54 Predictions About The State Of Data In 2021
From 2010 to 2020, the amount of data created, captured, copied, and consumed in the world increased from 1.2 trillion gigabytes to 59 trillion gigabytes, an almost 5,000% growth. What will data do in the coming decade?
Happy New Year!
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26 the five-year compound annual growth rate (CAGR) through 2024 of the data created, captured, copied, and consumed in the world; the amount of data created over the next three years will be more than the data created over the past 30 years [IDC]
22 the percentage of all full work days that will be supplied from home after the Covid-19 pandemic ends, compared with just 5% before [Becker Friedman Institute]
66 the percentage of employed Americans that agree that they would prefer a mix of in-office and remote work after the pandemic ends [Harris]
90 the percentage of HR leaders that plan to allow employees to work remotely at least part of the time, even after the COVID-19 vaccine is widely adopted [Gartner]
300 the percent increase in remote work compared to pre-Covid-19 levels [Forrester]
41 the percentage of Americans that say they will date more online once the pandemic is over [Harris]
400 million the number of video visits to doctors worldwide in 2021, 5% of all doctor visits, up from 1% in 2019 [Deloitte]
7 the number of leading wrist-worn wearables companies that will have released by the end of 2021 algorithms capable of early detection of potential signs of infectious diseases including Covid-19 and the flu [IDC]
100 the percentage growth of sales for enterprise and educational use of wearable headsets for virtual, augmented and mixed reality in 2021 over 2019 levels [Deloitte]
50 the percentage increase of investments in AI and advanced analytics by life science and healthcare provider companies by 2022 to avoid future supply chain disruptions [IDC]
65 the percentage of medical imaging workflows that by 2026 will use AI to detect underlying disease and guide clinical intervention, while 50% will use teleradiology to share studies and improve access to radiologists [IDC]
25 the percentage of CIOs and CTOs that believe AI and machine learning will have the greatest impact on global Covid-19 recovery [IEEE]
5 billion the number of internet users in 2023, up from 3.9 billion in 2018 [Cisco]
29.3 billion the number of web-connected devices in 2023, up from 18.4 billion in 2018 [Cisco]
36.8 billion the number of Industrial IoT connections in 2025, up from 17.7 billion in 2020 [Juniper]
110 mbps the broadband speed in 2023, up from 45.9 mbps in 2018 [Cisco]
61 the percentage of telecom providers planning AI projects for edge computing by 2023 [Cisco]
$6 trillion the cost of cyber crime worldwide in 2021, up from $3 trillion in 2015 [Cybersecurityventures.com]
“Ransomware in healthcare will increase significantly. This is driven by a combination of high-profile press on ransomware attacks, which further motivates threat actors, and a very diverse IoT infrastructure, which is fully interconnected to IT and often missing the basic security controls required to withstand a ransomware attack,” Galina Antova, Claroty
50 the percentage of CIOs that will accelerate robotization, automation, and augmentation by 2024 [IDC]
85 million the number of workers worldwide displaced by automation over the next five years [World Economic Forum]
97 million the number of jobs worldwide created by the robot revolution over the next five years [World Economic Forum]
65 the percentage of the world’s GDP set to be digitalized by 2022 [IDC]
“In many ways, digitization is simply the next chapter of a process under way for a century: the dematerialization of the economy,” Greg Ip, The Wall Street Journal
$6.8 trillion the total amount allocated to direct digital transformation investments worldwide between 2020 and 2023 [IDC]
25 the percentage of workers supported by new forms of automation either directly or indirectly by 2022 (e.g., handling employee benefits questions and supporting document, customer service, and line-of-business tasks that are often invisible to the home worker) [Forrester]
75 the percentage of grocery ecommerce orders that will be picked up curbside or in store by 2023, driving a 35% increase in investment in onsite or nearby micro-fulfillment centers [IDC]
222 million the number of video streaming users in the U.S. in 2024, up from 170.5 million in 2018 [eMarketer]
45 the percentage of repetitive work tasks that will be automated and/or augmented by using “digital co-workers,” powered by AI, robotics, and RPA [IDC]
1 trillion the number of parameters in a new 2021 NLP model, “most likely… from OpenAI and be named GPT-4” [Forbes]
1.4 billion the number of smart home devices that will be shipped worldwide in 2024, up from 854 million in 2020 [IDC]
637.1 million the number of wearable devices that will be shipped worldwide in 2024, up from 396 million in 2020 [IDC]
75 the percentage of enterprises that will use in 2021 new, external data sources to enhance their cross-functional decision-making capabilities in ways that increase value compared with using internal data alone [IDC]
35 the percentage of large organizations that by 2022 will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020 [Gartner]
50 the percentage of G2000 companies will have active development teams seeking to externally monetize their data to enhance traditional offerings, drive innovation, or deliver Data and Analytics as a Service [IDC]
50 the percentage of knowledge workers that by 2024 will regularly interact with their own AI-enhanced robot assistant, which will help identify and prioritize tasks, collect information, and automate repetitive work [IDC]
20 the percentage of all AI solutions that will be closer to artificial general intelligence (AGI) by 2026, leveraging neuro-symbolic techniques that combine deep learning with symbolic methods to create robust humanlike decision making [IDC]
60 the percentage of enterprises that will invest heavily in digitalizing employee experience in 2021, transforming the relationship between employers and employees [IDC]
$110 billion global spending on artificial intelligence (AI) in 2024, up from $50.1 billion in 2020 [IDC]
“Companies will adopt AI — not just because they can, but because they must,” Ritu Jyoti, IDC
35 the percentage increase in U.S. labor productivity by 2035 due to the influence of AI [Accenture/Frontier Economics]
60 the percentage of B2B sales organizations that will transition from experience- and intuition-based selling to data-driven selling by 2025 [Gartner]
40 the percent increase of marketing message volume (more emails, texts and push notifications to consumers) in 2021 [Forrester]
60 the percentage of enterprises that will invest heavily in digitalizing employee experience in 2021 [IDC]
30 the percentage of CIOs that will fail in protecting trust —the foundation of customer confidence — by 2021 [IDC]
500 the number of G2000 companies that by 2023, driven by the goal to embed intelligence in products and services, will acquire at least one AI software start-up to ensure ownership of differentiated skills and IP [IDC]
60 the percentage of software buyers that will expect AI functionality from existing vendors, rather than custom-creating their own applications [Forrester]
“At the end of the day, AI will be everywhere in software products, just as analytics, workflow, and data are part of those same software products,” Andrew Bartels and Mike Gualtieri, Forrester
5 (or less) the percentage of data-sharing programs that will correctly identify trusted data and locate trusted data sources [Gartner]
30 the percentage of digital businesses that by 2024 will mandate DNA storage trials, addressing the exponential growth of data poised to overwhelm existing storage technology [Gartner]
“One single gram of DNA could store all the knowledge generated by humans in one year,” Daryl Plummer, Gartner
