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TOP STRATEGIC TECHNOLOGY TRENDS FOR 2022, EXECUTIVE LEADERS PERSPECTIVE

Published 27 January 2022 - ID G00759928 - 16 min read

By Arun Chandrasekaran, Frances Karamouzis, and 4 more

--------------------------------------------------------------------------------

As organizations face demands to grow and digitalize efficiently, executive
leaders must embrace technology breakthroughs as force multipliers. Gartner’s
strategic technology trends highlight key emerging trends and technologies to
adopt, scale and grow.



OVERVIEW








OPPORTUNITIES

 * Executive leaders should use the digital acceleration opportunity created by
   the pandemic to challenge long-held beliefs and sculpt rapid enterprise
   change.
 * Boards of directors and CEOs are increasing budgets for digitalization in
   business units. Executive leaders must seize the opportunity to deliver new
   business models and products to accelerate growth.
 * CEOs are seeking trustworthy and efficient IT operational environments.
   Executive leaders must exploit agile cost models to build lean operating
   environments in a secure, scalable and resilient manner.




RECOMMENDATIONS

Executive leaders contributing to innovation management must:
 * Accelerate growth by deploying technology innovations that will rapidly scale
   digital products and increase market share.
 * Sculpt change by harnessing new technology innovations that allow your fusion
   teams to automate IT and business workflows to leapfrog competitors.
 * Engineer trust by creating resilient digital foundations that maximize the
   value of data and applications, enabling your organization to reap business
   value and scale cost-efficiently.




WHAT YOU NEED TO KNOW

The years 2020 and 2021 have triggered faster technology change than ever
before, and this pace will only increase. In this changing world, CEOs’
priorities are clear: They aim for growth, digitalization and operational
efficiency.

This means that it’s vital for you, as an executive leader, to address the three
themes in our top strategic technology trends for 2022 for action.

Trend Profiles: Click links to jump to profiles
Enlarge Table
Accelerating Growth
Sculpting Change
Engineering Trust
Generative Artificial Intelligence
AI Engineering
Cloud-Native Platforms
Autonomic Systems
Hyperautomation
Privacy-Enhancing Computation Techniques
Total Experience
Decision Intelligence
Cybersecurity Mesh
Distributed Enterprise
Composable Applications
Data Fabric






ACCELERATING GROWTH

You’re no stranger to your organization’s constant pursuit of growth. Our first
four technology trends produce combinatorial innovation and maximize value
creation, enabling exponential growth in the postpandemic world.

The four technology trends that will have the most significant impact in
accelerating your future growth are:


GENERATIVE ARTIFICIAL INTELLIGENCE

Back to Top

Description:
This form of AI learns a digital representation of digital or physical artifacts
from sample input and uses it to generate new, original artifacts that retain a
likeness to the training data, but don’t repeat it. Generative AI can produce
novel media content, such as art, music, prose or synthetic data, and can
propose innovative new examples of physical objects.

Why Trending:
Generative AI promises new levels of automation and creativity. We are at the
cusp of an emerging wave of commercial solutions for use cases such as data
generation, code generation, content creation and creative designs.

Implications:
 * Generative AI will augment marketing, software development, data science and
   business analytics functions across industries.
 * It can potentially reshape the R&D economics across customer experience, new
   product development, software engineering and data science, enabling lean and
   rapid product innovation.
 * Examples:
   * American Express experiments with AI-generated fake fraud patterns to
     sharpen its models’ ability to detect rare or uncommon swindles (see  Fake
     It to Make It: Companies Beef Up AI Models With Synthetic Data).
   * Researchers from the University of South Carolina College of Engineering
     and Computing and from Guizhou University, China, have shown the potential
     for generative AI to speed up the identification of new inorganic materials
     by over a hundredfold (see  Deep Learning Algorithm to Remove Materials
     Discovery Bottleneck in Emerging Tech Industries).


Actions:
 * COOs, CTOs and CMOs should trial generative AI use cases for accelerating R&D
   and for content personalization.
 * Data and analytics leaders should evaluate the use of generative AI to create
   synthetic data when handling large, sensitive data volumes in the cloud or
   with partners.

