ai in some shape or form in our in our lives every day for example this could be you know artificial intelligence completing our sentences you know as we type our ai generating uh paintings you know for example there was a ai generated painting that was sold for half a million dollars by christie's a couple of years ago or even more recently in fact this month where beethoven's 10th symphony which was unfinished because he died as he was composing it was actually completed by a ai model so this is kind of broadly what we mean by generative ar which is the ability of ai to create things that kind of resemble real-life artifacts but the broader question here is what does this mean from an enterprise perspective or from a cio perspective i'm going to give you some use cases or even case studies to illustrate it for example the uk financial authority contact authority they're using generative ai to create synthetic payment data from 5 million records of real payment data the reason for using the synthetic data set here is to create new fraud model without actually revealing individual data because the synthetic data sets while they resemble original data but they don't have personally identifiable information in them so the customer privacy is a very important driver here we've also seen research a lot of research coming out of it for example researchers have demonstrated the potential for generative ai to speed up identification of new organic materials you know things like for example protein or even like dies for example like synthetic dyes as an example finally much more closer to home for it professionals github microsoft and open ei they have collaborated to create github co-pilot which is a tool that can generate and recommend code to developers as they write software which in the long run can have a significant impact in terms of how we do software testing and potentially software development it's it's a little immature today but you know the potential for that in the future is uh is really transformative i love talking about the examples for that one i just think that's so fascinating like you can imagine the potential for it it's really cool one thing we always try and do obviously here at gartner is give our listeners actionable insights so how should executives go about using this research how should they think about it should it be part of their strategic planning like what are we advising yeah it's a great question i would argue that they should really start off by looking at these trends and evaluating which of these trends are relevant to their organization and more importantly to create an action plan around it right for example what's the business value of these trends maybe they can create a use case in a matrix in terms of identifying you know use cases that they want to prioritize and focus on or maybe agree on timelines if they want to go ahead and pilot or implement any of these trends obviously augmenting and enhancing skills around these are going to be vital to make sure that they're successful or perhaps planning for budget allocation so the first and the foremost step in my opinion is to create an action plan that includes everything from business value identification use case matrix agreeing on timelines enhancing teams and skills and more importantly allocating a budget around it gartner is committing to publishing a lot of research around use cases and best practices and emerging vendors in this area so you should also keep track of some of the upcoming research from gartner in 2022 around each of these trends thanks so much for joining us always a pleasure to have you and to talk about this research do you have any final thoughts i think i'll go back to where we started which is the past 18 months have seen faster change than ever before so i believe business and technology leaders today have a choice they can either continue their digital innovation at the same pace or they can go back to the status quo before the pandemic happened i think the choice is obvious in my opinion we believe that these trends should certainly be on the router of every leader to help them accelerate their digital business and i wish them well in this journey thanks so much thank you thanks for having me gartner thinkcast is a production of gartner this podcast may not be reproduced or distributed in any form without gardner's permission it consists of the opinions of gardner's research organization which should not be construed as statements of fact content provided by other speakers is expressly the views of the speaker and or their organization while the information contained in this podcast has been obtained from sources believed to be reliable gartner disclaims all warranties as to the accuracy completedness or adequacy of such information although gartner research may address legal and financial issues gartner does not provide legal or investment advice and its research should not be construed or used as such you can learn more at www.gardner.com [Music] you
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