40 the percentage of physical-experiences businesses that by 2025 will improve financial results and outperform competitors by extending into paid virtual [Gartner]
75 the percentage of conversations at work that by 2025 will be recorded and analyzed, enabling the discovery of added organizational value or risk [Gartner]
100 the percentage of additional business value from analytics investments that will be realized by 2021 by organizations that offer users access to a curated catalog of internally and externally prepared data [Gartner]
82 the percentage of CFOs that identified advanced data analytics technologies and tools as top priority for 2021 but 78% expected it to be difficult to successfully achieve their goals in this area next year [Gartner]
“The gap between good analytics that harbor potential value and actually realizing that value is one that data literacy is intended to bridge. The data literacy trend is a result of the realization that simply generating great analytics isn’t going to lead to results unless the business is ready, willing, and able to act on those analytics,” International Institute for Analytics
$1 billion the amount of transaction payments made through a vehicle by 2023, up from less than $100 million in 2020 [Gartner]
20 the percentage of all new cars that will be sold entirely online by 2025, up from less than 1% in 2020 [Gartner]
60 the percentage of businesses that pivoted to virtual events that will incorporate real-time/real-space experiential elements into marketing experiences by 2023 [Gartner]
13.5 billion the number of smart home devices in active use in 2025, up from 7.4 billion in 2020 [Juniper]
“Data teams today are on a collision course with conflicting priorities. For infrastructure teams, building for scale, security, and cost are of the utmost importance while engineering teams prioritize flexibility, development speed, and maintainability. Meanwhile, data scientists and analysts are focused on the availability and discoverability of data, and the connectivity of tools. As enterprises scale their efforts and their teams to build new data products, the interconnectedness and resulting complexity can be paralyzing for these groups. If organizations continue to cater to one group’s needs amidst these conflicting priorities, we can anticipate a rise of ‘data mutinies’ in 2021 – in which internal users create their own engineering organizations with a mandate to move quickly and free themselves from these conflicts,” Sean Knapp, founder and CEO, Ascend.io
See also The State of Data, December 2020
54 Predictions About The State Of Data In 2021
From 2010 to 2020, the amount of data created, captured, copied, and consumed in the world increased from 1.2 trillion gigabytes to 59 trillion gigabytes, an almost 5,000% growth. What will data do in the coming decade?
Happy New Year!
getty
26 the five-year compound annual growth rate (CAGR) through 2024 of the data created, captured, copied, and consumed in the world; the amount of data created over the next three years will be more than the data created over the past 30 years [IDC]
22 the percentage of all full work days that will be supplied from home after the Covid-19 pandemic ends, compared with just 5% before [Becker Friedman Institute]
66 the percentage of employed Americans that agree that they would prefer a mix of in-office and remote work after the pandemic ends [Harris]
90 the percentage of HR leaders that plan to allow employees to work remotely at least part of the time, even after the COVID-19 vaccine is widely adopted [Gartner]
300 the percent increase in remote work compared to pre-Covid-19 levels [Forrester]
41 the percentage of Americans that say they will date more online once the pandemic is over [Harris]
400 million the number of video visits to doctors worldwide in 2021, 5% of all doctor visits, up from 1% in 2019 [Deloitte]
7 the number of leading wrist-worn wearables companies that will have released by the end of 2021 algorithms capable of early detection of potential signs of infectious diseases including Covid-19 and the flu [IDC]
100 the percentage growth of sales for enterprise and educational use of wearable headsets for virtual, augmented and mixed reality in 2021 over 2019 levels [Deloitte]
50 the percentage increase of investments in AI and advanced analytics by life science and healthcare provider companies by 2022 to avoid future supply chain disruptions [IDC]
65 the percentage of medical imaging workflows that by 2026 will use AI to detect underlying disease and guide clinical intervention, while 50% will use teleradiology to share studies and improve access to radiologists [IDC]
25 the percentage of CIOs and CTOs that believe AI and machine learning will have the greatest impact on global Covid-19 recovery [IEEE]
5 billion the number of internet users in 2023, up from 3.9 billion in 2018 [Cisco]
29.3 billion the number of web-connected devices in 2023, up from 18.4 billion in 2018 [Cisco]
36.8 billion the number of Industrial IoT connections in 2025, up from 17.7 billion in 2020 [Juniper]
110 mbps the broadband speed in 2023, up from 45.9 mbps in 2018 [Cisco]
61 the percentage of telecom providers planning AI projects for edge computing by 2023 [Cisco]
$6 trillion the cost of cyber crime worldwide in 2021, up from $3 trillion in 2015 [Cybersecurityventures.com]
“Ransomware in healthcare will increase significantly. This is driven by a combination of high-profile press on ransomware attacks, which further motivates threat actors, and a very diverse IoT infrastructure, which is fully interconnected to IT and often missing the basic security controls required to withstand a ransomware attack,” Galina Antova, Claroty
50 the percentage of CIOs that will accelerate robotization, automation, and augmentation by 2024 [IDC]
85 million the number of workers worldwide displaced by automation over the next five years [World Economic Forum]
97 million the number of jobs worldwide created by the robot revolution over the next five years [World Economic Forum]
65 the percentage of the world’s GDP set to be digitalized by 2022 [IDC]
“In many ways, digitization is simply the next chapter of a process under way for a century: the dematerialization of the economy,” Greg Ip, The Wall Street Journal
$6.8 trillion the total amount allocated to direct digital transformation investments worldwide between 2020 and 2023 [IDC]
25 the percentage of workers supported by new forms of automation either directly or indirectly by 2022 (e.g., handling employee benefits questions and supporting document, customer service, and line-of-business tasks that are often invisible to the home worker) [Forrester]
75 the percentage of grocery ecommerce orders that will be picked up curbside or in store by 2023, driving a 35% increase in investment in onsite or nearby micro-fulfillment centers [IDC]
222 million the number of video streaming users in the U.S. in 2024, up from 170.5 million in 2018 [eMarketer]
45 the percentage of repetitive work tasks that will be automated and/or augmented by using “digital co-workers,” powered by AI, robotics, and RPA [IDC]
1 trillion the number of parameters in a new 2021 NLP model, “most likely… from OpenAI and be named GPT-4” [Forbes]
1.4 billion the number of smart home devices that will be shipped worldwide in 2024, up from 854 million in 2020 [IDC]
637.1 million the number of wearable devices that will be shipped worldwide in 2024, up from 396 million in 2020 [IDC]
75 the percentage of enterprises that will use in 2021 new, external data sources to enhance their cross-functional decision-making capabilities in ways that increase value compared with using internal data alone [IDC]
35 the percentage of large organizations that by 2022 will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020 [Gartner]
50 the percentage of G2000 companies will have active development teams seeking to externally monetize their data to enhance traditional offerings, drive innovation, or deliver Data and Analytics as a Service [IDC]