AUTONOMIC SYSTEMS

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Description:
Autonomic systems feature automation, learning and awareness, which enables them
to adapt their behavior to support new requirements and situations, optimize
their performance, and react to ecosystem changes.

Why Trending:
By 2025, we expect that autonomic systems will have broad application in areas
like robotics, smart vehicles, drones and smart spaces. In the long term,
autonomic systems will mature from simple tasks and stand-alone devices to wider
and more-complex domains, such as collaboration between swarms of smart objects.

Implications:
 * Autonomic systems will enable organizations to respond and adapt quickly to
   market changes, excelling where conventional automation is inadequate.
 * Autonomic systems’ ability to learn and adapt their behavior may make them
   unpredictable. Other challenges relate to social concerns, digital ethics and
   safety, and legal liability.
 * Example:
   * NASA Jet Propulsion Laboratory autonomously explores caves by integrating
     autonomy and AI on a robot (see  Search for Life: Exploring Martian-Like
     Caves).


Actions:
 * Create a multidisciplinary task force to prepare for the business, legal,
   social and ethical consequences of deploying systems that may act in
   unpredictable ways.
 * COOs, CTOs and CIOs should pilot autonomic technologies where early adoption
   will deliver agility and performance benefits in software or physical
   systems.



TOTAL EXPERIENCE

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Description:
This is a business strategy for creating superior shared customer and employee
experiences by interlinking the customer experience, employee experience, user
experience and multiexperience disciplines. Total experience uses digital and
nondigital techniques to enhance customer and employee confidence, satisfaction,
loyalty and advocacy.

Why Trending:
Organizations are seeking better customer experience and employee experience
strategies that lead to greater confidence, satisfaction, loyalty and advocacy.
As digital channels are increasingly the main vehicles for engagement, executive
leaders must identify and improve user experience touchpoints for customers and
employees using multiexperience technologies.

Implications:
 * Total experience will enable organizations to achieve more-resilient business
   outcomes and simultaneously increase revenue from customers and reduce
   internal costs.
 * Organizations that use total experience to transform their business and
   operating models will achieve comparative and competitive advantage.
 * Example:
   * Canadian Blood Services, for example, transformed its business and
     operating models through multiple total experience initiatives (see Top
     Strategic Technology Trends for 2022: Total Experience).


Actions:
 * CMOs, chief customer success leaders and COOs should enhance experiences for
   customers and employees by driving an experience-driven agenda for technology
   investments.
 * CTOs, CIOs and chief customer success leaders should increase both employee
   and customer retention by investing in multiexperience technologies to
   improve user experiences across channels, devices, touchpoints and
   interaction modalities.



DISTRIBUTED ENTERPRISE

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Description:
A distributed enterprise uses digital capabilities to support hybrid and remote
workers, and to deliver services to consumers or citizens — both remotely and in
person.

Why Trending:
The rapid increase in hybrid working makes the reality of distributed
enterprises a “new normal” and creates significant impact on business models,
hybrid workforce and digital enablement.

Implications:
 * Distributed enterprises will lead to a change to business models. With the
   shift to the hybrid working model and the lack of consumers traveling into
   offices, businesses will need to expand digital service models to combine
   virtual and physical offerings.
 * Organizations that move to a combination of office and remote working will
   gain substantial benefits, such as increased employee flexibility and cost
   savings (from reduced office space).
 * Example:
   * Mastercard built a centralized virtual events playbook to help marketers
     effectively organize virtual events at scale (see Case Study: Virtual
     Events Playbook (Mastercard)).


Actions:
 * CEOs and COOs should create fusion teams of IT and business technologists to
   accelerate creation of business models that integrate digital and physical
   services (see Note 1).
 * CHROs and CIOs should reduce employee fatigue and burnout by deploying new
   collaboration tools, redesigning workspaces and processes to support hybrid
   ways of working.


Further Reading:
Top Strategic Technology Trends for 2022: Generative AI
Top Strategic Technology Trends for 2022: Autonomic Systems
Top Strategic Technology Trends for 2022: Total Experience
Top Strategic Technology Trends for 2022: Distributed Enterprise


SCULPTING CHANGE

With increasing use of fusion teams to enable business-led technology creation,
executive leaders must deliver creative new uses of technology to enable their
organizations to scale digitalization rapidly.

