Gartner's 2022 Top Strategic Technology Trends. Old Problems. Old Trends. New Names. – Forbes

Business Trends Graphs and charts 2022 3d image
Every year the Gartner Group releases their list of top strategic technology trends.  The three themes for 2022 are “engineering trust,” “sculpting change” and “accelerating growth.”  At the outset one must be impressed with how Gartner neologizes so creatively year after year. But are the trends really new or just renamed old ones? And are they all just aspirations?
The Gartner 2022 Trends
  Here’s the new list:

Let’s look at each of them, noting their age, overlap and real nature. 
Data has always been essential to operational and strategic effectiveness.  “Making data available everywhere” has been a priority for decades.  Accessing the right data at the right time across multiple platforms and applications has always been a problem.  “Analytics” is how we decided to brand both the problems and solutions.  Old problems; new names.  While “data fabric” is cute, it’s just the latest way we describe data nirvana, but in a proprietary technology world, it remains a pipedream.  
Should we continue to clean, integrate and present structured and unstructured data?  Of course, through whatever cost-effective means we can.  But we should also recognize there are limits to what we can cost-effectively achieve, especially when we pursue best of breed data and applications strategies?  Gartner also argues that data fabrics “can reduce data management efforts by up to 70%.”  How in the world is this a knowable number?
Everyone knows that the number and nature of cyberthreats is increasing.  We also know that there are no perfect solutions – and never will be.  We know that most companies do not fully understand the threats and therefore underspend in cybersecurity or spend the wrong way.  Statements like these are what you hear from vendors:
“Cybersecurity mesh is a flexible, composable architecture that integrates widely distributed and disparate security services.  
“Cybersecurity mesh enables best-of-breed, stand-alone security solutions to work together to improve overall security while moving control points closer to the assets they’re designed to protect.  
“It can quickly and reliably verify identity, context and policy adherence across cloud and noncloud environments”  
These are normative and prescriptive statements, not actionable ones.  Of course we want “composable architecture that integrates widely distributed and disparate security services.”  Who doesn’t?  There’s always an ocean between what we want and how to get it.  This a not trend, just an old aspiration.
How long have we been talking about “privacy”?  Why is privacy still just a great bar or party conversation?  Because too many business models only work when there’s no privacy.  Worse, how many Americans really worry about digital privacy?  (I understand that Europeans worry more about privacy than Americans and that privacy itself is an issue/non-issue depending upon where countries sit across the democratic/authoritarian spectrum.)  Americans say they’re concerned about privacy, but Despite Privacy Concerns, Consumer-Level Inaction Reins Supreme.”  While there’s lip-service to privacy, retail and other vendors continue to bask in their access to personal data everywhere, all the time. They need it. They pay for it. They sell it. Gartner’s “privacy-enhancing computation” is a lovely name for an old problem that no one cares enough about to change their online behavior.  (Oh, and where’s all this legislation everyone keeps talking about?)  
This important trend is well underway.  Cloud-native has real traction among companies still struggling to repurpose their applications portfolios as well as the use of cloud-native platforms to build applications built upon containers, microservices, serverless functions and immutable infrastructure, deployed via declarative code are common elements of this architectural style … these techniques enable loosely coupled systems that are resilient, manageable, and observable.”   Yes.  Is this the future?  No question.  So what’s new here?
“Composable” applications (and the larger composable enterprise architecture) is as much about developing an API- and event driven- culture as anything else.  It’s also about legacy applications preservation, as more and more companies try to migrate away from old architectures all the way to cloud-native status without too much upheaval – which has been a goal for years.  More recently, we added low code/no code for the development of microservices-based applications while reusing APIs as much as possible, ideally within a “modern” governance model.  
New-est name for an old goal.
How is this not a homage to analytics, circa 2010?  In 2021 we think about automaton.  This is an old trend with a few slightly new twists.
This one speaks to automation trends.  If one tracks the 4IR and the Future of Work, you already know all about automation.  Hyperautomation, according to Gartner, is “a disciplined, business-driven approach to rapidly identify, vet and automate as many business and IT processes as possible.”  This trend has been in play forever.  Under the umbrella of robotic process automation (RPA) where processes are modeled, mined, eliminated, modified, or automated, the goal has been a trend — and an ongoing aspiration — for some time.
AI engineering is about best practices for repeatable design, development and deployment.  OK.  But is this really a new trend?  Or just one that’s been around for a while now, at least for five years?
This is where digital transformation meets edge computing.
This has been aspirational forever.  Just Google it.  Who’s doing it?  There are pockets — such as returning unboxed items from Amazon to Whole Foods — but it’s still illusive.  Who wouldn’t want seamless, easy and fun TX? 
Wow.  Sure.  Of course:  Autonomic systems are self-managed physical or software systems that learn from their environments and dynamically modify their own algorithms in real time to optimize their behavior in complex ecosystems.”  Why not? But a trend?
Absolutely a long-term strategic objective.  If only generative AI was a discernible trend.
Let’s pretend that Gartner invited us to describe the trends we see.  What would they look like?  Not what should they look like, but what they actually are.  First, let’s reorganize the Gartner trends into baskets and then assess how well they’re doing out there.  
Three baskets jump out:



So what’s happening out there that might be described as trends?  Survey data suggests that there’s still some skepticism about how quickly executives believe they have to ramp up their AI and machine learning (ML) investments.  That said, there’s a steady increase in AI/ML pilots.  So that’s a trend that will accelerate over time so long as impact is quantifiable.  We do know that trends in RPA are positive.  We know that there are new “Centers of Excellence” being created and we also know that the adoption of AI/ML is vertically driven:  some industries are piloting applications more than others.  There’s also a rank-ordering of AI/ML applications, like chatbots (“conversational AI”) and selection applications (such as selecting the best/worse candidates for loans, “admission,” and any binary decision).  We know that adoption rates are dependent upon strategy, which has yet to fully embrace intelligent systems as the future that will define profitable growth.  Deloitte reports that the distribution across “path seekers,” “transformers,” “starters” and “underachievers” in AI is challenging, suggesting that more than two thirds of companies are not “high achievers.” Deloitte also reports that companies are bullish on AI/ML, that eventually they expect AI/ML to really impact their business.  So the real trend is cautious optimism.
Data trends?  Analytics is now mainstream – at least by name and intent.  But clean, integrated, accessible data remains elusive.  Why?  The old standbys, such as competing data formats across applications, “lost” data, bad data, duplicate data and no enterprise data architecture, are among the problems that have haunted us for decades.  So seamless, integrated and accessible data is an ongoing aspiration.  But trend?  Yes, if by trend we mean investment trends.  Make no mistake this is an ongoing slog.  
Applications architecture is finally getting some traction.  Companies are moving toward microservices-based applications as they remove their legacy applications from life support.  “Applications rationalization” is a real thing.  It saves money and moves the ball toward the microservices goal line.  Container technology has evolved nicely and, perhaps most importantly, the major cloud vendors offer a variety of solutions here.  Perhaps this is the real trend.  Edge computing is part of this, and total experience applications will always be what everyone strives to build.
The main problem with Gartner’s (and other) trends lists is they cannot help but be repetitive and overlapping, especially with prior years.  While things move fast in technology land, major trends – like applications architecture and AI – will be “trends” for years.  “Updates” are interesting but not that useful.  Sure, more people are thinking about microservices and containers than they were last year, and more companies are investing in AI pilots.  It’s hard to describe truly new trends, so technology prognosticators rename old ones with catchy names.  But some of the names are just too obvious.  Maybe next year the trends will be new or the (re) names more creative. Or maybe they’ll just be called aspirations.