50 the percentage of knowledge workers that by 2024 will regularly interact with their own AI-enhanced robot assistant, which will help identify and prioritize tasks, collect information, and automate repetitive work [IDC]
20 the percentage of all AI solutions that will be closer to artificial general intelligence (AGI) by 2026, leveraging neuro-symbolic techniques that combine deep learning with symbolic methods to create robust humanlike decision making [IDC]
60 the percentage of enterprises that will invest heavily in digitalizing employee experience in 2021, transforming the relationship between employers and employees [IDC]
$110 billion global spending on artificial intelligence (AI) in 2024, up from $50.1 billion in 2020 [IDC]
“Companies will adopt AI — not just because they can, but because they must,” Ritu Jyoti, IDC
35 the percentage increase in U.S. labor productivity by 2035 due to the influence of AI [Accenture/Frontier Economics]
60 the percentage of B2B sales organizations that will transition from experience- and intuition-based selling to data-driven selling by 2025 [Gartner]
40 the percent increase of marketing message volume (more emails, texts and push notifications to consumers) in 2021 [Forrester]
60 the percentage of enterprises that will invest heavily in digitalizing employee experience in 2021 [IDC]
30 the percentage of CIOs that will fail in protecting trust —the foundation of customer confidence — by 2021 [IDC]
500 the number of G2000 companies that by 2023, driven by the goal to embed intelligence in products and services, will acquire at least one AI software start-up to ensure ownership of differentiated skills and IP [IDC]
60 the percentage of software buyers that will expect AI functionality from existing vendors, rather than custom-creating their own applications [Forrester]
“At the end of the day, AI will be everywhere in software products, just as analytics, workflow, and data are part of those same software products,” Andrew Bartels and Mike Gualtieri, Forrester
5 (or less) the percentage of data-sharing programs that will correctly identify trusted data and locate trusted data sources [Gartner]
30 the percentage of digital businesses that by 2024 will mandate DNA storage trials, addressing the exponential growth of data poised to overwhelm existing storage technology [Gartner]
“One single gram of DNA could store all the knowledge generated by humans in one year,” Daryl Plummer, Gartner
40 the percentage of physical-experiences businesses that by 2025 will improve financial results and outperform competitors by extending into paid virtual [Gartner]
75 the percentage of conversations at work that by 2025 will be recorded and analyzed, enabling the discovery of added organizational value or risk [Gartner]
100 the percentage of additional business value from analytics investments that will be realized by 2021 by organizations that offer users access to a curated catalog of internally and externally prepared data [Gartner]
82 the percentage of CFOs that identified advanced data analytics technologies and tools as top priority for 2021 but 78% expected it to be difficult to successfully achieve their goals in this area next year [Gartner]
“The gap between good analytics that harbor potential value and actually realizing that value is one that data literacy is intended to bridge. The data literacy trend is a result of the realization that simply generating great analytics isn’t going to lead to results unless the business is ready, willing, and able to act on those analytics,” International Institute for Analytics
$1 billion the amount of transaction payments made through a vehicle by 2023, up from less than $100 million in 2020 [Gartner]
20 the percentage of all new cars that will be sold entirely online by 2025, up from less than 1% in 2020 [Gartner]
60 the percentage of businesses that pivoted to virtual events that will incorporate real-time/real-space experiential elements into marketing experiences by 2023 [Gartner]
13.5 billion the number of smart home devices in active use in 2025, up from 7.4 billion in 2020 [Juniper]
“Data teams today are on a collision course with conflicting priorities. For infrastructure teams, building for scale, security, and cost are of the utmost importance while engineering teams prioritize flexibility, development speed, and maintainability. Meanwhile, data scientists and analysts are focused on the availability and discoverability of data, and the connectivity of tools. As enterprises scale their efforts and their teams to build new data products, the interconnectedness and resulting complexity can be paralyzing for these groups. If organizations continue to cater to one group’s needs amidst these conflicting priorities, we can anticipate a rise of ‘data mutinies’ in 2021 – in which internal users create their own engineering organizations with a mandate to move quickly and free themselves from these conflicts,” Sean Knapp, founder and CEO, Ascend.io
See also The State of Data, December 2020
Generative AI is getting kicked off its pedestal — it will be painful but it’s not a bad thing
It’s been two years since the phrase “generative AI” began cluttering my email inbox. It wasn’t a new term (it appeared in one of Gartner’s famous hype cycle reports back in 2020) but as the summer of 2022 came to a close, the inbound flow of messages and pitches I received were a clear sign that buzz was quickly building for AI-powered tools that could generate content–such as text, images and computer code. And when OpenAI launched ChatGPT in November 2022, generative AI catapulted into the mainstream culture and has been flying high ever since.
Something in that cheery narrative has changed during the past few weeks, however.
Goldman Sachs called generative AI “overhyped” and “wildly expensive”; VC firm Sequoia Capital said “the AI bubble is reaching a tipping point”; a spate of media headlines such as “The AI Hype Machine is Running On Empty” are zealously pouring cold water on the whole affair.
Why? Well, generative AI chatbots struggle to answer basic questions or hallucinate incorrect information. The most sophisticated generative AI models are constantly hungry for data and computing power. Generative AI startups with little to no revenue have to constantly scrounge for massive funding rounds to stay afloat. Fortune 500 companies can’t put generative AI use cases into production because of concerns about accuracy, liability and security.
And with the S&P 500 suffering its biggest selloff in two years on Monday, there’s a growing sense that the Generative AI bubble has begun to deflate.
Gartner’s hype cycle now says generative AI has passed the “Peak of Inflated Expectations” and is headed straight for a looming “Trough of Disillusionment.” If that’s true, what comes next will be painful and disruptive. Investment dollars could dry up. Startups could fail. There could be layoffs.
For many of the startup employees, founders, and investors who put in the work and took the risks necessary for the generative AI sector to take off, the sting of the market correction will be unjust and brutal. But knocking generative AI off of its lofty pedestal is also necessary for the long-term sustainability of the AI landscape, Kjell Carlsson, a former analyst at Forrester Research who is now head of AI strategy at enterprise data platform Domino Data Lab, told me.
“I’m fairly confident that folks will recognize that Gen AI isn’t all the AI,” he said, referring to the wide variety of other artificial intelligence technologies, including predictive AI and machine learning, that were already delivering real return-on-investment before generative AI came on the scene. “Gen AI is one set of technologies that are part of this broad toolkit of different technologies that take work,” he explained. “There’s no magic button, it’s all about leveraging technologies for the right use cases.”