The four technology trends that will significantly enable your fusion teams to
sculpt change in your organization are:

AI ENGINEERING

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Description:
This is the discipline of industrializing to AI models. It enables organizations
to industrialize AI to rapidly deliver new kinds of business value and compound
this value over time.

Why Trending:
AI continues to be a top priority for executive leaders; however, reaping its
business value has been elusive and difficult for many enterprises. Executive
and technology leaders need to create a strong and cohesive AI engineering
discipline within their organization to usher governance, agility and
scalability of AI projects.

Implications:
 * AI hype actually underestimates the potential value that AI can provide an
   enterprise, because most of the current value from AI is still derived from
   point-to-point solutions as part of narrow process automation initiatives.
   For AI to deliver transformative value, enterprises need to industrialize the
   creation, deployment and monitoring of AI models in the business ecosystem in
   which they operate.
 * AI engineering will enable organizations to scale from only a few AI models
   in production to hundreds, realizing significant additional value with each
   model deployed.
 * Example:
   * Case Study: Monitoring the Business Value of AI Models in Production
     (Georgia Pacific)


Actions:
 * Data and analytics leaders should Include AI engineering practices as a core
   part of your AI strategy to reduce failures and to industrialize AI
   initiatives.
 * CHROs should partner with CIOs and CTOs to nurture a high-performance team
   from the start. Fill critical roles for AI, develop a training and/or hiring
   program with HR, ensure responsibilities are well-defined among teams and
   define common key performance indicators that emphasize agility, accuracy,
   performance and responsible AI.

HYPER-AUTOMATION

Back to Top

Description:
This is a disciplined approach to rapidly identify, vet and automate as many
business and IT processes as possible. It involves the orchestrated use of
multiple technologies, tools or platforms.

Why Trending:
The main reason that hyper-automation continues to trend is the unrelenting
demand for accelerated growth through business model innovation or disruption,
coupled with the underlying foundation of operational excellence across
processes and functions.

Implications:
 * Hyper-automation enables organizations to design and implement business model
   change that covers many functions. This should lead to long lasting
   competitive advantage.
 * Hyper-automation can be applied to any business or IT process and is not
   partial to any technology or vendor, thereby reducing technical debt and
   enhancing business agility.

 * Example:
   * Case Study: Kick-Starting a Low-Code/No-Code Community of Practice
     (Heathrow Airport)


Actions:
 * Line of business leaders and CFOs should maximize the chance of successful
   hyperautomation initiatives by planning and architecting multiple concurrent
   initiatives. Demand holistic mapping of collective initiatives, rather than
   islands of task automation.
 * Optimize the results of hyperautomation initiatives for business model change
   by using fusion teams throughout the iterative process of designing,
   building, scaling and governing your hyperautomation roadmap.



DECISION INTELLIGENCE

Back to Top

Description:
This is a practical discipline designed to improve organizational decision
making. It involves understanding and engineering how organizations make
decisions and how they evaluate, manage and improve outcomes through feedback.

Why Trending:
Organizations struggle to make decisions in an environment featuring
unprecedented levels of business complexity and uncertainty. There is more
unpredictability about the direction of the market, whether decisions will be
questioned and about the durability of a decision. This implies that decision
making must become more adaptive, allowing business stakeholders to quickly make
changes to the decision flow or logic, should the need arise.

Implications:
 * Decision intelligence helps organizations become disruption-ready and
   resilient by identifying, prioritizing, modeling and (re)engineering
   decisions for improvement.
 * Decision intelligence enables organizations to analyze and improve their
   decision-making process. This enables them to augment and automate it with a
   combination of human workers and techniques such as AI.
 * Example: How Proportunity uses decision intelligence to improve lending
   decisions (see  Podcast Episode: Artificial Intelligence, Data-Driven
   Decisions and Proptech Shaking Up Mortgage Lending With …).


