Organizations are continually disrupted by unforeseen events ranging from COVID-19 to political instability to climate change. However, the potential of digital business as a means of thriving and outperforming the competition throughout these changes remains unmistakable.
The burden for supporting the technical side of digital business falls entirely on your shoulders as an IT leader. You now have the potential to adopt force-multiplying innovations in order to accelerate growth and strategically propel your business ahead.
These advancements will provide:
These tendencies reinforce and build on one another. Taken as a whole, our top strategic technology trends for 2022 will assist you in meeting your goals for scaling, adapting, and growing.
With our first four trends, IT is responsible for designing the trust required in a connected world:
The importance of data has never been more apparent. However, data is frequently compartmentalized within apps, which means it is not being used as efficiently as it could be.
Data fabric connects data across systems and people, making it available wherever it is required.
Data fabric can understand what data is being utilized by scanning metadata inside built-in analytics. Its true value lies in its capacity to propose additional, diverse, and better data, decreasing data administration by up to 70%.
Assess priority areas for implementing data fabric solutions by employing metadata analytics to determine existing data use trends for ongoing business processes. Prioritize locations where real and modeled data differ significantly.
Cloud and data center resources are used to disseminate digital business assets. Traditional, disjointed security techniques centered on corporate perimeters leave firms vulnerable to intrusions.
A cybersecurity mesh architecture offers a modular approach to security based on identification, allowing for the creation of scalable and interoperable services. The shared integrated framework safeguards all assets, independent of location, enabling a security strategy that spans the core of IT services.
By 2024, firms that use a cybersecurity mesh architecture to integrate security solutions and function as a cooperative ecosystem will have reduced the cost effect of individual security events by an average of 90%.
When choosing security solutions, prioritize composability and interoperability. Create a common foundation for composing and integrating security solutions.
The true value of data is found in how it is used for AI models, analytics, and insight rather than just having it.
Privacy-enhancing computation (PEC) techniques enable data to be exchanged across ecosystems while maintaining privacy. Encrypting, dividing, or preparing sensitive data to allow it to be handled without jeopardizing secrecy is one approach.
By 2025, 60% of global enterprises will have implemented one or more privacy-enhancing computation approaches in analytics, business intelligence, or cloud computing.
Enterprises that invest in privacy enhancement technologies early on are sure to get an advantage over the competition.
Examine critical use cases inside your business and the wider ecosystem where there is a need to use personal data in untrusted settings or for analytics and business intelligence reasons.
Lift-and-shift cloud migrations concentrate on moving older workloads to the cloud. Because these workloads were not developed for the cloud, they require extensive upkeep and do not reap any benefits.
Cloud-native solutions take use of the underlying flexibility and scalability of cloud computing to provide faster time to value. They eliminate infrastructure requirements, giving up time to focus on application functionality.
Reduce the number of basic lift-and-shift migrations that do not fully utilize cloud capabilities. Invest in cloud-native platforms and use current application architectural ideas.
As our next trends indicate, IT's duty is to supply the tools that empower fusion teams to shape the transformation:
Fusion teams have several obstacles, including a lack of coding expertise, being trapped into the incorrect technology, and frequently being charged with fast-paced delivery.
Composable applications are built using packaged business capabilities (PBCs) or software-defined business components. PBCs, which can represent a patient or a digital twin, generate reusable modules that fusion teams can self-assemble to construct applications quickly, shortening time to market.
Encourage the use of reusable architectural concepts in all new technology efforts, such as application modernization, new engineering, and the selection of new vendor services. Purchase standard PBCs from application marketplaces.
Decision making can be impacted by a wide range of experiences and prejudices, yet in today's fast-paced environment, companies must make better decisions quicker.
By modeling decisions within a framework, decision intelligence helps corporate decision making. Based on learnings and feedback, fusion teams may monitor, assess, and improve choices.
By combining data, analytics, and AI, decision intelligence systems may be built to assist, enhance, and automate choices.
Begin employing decision intelligence in areas where business-critical decision making requires more data-driven assistance or AI-powered augmentation, or where choices can be scaled and hastened with automation.
Increased emphasis on expansion, digitization, and operational excellence has underlined the need for more effective, ubiquitous automation.
Hyperautomation is a business-driven strategy to identify, validate, and automate as many business and information technology activities as feasible. It necessitates the coordinated use of numerous technologies, tools, and platforms, such as RPA, low-code platforms, and process mining tools.
To achieve coordinated business outcomes, establish comprehensive mapping and prioritizing automation of initiatives rather than just individual tasks.
AI offers game-changing solutions that will allow enterprises to emerge from the epidemic in a strong position, but just adopting AI will not be enough. AI must be optimized by organizations.
AI engineering is the discipline of delivering consistent business value from AI by operationalizing updates to AI models utilizing integrated data, model, and development pipelines. It integrates automated update pipelines with strict AI oversight.
By 2025, the 10% of organizations that develop AI engineering best practices will earn at least three times the value from their AI initiatives as the 90% who do not.
Use AI engineering as a strategic differentiator to create and sustain production AI value.
Establish and improve AI engineering techniques by incorporating best practices from DataOps, ModelOps, and DevOps.
The concept of distributed enterprise originates from two distinct domains. On the one hand, personnel working remotely as a result of COVID-19 need new tools and greater flexibility. On the other hand, customers are more unavailable through traditional, physical channels.
Distributed enterprise is an architectural strategy that is virtual-first and remote-first in order to digitize customer touchpoints and build out experiences to support products.
Plan to pivot company models to gain market share as a result of client and consumer shifts brought about by remote working by implementing "virtual first, remote first" architectural concepts.
Give fusion teams the tools they need to swiftly build and upgrade customer-facing technology.
Total experience combines four disciplines to produce a better experience for customers and employees: customer experience, user experience, staff experience, and multi experience. The objective is to link and improve each of these in order to provide a more comprehensive overall experience for all stakeholders.
Instruct teams working on experience enhancement efforts to collaborate and learn from others.
Make all leaders of experience-related initiatives equally responsible for addressing the requirements of both consumers and workers.
Traditional manual management cannot scale at the same rate as enterprises develop.
Self-managing physical or software systems that learn from their surroundings are referred to as autonomic systems. They may, however, dynamically adjust their own algorithms without requiring software updates, unlike autonomous or automated systems. This enables for quick reactions to change, allowing for sophisticated environment management at scale.
Pilot autonomic technologies in circumstances where early adoption will result in increased agility and performance while managing complex software or physical systems.
AI is often trained to create conclusions, but real force-multipliers may innovate on their own.
Generative AI is a type of AI that learns a digital representation of artifacts from sample data and then utilizes it to create new, unique, realistic artifacts that are similar to the training data but do not replicate it. As a result, generative AI may serve as a catalyst for fast organizational innovation.
Speed content development and R&D efforts by picking proven uses of generative AI to accelerate the creation of new products and enhance artifact personalisation.
What good is technology if it cannot be trusted? At the heart of digital business is a durable and effective IT infrastructure. There is no way to grow cost-effectively without a well-designed foundation.
With a solid framework in place, the organization can now focus on technology that will allow it to grow its digitalization activities.
However, IT cannot keep up with the rate of change on its own. Fusion teams, comprised of IT and business personnel, will interact and drive innovation in order to rapidly digitize the business.
When the foundation and building blocks are in place, it is time to concentrate on technological trends that optimize the value of what the firm generates.
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