Let’s be clear: generative AI is not going away. These models and tools, from ChatGPT and Microsoft Copilot to Google’s Gemini, Anthropic’s Claude and Meta’s Llama, have already become part of our lives– for productivity, for efficiency, or just for fun. Just as we’ve become accustomed to getting any information we need in seconds by doing a Google search, so too will the ability to obtain easy-to-read summaries of work meetings, to compose memos to colleagues, and to create images and presentations by speaking just a few words.
But let’s also get real: The massive amount of generative AI investment, estimated to be at the tune of $1 trillion, has yet to pay off. Much of that may not be as ridiculous as, say, the dot-com bubble of UrbanFetch and Pets.com (I well remember getting ice cream deliveries and puppet swag), but it’s difficult to argue against the notion that generative AI is getting the reality check it deserves.
“The irony of this is that I think I was the first of the industry analysts to jump on the Gen AI bandwagon,” said Carlsson. “While it’s been a success by anyone’s measure, the expectations around how quickly that would impact the bottom line of major organizations weren’t based in reality.”
That’s where the so-called Trough of Disillusionment becomes an important stage for any tech development, Gartner’s global chief of research Chris Howard said in a recent video. The premise is simple: After an initial burst of excitement and enthusiasm by early adopters, new technology makes its way into the hands of mainstream users who find it doesn’t live up their overinflated expectations. A retrenchment follows, during which the technology is refined and expectations are reset.
“It’s not this dark, dangerous place,” Howard explained in the video. “It’s where we figure out how to make something work–or not.”
For generative AI, the trough will be a phase marked by small incremental progress in applications that deliver real benefits to businesses and to users, and less by proclamations by OpenAI CEO Sam Altman about creating “the most powerful technology humanity has yet invented” with artificial general intelligence (AGI) — though it might make for less sexy headlines.
Even Dan Ives, a Wall Street tech analyst at Wedbush who remains bullish on AI stocks, said this is a key period for tech companies to walk the walk, not just talk the talk, when it comes to generative AI. They need to “show the use cases and monetization to justify the AI Revolution,” he told me in a text.
Ives said that he believes Microsoft, AMD, Nvidia, Palantir and Oracle have shown they can deliver real value. Still, with so many generative AI startups riding on multi-billion-dollar valuations, the sector as a whole still has a lot to prove.
There are no guarantees, but there is a long history of AI technologies that have become mature and gone on to contribute to other, newer AI disciplines, like computer vision– which has become a key part of today’s multimodal generative AI (AI that can generate not just text but images and video, for example).
So perhaps generative AI, pushed along by other, newer technologies like agentic AI (AI systems designed to act like autonomous agents to pursue complex goals and workflows) can still reach its full potential.
Now, perhaps, it’s time for the real down-and-dirty work in generative AI to begin. “I think this will be a false AI Winter,” said Steve Jones, an executive VP at tech consultancy Capgemini, in a LinkedIn post,. ”One where hopefully the hype dies, and we can concentrate on getting work done.”
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Gartner: Software And IT Services Lead $5.1 Trillion Tech Market In 2024
Worldwide spending on IT—from software and services to devices and data center systems—will hit $4.7 trillion in 2023 and then reach $5.1 trillion by 2024, Gartner reports. Here’s the top five tech markets.
Research firm Gartner is expecting worldwide IT spending to reach over $5.1 trillion in 2024 as businesses buy automation and efficiency technologies to drive growth at scale with fewer employees.
“Digital business transformations are beginning to morph,” said John-David Lovelock, distinguished vice president analyst at Gartner. “IT projects are shifting from a focus on external-facing deliverables such as revenue and customer experience, to more inward-facing efforts focused on optimization.”
Worldwide spending on IT—such as software and communications services—will increase by 4.3 percent this year, reaching $4.7 trillion in 2023 compared to $4.5 trillion in 2022, according to new data from Gartner.
Global IT spending is projected to increase by a whopping 8.8 percent in 2024, hitting approximately $5.13 trillion next year.
[Related: AWS, Microsoft, Google Battle For $169B Cloud Services Market: IDC]
Top 5 Global IT Markets
The top five largest global IT markets that will generate the most revenue are: IT services, software, communication services, devices and data center systems. Gartner says while some of these markets are expected to shrink in 2023, all five will rebound in 2024 in terms of sales.
For example, the software market will see double-digit growth in 2023 with global spending levels reaching $911 billion, up 13.5 percent compared to 2022. Vendor price increases will also continue to bolster software spending through this year, Gartner reports. Comparatively, spending on devices will drop nearly 9 percent in 2023 compared to last year due to the impact of consumer inflation.
Gartner said its IT spending forecast methodology relies heavily on rigorous analysis of the sales by over a thousand vendors across the entire range of IT products and services.
CRN breaks down the exact IT spending figures Gartner is projecting on a worldwide basis in 2023 and 2024 for devices, IT services, software, data center systems and communication services.
No. 5: Data Center Systems
2022: $221 Billion
2023: $218 Billion
2024: $235 Billion
Data center systems annual sales on a global basis spiked nearly 17 percent in 2022 to $221 billion as companies continued to spend millions on infrastructure and hardware to support their new hybrid workforce and digital transformation projects.
That spending level is cooling down this year, according to Gartner, with data center systems sales falling 1.5 percent to $218 billion in 2023.
However, data center hardware spending is expected to bounce back in 2024 by growing 8.1 percent to $235 billion.
No. 4: Devices
2022: $766 Billion
2023: $700 Billion
2024: $748 Billion
While the overall outlook for enterprise IT spending is positive in 2023, spending on devices will decline 8.6 percent to $700 billion in 2023 due to the ongoing impact of inflation on consumer purchasing power.
“The devices segment is experiencing one of its worst growth years on record,” said Gartner’s Lovelock. “Even as inflation eases slightly in some regions, macroeconomic factors are still negatively impacting discretionary spending and lengthening device refresh cycles.”
Worldwide spending on devices also declined 6.3 percent in 2022 to $766 billion compared to 2021.
However, the devices market will begin to somewhat bounce back in 2024 with spending expected to increase by 3.8 percent annually to $748 billion.
“Devices spending is not expected to recover to 2021 levels until at least 2026,” said Lovelock.
No. 3: Software
2022: $803 Billion
2023: $912 Billion
2024: $1.04 Trillion
The fastest-growing market in IT is software with businesses expected to spend $912 billion in 2023, up 13.5 percent compared to 2022.
In 2024, spending on software will increase again by a whopping 14 percent on a global basis, reaching $1.04 trillion next year.
The software segment is witnessing double-digit growth in 2023 as organizations increase utilization and reallocate spending to core applications and platforms that support efficiency gains—such as enterprise resource planning (ERP) and customer relationship management (CRM) applications. Vendor price increases will also continue to bolster software spending through this year, Gartner said.
No. 2: Communications Services
2022: $1.42 Trillion
2023: $1.46 Trillion
2024: $1.52 Trillion
The largest IT market in 2023 revolves around communications services which is set to reach $1.46 trillion, representing a 2.7 increase in spending compared to last year.