Actions:
 * CEOs, COOs and line-of-business leaders should use decision intelligence to
   improve business-critical decision making with more data-driven support or
   AI-powered augmentation, or to scale and accelerate decisions with
   automation.
 * Data and analytics leaders should model decisions and develop practices that
   incorporate both human and AI decision-making capabilities. Measure results
   by incorporating a feedback loop.

COMPOSABLE APPLICATIONS

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Description:
These are business-centric modular applications that organizations can assemble
by adopting composable business principles.

Why Trending:
Driven by the broad shift to cloud services, APIs now dominate the mainstream
design of applications and application integration. Event-driven interactions
and adoption of low-code solutions are on the rise as well. These cumulative
trends provide a foundation for building composable applications, which
accelerate business agility.

Implications:
 * Composable applications create agility and enable safer, more-efficient and
   faster change.
 * Composability helps business leaders reengineer how they make the most
   complex and pressing business decisions.
 * Example:
   * Case Study: Composable Platforms to Foster Reuse (Ally Financial)


Actions:
 * CEOs and COOs should set up business-IT fusion teams and begin to equip them
   with dedicated design tools.
 * CIOs and CDOs should champion composable architectural principles in all new
   technology initiatives and develop a roadmap for building them.


Further Reading:
Top Strategic Technology Trends for 2022: AI Engineering
Top Strategic Technology Trends for 2022: Hyperautomation
Top Strategic Technology Trends for 2022: Decision Intelligence
Top Strategic Technology Trends for 2022: Composable Applications


ENGINEERING TRUST

To deliver the cost-efficient growth your CEO demands, you need to securely
integrate your digital assets and scale as your business does. The following
trends engineer the trust necessary in a connected world by enabling scalable
and secure integration and processing of data across cloud and noncloud
environments.

The four technology trends that will help most in engineering trust in your
organization are:


CLOUD-NATIVE PLATFORMS

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Description:
These platforms provide capabilities that enable organizations to accelerate the
pace of digitalization more quickly and more effectively.

Why Trending:
A combination of modern application architectures, cloud technologies,
operational best practices and enterprise requirements is empowering
organizations to build and run scalable applications in new ways. Cloud-native
platforms enable enterprises to involve stakeholders aligned with, or embedded
in, the business (also called fusion teams) as they collaborate to improve value
through digitization initiatives.

Implications:
 * Cloud-native platforms enable traditional organizations that lack digital
   talent and expertise to build agile applications and succeed more quickly
   with their digital initiatives.
 * Cloud-native platforms maximize the potential of cloud computing, delivering
   faster time to value, increasing productivity and improving efficiency.
 * Example:
   * Building a Platform for Product Team Productivity (adidas)


Actions:
 * CDOs, CTOs and CIOs should make cloud-native platforms the first choice for
   new initiatives and progressively expand the scope of digital workloads that
   can benefit from cloud-native technologies.
 * CIOs and CTOs should encourage organizational structures (such as platform
   teams) that have the primary goal of enhancing the usability of cloud-native
   platforms and ensure they are in tune with business outcomes and service
   delivery.

PRIVACY-ENHANCING COMPUTATION TECHNIQUES

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Description:
Privacy-enhancing computation (PEC) techniques enable organizations to securely
gain, share, pool, process and analyze personal data without compromising
confidentiality and privacy while the data is in use. They also include robust
approaches to protect personal data in transit and at rest.

Why Trending:
The demand for processing data in untrusted environments (such as in public
cloud) and performing multiparty data sharing and analytics continues to grow
and has become elemental to an organization’s success. Rather than a bolt-on
approach, the increasing complexity of analytics engines and architectures
mandates a by-design privacy capability. The pervasiveness of AI models and the
necessity to train them is only the latest addition to privacy concerns.

Implications:
 * Through the use of various (combinations of) PEC techniques, confidentiality
   can be ensured, even after spillage. Analytics and business intelligence use
   cases can be completed without compliance or privacy risk concerns, allowing
   the gaining and use of information without exposing identifiable data.
 * Other PEC techniques enable organizations to further their cloud adoption
   strategy under residency restrictions or enhance AI model training.
 * Examples:
   *  Synthetic Data Mimics Real Health-Care Data Without Patient-Privacy
     Concerns
   * ING Belgium is in the early stages of using analytics on sensitive
     databases across multiple jurisdictions, using an approach that analyzes
     data while it remains encrypted (see  ING Belgium Sees Opportunities for
     ‘Secret’ Sharing of Encrypted Data).