The communications services market is expected to growth again by 3.8 percent in 2024 with spending levels reaching a record $1.52 trillion, Gartner said.
The communications services market is led by both channel partners—such as Accenture, DXC Technology, Wipro and Tata Consultancy Services (TCS)—as well as communications services providers. This market has typically been the largest IT spending market in the world for years, but Gartner expects it will be surpassed by IT services by 2024.
No. 1: IT Services
2022: $1.31 Trillion
2023: $1.42 Trillion
2024: $1.58 Trillion
IT services will become the largest market next year thanks to a nearly 12 percent growth increase in spending, Gartner estimates, as worldwide spending on IT services will reach $1.58 trillion in 2024.
The IT services market has continuously grown in the high single-digits year after year as businesses seek more services from channel partners and vendors. In 2022, spending on IT services increased 7.5 percent to $1.31 trillion. This year, global spending on IT services is expected to grow annually by 8.8 percent to $1.42 trillion.
Gartner said generative artificial intelligence (AI) is expected to boost the IT services market as the new technology will be primarily incorporated into enterprises through existing spending.
“Generative AI’s best channel to market is through the software, hardware and services that organizations are already using,” said Lovelock. “Every year, new features are added to tech products and services as add-ons or upgrades.”
With organizations spending $1.58 trillion in 2024 on IT services, Gartner projects this market to generate the most revenue on a worldwide basis next year.
Is the GenAI Bubble Finally Popping?
(Nicoleta Ionescu/Shutterstock)
Doubt is creeping into discussion over generative AI, as industry analysts begin to publicly question whether the huge investments in GenAI will ever pay off. The lack of a “killer app” besides coding co-pilots and chatbots is the most pressing concern, critics in a Goldman Sachs Research letter say, while data availability, chip shortages, and power concerns also provide headwinds. However, many remain bullish on the long-term prospects of GenAI for business and society.
The amount of sheer, unadulterated hype layered onto GenAI over the past year and a half certainly caught the attention of seasoned tech journalists, particularly those who lived through the dot-com boom and ensuing bust at the turn of the century, not to mention the subsequent rise of cloud computing and smartphones with the introduction of Amazon Web Services and the Apple iPhone in 2006 and 2007, respectively.
The big data boom of the early 2010s was the next tech obsession, culminating with the coronation of Hadoop as The New New Thing, to paraphrase Michael Lewis’ illuminating 1999 book into Silicon Valley’s fixation on continuous technological reinvention. After the collapse of Hadoop–slowly at first, and then all of a sudden in 2019–the big data marketing machine subtly shifted gears and AI was the hot new thing. Several other new (new) things made valiant runs for attention and VC dollars along the way–Blockchain will change the world! 5G will turbocharge edge computing! Self-driving cars are almost here! Smart dust is new oil!–but nothing really seemed to really gain traction, and the big data world made incremental gains with traditional machine learning while wondering what these newfangled neural networks would ever be good for.
GenAI is the newest new thing
That is, until OpenAI dropped a new large language model (LLM) called ChatGPT onto the world in late 2022. Since then, the hype level for neural network-powered AI, and transformer network-based GenAI in particular, has been eerily reminiscent of these previous Big Moments In Tech. It’s worth pointing out that some of these big moments turned out to be actual inflection points, such as mobile and cloud, some had us asking ourselves “What were we thinking (blockchain, 5G), while it took years for the full lessons from other technological breakthroughs to become apparent (the dot-com boom, even Hadoop-style computing).
So the big question for us now is: Which of those categories will we be putting GenAI into in five years? One of the voices suggesting AI may go the way of 5G and blockchain is none other than Goldman Sachs. In a much-read report from the June edition of the Goldman Sachs Research Newsletter titled “Gen AI: too much spend, too little benefit?” Editor Allison Nathan ponders whether AI will pan out.
“The promise of generative AI technology to transform companies, industries, and societies continues to be touted, leading tech giants, other companies, and utilities to spend an estimated ~$1tn on capex in coming years, including significant investments in data centers, chips, other AI infrastructure, and the power grid,” she writes. “But this spending has little to show for it so far beyond reports of efficiency gains among developers.”
Nathan interviewed MIT Professor Daron Acemoglu, who said that only a quarter of tasks that AI is supposed to automate will actually be automated in a cost-effective manner. Overall, Acemoglu estimates that only 5% of all tasks will be automated within 10 years, raising the overall productivity of the United States by less than 1% over that time.
“Generative AI has the potential to fundamentally change the process of scientific discovery, research and development, innovation, new product and material testing, etc. as well as create new products and platforms,” Acemoglu told Nathan. “But given the focus and architecture of generative AI technology today, these truly transformative changes won’t happen quickly and few–if any–will likely occur within the next 10 years.”
Accelerating GenAI progress by ramping up production of its two core ingredients–data and GPUs–probably won’t work, as data quality is a big piece of the equation, Acemoglu said.
GenAI seems to attract irrational exuberance (Roman-Samborskyi/Shutterstock)
“Including twice as much data from Reddit into the next version of GPT may improve its ability to predict the next word when engaging in an informal conversation,” he said, “but it won’t necessarily improve a customer service representative’s ability to help a customer troubleshoot problems with their video service.”
A shortage in chips suitable for training GenAI models is another factor in Goldman’s pessimistic (some would say realistic) take on GenAI. That has benefited Nvidia enormously, which saw revenue grow by more than 260%, to $26 billion, for the quarter ended April 28. That helped pump its market cap over the $3-trillion market, joining Microsoft and Apple as the most valuable companies in the world.
“Today, Nvidia is the only company currently capable of producing the GPUs that power AI,” Jim Covello, Goldman’s head of global equity research, wrote in the newsletter. “Some people believe that competitors to Nvidia from within the semiconductor industry or from the hyperscalers–Google, Amazon, and Microsoft–themselves will emerge, which is possible. But that’s a big leap from where we are today given that chip companies have tried and failed to dethrone Nvidia from its dominant GPU position for the last 10 years.”
The huge costs involved in training and using GenAI act as headwinds against any productivity or efficiency gains that the GenAI may ultimately deliver, Covello said.
“Currently, AI has shown the most promise in making existing processes–like coding–more efficient, although estimates of even these efficiency improvements have declined, and the cost of utilizing the technology to solve tasks is much higher than existing methods,” he wrote.
Nvidia’s fortunes have skyrocketed thanks to GPU demand from GenAI
Covello was semiconductor analyst when smartphones were first introduced, and learned a few lessons about what it takes to actually realize monetary gains from technological innovation. For instance, the smartphone makers promised to integrate global positioning systems (GPS) into the phones, he said, and they had a roadmap that proved prescient.