Actions:
 * CDOs and CISOs should identify where the organization wants to use personal
   data in untrusted environments or for analytics and business intelligence
   purposes, both internally and externally.
 * CMOs and chief revenue officers should enable business value creation in
   otherwise locked opportunities by applying PEC techniques. Main use cases
   include sharing, cloud-based processing and analytics of people’s
   information.

CYBERSECURITY MESH

Back to Top

Description:
This approach extends composable security controls beyond traditional enterprise
perimeters, enabling trusted access by the workforce, clients, business partners
and things.

Why Trending:
Existing approaches to identity and security architectures are not sufficient to
meet today’s rapidly changing demands. Cybersecurity mesh architecture (CSMA)
helps provide a common, integrated security structure and posture to secure all
assets, whether they’re on-premises, in data centers or in the cloud. CSMA
enables stand-alone solutions to work together in complementary ways to improve
overall security posture by standardizing the way the tools interconnect.

Implications:
 * CSMA helps provide a common, integrated security structure to secure all
   assets, whether they’re on-premises, in data centers or in the cloud.
 * CSMA fosters a more-consistent security posture to support increased agility
   for the composable enterprise.
 * Example:  Case Studies: CISOs Take on the ‘Zero Trust’ Challenge


Actions:
 * CISOs should enable a robust defensive posture by eliminating silos and
   inefficiencies, both from an organizational perspective, as well as within
   technology.
 * CIOs and CISOs should combat the increase in security complexity by evolving
   your security infrastructure to be more integrated, focusing on centralized
   administration and decentralized policy enforcement.

DATA FABRIC

Back to Top

Description:
A data fabric informs smarter and more-effective support of data management
tasks, regardless of deployment platform and architectural approach. It does so
by using continuous analytics over existing, discoverable and inferenced
metadata assets to see where and how data is being used.

Why Trending:
Cloud capacity has enabled the expansion of data assets in terms of volume and
variety, while at the same time offering significantly more-complex resource
allocation and utilization models in an on-demand, elastic environment. This has
contributed to a significantly broader spectrum of data and process
distribution.

Implications:
 * Data fabric ensures that lessons learned from interactions with the data can
   be reused for other operational systems, and back-office and analytics
   solutions.
 * Data fabric will automate predictable or inference-based tasks, and offload
   them to systems on an ever-increasing number of data assets. This will
   quadruple the output from human-driven efforts.
 * Example: Equifax’s journey to data fabric (see  Data Fabric From Equifax: A
   Technology, Security and Privacy Overview).


Actions:
 * CDOs and CIOs should confront roadblocks to data sharing by proactively and
   continually instilling enterprisewide data-sharing capabilities across
   people, processes and technologies.
 * CDOs and CIOs should conduct a pilot focusing on one data product or
   domain-oriented prototype that integrates data from a minimum of three
   sources (from three different areas of business operations if possible) and
   vet its fit within your organization.


Further Reading:
Top Strategic Technology Trends for 2022: Cloud-Native Platforms
Top Strategic Technology Trends for 2022: Privacy-Enhancing Computation
Top Strategic Technology Trends for 2022: Cybersecurity Mesh
Top Strategic Technology Trends for 2022: Data Fabric


EVIDENCE

Gartner Annual Tech CEO Survey, 2021: Transitioning From the Pandemic


NOTE 1: MORE ABOUT FUSION TEAMS

Definition: A fusion team is a multidisciplinary team that blends technology, or
analytics and business domain expertise, and shares accountability for business
and technology outcomes. Instead of organizing work by functions or
technologies, fusion teams are typically organized by the cross-cutting business
capabilities, business outcomes or customer outcomes they support.

Context: Fusion teams do not have a prescribed reporting structure. Team leaders
or members may report to either dedicated IT departments or business areas
outside IT. Fusion teams often start as agile project or scrum teams, and
gradually adopt product management discipline to oversee a capability end to
end, from strategy to delivery and continuous enhancements.
 

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