“No comparable roadmap exists today” for AI, he said. “AI bulls seem to just trust that use cases will proliferate as the technology evolves. But eighteen months after the introduction of generative AI to the world, not one truly transformative–let alone cost-effective–application has been found.”
Finally, the amount of power required to train LLMs and other GenAI models has to be factored into the equation. It’s been estimated that AI currently consumes about 0.5% of the world’s energy, and that amount is expected to increase in the future.
“Utilities are fielding hundreds of requests for huge amounts of power as everyone chases the AI wave, but only a fraction of that demand will ultimately be realized,” says Brian Janous, the Co-founder of Cloverleaf Infrastructure and formerly the VP of energy at Microsoft.
The total capacity of power projects waiting to connect to the grid grew nearly 30% last year, with wait times currently ranging from 40-70 months, Janous said. With so many projects waiting for power, data centers looking for more power to fuel AI training will become “easy targets.”
The US needs to expand its grid to handle expected increase for power demand, but that isn’t likely to be done cheaply or efficiently, he said. “The US has unfortunately lost the ability to build large infrastructure projects–this is a task better suited for 1930s America, not 2030s America,” Janous said. “So, that leaves me a bit pessimistic.”
The enormous electricity demands of AI, and the US’s inabilty to build new power sources, also pose headwinds to AI success (BESTWEB/Shutterstock)
But not everyone is pessimistic about AI’s future. One GenAI optimist is Joseph Briggs, Goldman’s senior global economist. In his article countering Acemoglu, Briggs estimates that GenAI ultimately will automate 25% of all work tasks and raise US productivity by 9% and GDP growth by 6.1% cumulatively over the next decade. What’s more, GenAI will not only automate some existing tasks currently done by humans, but will spur the creation of new tasks, he said.
“…[T]he full automation of AI exposed tasks that are likely to occur over a longer horizon could generate significant cost savings to the tune of several thousands of dollars per worker per year,” he wrote. “The cost of new technologies also tends to fall rapidly over time. Given that cost-saving applications of generative AI will likely follow a similar pattern, and that the marginal cost of deployment will likely be very small once applications are developed, we expect AI adoption and automation rates to ultimately far exceed Acemoglu’s 4.6% estimate.”
Kash Rangan is another GenAI believer. In an interview with the Goldman editor Nathan, the senior equity research analyst said he’s amazed at the pace of GenAI innovation and impressed at the infrastructure buildout of the cloud bigs. He acknowledged that GenAI hasn’t discovered its killer app yet, in the way that ERP dominated the 1990s, search and e-commerce dominated the 2000s, and cloud applications dominated the 2010s.
“But this shouldn’t come as a surprise given that every computing cycle follows a progression known as IPA—infrastructure first, platforms next, and applications last,” Rangan said. “The AI cycle is still very much in the infrastructure buildout phase, so finding the killer application will take more time, but I believe we’ll get there.”
His colleague, Eric Sheridan, joined him in a bullish stance.
“So, the technology is still very much a work in progress. But it’s impossible to sit through demonstrations of generative AI’s capabilities at company events or developer conferences and not come away excited about its long-term potential,” he said.
“So, while I would never say I’m not concerned about the possibility of no payback, I’m not particularly worried about it today, though I could become more concerned if scaled consumer applications don’t emerge over the next 6-18 [months],” Sheridan said.
The promise of GenAI remains high, if unfulfilled at the end of the day. The big question right now is whether GenAI’s returns will go up before the clock runs out. The clock is ticking.
Related Items:
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GenAI Hype Bubble Refuses to Pop
When GenAI Hype Exceeds GenAI Reality
Generative AI is getting kicked off its pedestal — it will be painful but it’s not a bad thing
It’s been two years since the phrase “generative AI” began cluttering my email inbox. It wasn’t a new term (it appeared in one of Gartner’s famous hype cycle reports back in 2020) but as the summer of 2022 came to a close, the inbound flow of messages and pitches I received were a clear sign that buzz was quickly building for AI-powered tools that could generate content–such as text, images and computer code. And when OpenAI launched ChatGPT in November 2022, generative AI catapulted into the mainstream culture and has been flying high ever since.
Something in that cheery narrative has changed during the past few weeks, however.
Goldman Sachs called generative AI “overhyped” and “wildly expensive”; VC firm Sequoia Capital said “the AI bubble is reaching a tipping point”; a spate of media headlines such as “The AI Hype Machine is Running On Empty” are zealously pouring cold water on the whole affair.
Why? Well, generative AI chatbots struggle to answer basic questions or hallucinate incorrect information. The most sophisticated generative AI models are constantly hungry for data and computing power. Generative AI startups with little to no revenue have to constantly scrounge for massive funding rounds to stay afloat. Fortune 500 companies can’t put generative AI use cases into production because of concerns about accuracy, liability and security.
And with the S&P 500 suffering its biggest selloff in two years on Monday, there’s a growing sense that the Generative AI bubble has begun to deflate.
Gartner’s hype cycle now says generative AI has passed the “Peak of Inflated Expectations” and is headed straight for a looming “Trough of Disillusionment.” If that’s true, what comes next will be painful and disruptive. Investment dollars could dry up. Startups could fail. There could be layoffs.
For many of the startup employees, founders, and investors who put in the work and took the risks necessary for the generative AI sector to take off, the sting of the market correction will be unjust and brutal. But knocking generative AI off of its lofty pedestal is also necessary for the long-term sustainability of the AI landscape, Kjell Carlsson, a former analyst at Forrester Research who is now head of AI strategy at enterprise data platform Domino Data Lab, told me.
“I’m fairly confident that folks will recognize that Gen AI isn’t all the AI,” he said, referring to the wide variety of other artificial intelligence technologies, including predictive AI and machine learning, that were already delivering real return-on-investment before generative AI came on the scene. “Gen AI is one set of technologies that are part of this broad toolkit of different technologies that take work,” he explained. “There’s no magic button, it’s all about leveraging technologies for the right use cases.”
Let’s be clear: generative AI is not going away. These models and tools, from ChatGPT and Microsoft Copilot to Google’s Gemini, Anthropic’s Claude and Meta’s Llama, have already become part of our lives– for productivity, for efficiency, or just for fun. Just as we’ve become accustomed to getting any information we need in seconds by doing a Google search, so too will the ability to obtain easy-to-read summaries of work meetings, to compose memos to colleagues, and to create images and presentations by speaking just a few words.
But let’s also get real: The massive amount of generative AI investment, estimated to be at the tune of $1 trillion, has yet to pay off. Much of that may not be as ridiculous as, say, the dot-com bubble of UrbanFetch and Pets.com (I well remember getting ice cream deliveries and puppet swag), but it’s difficult to argue against the notion that generative AI is getting the reality check it deserves.
“The irony of this is that I think I was the first of the industry analysts to jump on the Gen AI bandwagon,” said Carlsson. “While it’s been a success by anyone’s measure, the expectations around how quickly that would impact the bottom line of major organizations weren’t based in reality.”
That’s where the so-called Trough of Disillusionment becomes an important stage for any tech development, Gartner’s global chief of research Chris Howard said in a recent video. The premise is simple: After an initial burst of excitement and enthusiasm by early adopters, new technology makes its way into the hands of mainstream users who find it doesn’t live up their overinflated expectations. A retrenchment follows, during which the technology is refined and expectations are reset.
“It’s not this dark, dangerous place,” Howard explained in the video. “It’s where we figure out how to make something work–or not.”
For generative AI, the trough will be a phase marked by small incremental progress in applications that deliver real benefits to businesses and to users, and less by proclamations by OpenAI CEO Sam Altman about creating “the most powerful technology humanity has yet invented” with artificial general intelligence (AGI) — though it might make for less sexy headlines.
Even Dan Ives, a Wall Street tech analyst at Wedbush who remains bullish on AI stocks, said this is a key period for tech companies to walk the walk, not just talk the talk, when it comes to generative AI. They need to “show the use cases and monetization to justify the AI Revolution,” he told me in a text.
Ives said that he believes Microsoft, AMD, Nvidia, Palantir and Oracle have shown they can deliver real value. Still, with so many generative AI startups riding on multi-billion-dollar valuations, the sector as a whole still has a lot to prove.
There are no guarantees, but there is a long history of AI technologies that have become mature and gone on to contribute to other, newer AI disciplines, like computer vision– which has become a key part of today’s multimodal generative AI (AI that can generate not just text but images and video, for example).
So perhaps generative AI, pushed along by other, newer technologies like agentic AI (AI systems designed to act like autonomous agents to pursue complex goals and workflows) can still reach its full potential.
Now, perhaps, it’s time for the real down-and-dirty work in generative AI to begin. “I think this will be a false AI Winter,” said Steve Jones, an executive VP at tech consultancy Capgemini, in a LinkedIn post,. ”One where hopefully the hype dies, and we can concentrate on getting work done.”
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Data Center Outsourcing Market Forecasts from 2024 to 2029: Increasing Demand for Shared Infrastructure, Growing Need for Affordable IT Solutions
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Data Center Outsourcing Market
Data Center Outsourcing Market
Dublin, Aug. 13, 2024 (GLOBE NEWSWIRE) — The “Global Data Center Outsourcing Market – Forecasts from 2024 to 2029” report has been added to ResearchAndMarkets.com’s offering.
The data center outsourcing market is evaluated at US$195.537 billion for the year 2022 growing at a CAGR of 5.06% reaching the market size of US$276.282 billion by the year 2029
It is expanding steadily as businesses increasingly contract with suppliers who have superior resources and technical know-how to handle their IT infrastructure, including cloud computing services. The emergence of big data, artificial intelligence, and cloud services has made this easier. One of the main factors propelling the global market for data center outsourcing and hybrid infrastructure managed services is the increasing need for better IT infrastructure scalability, efficiency, and cost-effectiveness.
Managed services for hybrid infrastructure and data center outsourcing are becoming more and more appealing choices for businesses trying to cut expenses. Furthermore, the need for managed hybrid infrastructure services and data center outsourcing is being driven by the growing demand for cloud-based services.
Increasing demand for shared infrastructure
The demand for virtual storage services increased due to the growth of multiple industries and the rise in shared infrastructure services. Other factors driving the data center outsourcing market include higher spending levels on business technology and IT services and a rise in market research and development initiatives. Additionally, during the projection period, the data center outsourcing market will have new opportunities due to advancements in technology, modernization of manufacturing practices, and rising demand from emerging economies.
Growing need for affordable IT solutions
Businesses are searching more and more for solutions that are both affordable and valuable in the long run. Managed services for hybrid infrastructure and data center outsourcing offer an affordable means of utilizing the newest IT solutions while minimizing operating expenses. With the help of these solutions, businesses can contract out the management of their IT infrastructure to a third-party supplier, who can offer a variety of services like networking, hosting, and storage. They can also offer improved data security, scalability, and dependability
The demand for cloud-based services is rising.
An increasingly significant component of contemporary IT infrastructure is cloud computing. Businesses are using cloud computing solutions more frequently in an effort to cut expenses while enhancing scalability, security, and performance. Cloud-based solutions provide an affordable means of storing information, programs, and services, and enabling access to these resources from any location and on any device. Because data is stored in multiple locations, making it more difficult for hackers to access, cloud-based solutions are also more secure which is also leading to market growth.
North America is expected to grow at a high rate during the forecast period.
Owing to the region’s strong digital economy, outsourcing providers are able to effectively provide scalable and secure data solutions. Furthermore, a large number of multinational companies are based in North America and are looking for flexible and affordable data center solutions. The confluence of these elements establishes the area as a hub for the expansion of data center outsourcing, drawing in investments from both local and foreign sources. For example, Rimini Street unveiled Rimini ONE, a complete outsourcing solution that includes IT operations, support, and maintenance, in March 2023. By giving businesses a centralized, efficient source for essential IT services, it lowers the expenses and complexity involved in running data centers. Additionally, by outsourcing, it allows companies to concentrate on their core competencies while guaranteeing the dependability and effectiveness of their data center infrastructure.
Market key launches
Story continues
In August 2023,Fujitsu has been identified by Gartner as a Visionary in their most recent Data Center Outsourcing and Hybrid Infrastructure Managed Services Magic QuadrantTM. Being listed in the Gartner Magic Quadrant as a Visionary, according to Fujitsu, highlights its advantages, progressive business practices, and steady expansion. Fujitsu has been able to redefine industry standards and offer its clients transformative solutions that produce measurable business outcomes thanks to its approach to hybrid IT.
In July 2023,NTT DATA, a global leader in digital business and IT services, today announced the launch of an outsourcing service for security management (MDR service1) to help prevent incidents and minimize damage when they do occur. Beginning in July 2023, the service will be offered in Japan before being made available globally by the end of the fiscal year, which is March 2024. Advanced security engineers with over 20 years of experience in incident response as members of the company’s CSIRT2 organization, along with the knowledge gained from NTT DATA’s global Zero Trust Security Service, will support client companies with the MDR service.
Key Attributes:
Report Attribute
Details
No. of Pages
141
Forecast Period
2022 – 2029
Estimated Market Value (USD) in 2022
$195.54 Billion
Forecasted Market Value (USD) by 2029
$276.28 Billion
Compound Annual Growth Rate
5.0%
Regions Covered
Global
Companies Featured
IBM
NTT DATA, Inc.
Blackstone (QTS Realty Trust, Inc.)
KKR (Ensono)
Accenture
Cognizant
Atos SE
Tata Consultancy Services (TCS)
Capgemini
Infosys Limited
HCL Technologies Limited
Wipro Limited
Segmentation:
By Enterprise Size
By Data Centre Location
Customer Premises
Vendor Premises
By Industry Vertical
By Geography
North America
USA
Canada
Mexico
South America
Brazil
Argentina
Others
Europe
UK
Germany
France
Italy
Others
Middle East and Africa
Saudi Arabia
Israel
Others
Asia Pacific
China
Japan
India
South Korea
Indonesia
Thailand
Taiwan
Others
For more information about this report visit https://www.researchandmarkets.com/r/omf7xx
About ResearchAndMarkets.comResearchAndMarkets.com is the world’s leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.
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CONTACT: CONTACT: ResearchAndMarkets.com Laura Wood,Senior Press Manager press@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900
Gartner: 12 Top Strategic Technology Trends For 2022
From cloud-native platforms to decision intelligence to hyperautomation, here’s what technology research firm Gartner is predicting to be trending in 2022.
Cybersecurity mesh, data fabric architecture, autonomic systems, generative AI and decision intelligence are all hot strategic trends that CEOs should watch out for in 2022, according to research firm Gartner.
“CEOs know they must accelerate the adoption of digital business and are seeking more direct digital routes to connect with their customers,” said David Groombridge, VP analyst at Gartner, in a statement. “But with an eye on future economic risks, they also want to be efficient and protect margins and cash flow.”
Here are Gartner’s top 12 strategic technology trends for 2022.
Data Fabric
Data fabric is a design concept that acts as an integrated layer (fabric) of data and connecting processes, according to Gartner. It uses analytics over existing metadata assets to support the design, deployment and utilization of integrated and reusable data across all environments.
It can reduce data management efforts by up to 70 percent by using analytics to learn and locate where data is used and recommend how it should be changed.
According to Gartner, data fabric provides a flexible, strong integration of data sources across multiple platforms for different users, making data available everywhere and anywhere regardless of where it’s stored.
Data management agility has become vital for organizations, Gartner stated. Data and analytics experts must look beyond traditional data management practices and move toward modern solutions like AI-enabled data integration to reduce human errors and decrease cost.
Cybersecurity Mesh
Cybersecurity mesh is a flexible, composable architectural landscape that integrates distributed and disparate security services across a wide environment.
Gartner predicts that by 2024, organizations using a cybersecurity mesh architecture will decrease the costs associated with security incidents by about 90 percent.
Cybersecurity mesh allows for modern day, stand-alone security solutions to work cohesively to improve security overall by shifting control points closer to the assets they’re protecting. To better secure environments, cybersecurity mesh can rapidly verify identity, context and policy adherences across cloud and noncloud environments.
Privacy-Enhancing Computation
Privacy-enhancing computation techniques allow secure data processing, sharing, cross-border transfers and analytics that protect data while it’s being used, according to Gartner.
It safely secures the data processing in untrusted environments, which is becoming more and more critical as privacy and data protection laws continue to evolve. Through privacy-enhancing computation, privacy-protection techniques allow value to be extracted from data while still meeting compliance requirements, according to Gartner.
This technology is increasingly moving from academic research to real-time projects that deliver real value and new forms of computing and decreasing the risk of compromising data breaches.
Cloud-Native Platforms
Cloud-native platforms allow IT leaders to build new applications that are long withstanding and flexible. Cloud adoption has been on the rise since the start of the pandemic, with Gartner predicting that cloud deployments will quickly surpass private data center workloads.
Cloud-native platforms improve on the traditional “lift-and-shift” approach to cloud, Gartner stated. The ever-increasing move to cloud provides an even more urgent need to enhance secure access to the web, cloud services and other cloud-native applications.
Composable Applications
Composable applications are built from modular components that center around the business environment. They make it simple to use and reuse code, rapidly increasing the time it takes to go to market with new software solutions.
As business needs are rapidly evolving, organizations need to deliver innovation quickly and securely all while simultaneously adapting its applications, according to Gartner. Capabilities must be reassessed from the inside out, understanding and implementing the “composable enterprise.”
Decision Intelligence
Decision intelligence is the most practical approach to improve organizational decision making, according to Gartner. Effective decision making in modern-day, challenging business environments are critical and require set of processes. Intelligence and analytics are used to inform, learn from and refine decisions, driving new core competencies.
It can support and accelerate human decision making and, potentially, automate it through the use of augmented analytics, simulations and artificial intelligence, according to Gartner.
Hyperautomation
Hyperautomation is a unique way to rapidly identify, vet and automate as many business and IT processes as possible. Through artificial intelligence, machine learning, robotic process automation (RPA), integration platform-as-a-service, low-code/no-code tools and more, hyperautomation helps enable scalability and remote operations and fix business model disruptions, according to Gartner.
As a business-driven approach, it can be orchestrated across multiple technologies and is a fast-growing trend as businesses are looking for more efficient and agile ways to organize and address operations.
AI Engineering
Artificial intelligence engineering automates updates to data, models and applications to streamline AI delivery, Gartner stated. With strong AI governance, AI engineering can operationalize the delivery of AI to ensure ongoing valued business practices.
This ever-evolving discipline focuses on developing tools, systems and processes that help humans achieve mission outcomes. With the rise in computing power and massive datasets, the creation of new AI, models and algorithms encompass thousands of variables and is capable of making quick and impactful decisions.
Distributed Enterprises
Distributed enterprises are a reflection of a digital, remote-first business model that improves employee experiences, digitalize consumer and partner touchpoints and builds out product experiences, Gartner stated.
Distributed enterprises, such as a hospital, bank or retailer, better serve the needs of remote employees and customers who increasingly demand virtual services and hybrid workplaces. But with distributed enterprises comes increases security needs as the attack surface for a cybercriminal becomes larger.
Total Experience
Total experience is a strategic business approach that encompasses employee experience, customer experience, user experience and multi-experience across a variety of touchpoints to enable growth within a company, according to Gartner.
Total experience can drive greater company, customer and employee satisfaction, loyalty and advocacy through an intersection of multiple avenues to achieve a transformational business outcome.
Autonomic Systems
Autonomic systems are self-managed physical or software systems that learn from their environments and greatly modify their own algorithms in real time, according to Gartner. This optimizes their behavior in complex environments.
They build a flexible set of technological capabilities that help support new requirements and situations, enhance performance and protect against cyberattacks without human intervention.
Generative AI
Generative AI learns about artifacts from data and generates new creations that are similar to the original, but doesn’t repeat it, Gartner stated. While it advances machine learning, computer vision and edge artificial intelligence drive adoption, it also improves existing process and platforms and helps with organizational needs to streamline and speeds up operations.
Through efficient use of data, models and compute, and responsible AI, generative AI has the potential to create new forms of innovative content accelerate research and development cycles and process in industries from medicine to product creation to engineering.