Gartner’s Top 10 Strategic Technology Trends for 2023

Annually, the research firm Gartner highlights a selection of strategic technology trends that are poised to significantly influence enterprise strategies. These trends offer both prospects for advancement and potential pitfalls that organizations must navigate carefully to sustain their strategic progress and fulfill long-term aspirations.

The “Top Strategic Technology Trends for 2023” by Gartner takes a holistic view, acknowledging the complexities of the current economic landscape, which is marked by challenges in the supply chain, energy procurement, and workforce skill gaps. The trends identified for 2023 are tailored to address four critical organizational priorities.

The urgency for this shift is highlighted by Frances Karamouzis, Distinguished VP Analyst at Gartner, who states that the task at hand extends beyond accelerating digital transformation. It’s about reinforcing the organizational framework with robust technological fibers that can withstand the economic turbulence and propel businesses forward.

Also read: Gartner’s Top 10 strategic technology trends of 2024

These are Gartner’s top 10 strategic technology trends for 2023

Sustainability: The Foundation of Future Tech

At the heart of the technological evolution lies a critical and pressing agenda: sustainability. It has rapidly climbed the priority ladder, becoming more than just an ethical choice; it’s now a strategic imperative. Gartner’s insights underscore this shift, with environmental and social changes securing their spot among the top concerns for investors, rivalling even profit and revenue.

In a recent Gartner survey, CEOs reported that environmental and social changes are now a top three priority for investors, after profit and revenue.

The call to action is for executives to champion innovations that cater to the burgeoning Environmental, Social, and Governance (ESG) demands. The path forward involves crafting a new framework for sustainable technology that accomplishes several vital goals. It must enhance the energy and material efficiency of IT services and foster broader organizational sustainability. This could be through advancements in traceability, analytics, renewable energy sources, and the integration of AI to optimize resource utilization.

Furthermore, sustainability in technology is not a standalone venture but a collaborative one. The technology solutions of tomorrow should empower not just the organizations deploying them but also their customers in achieving their sustainability targets. This comprehensive approach will pave the way for eco-friendly innovation that resonates with a broader audience and drives the market towards greener solutions.

By weaving sustainability into the fabric of every technological trend, organizations can ensure they’re not just future-proofing their businesses but also contributing positively to the planet. The result is a dual-purpose strategy where each technological investment and initiative is also a step towards a more sustainable future for all.

The Gartner survey reveals a compelling statistic that highlights this shift: environmental and social changes have become a top three priority for investors.

Metaverse: Merging Realities for Innovative Engagement

As we inch closer to the realization of a collective virtual shared space, Gartner defines the metaverse as a convergence of virtually enhanced physical reality and digital reality, creating persistent and immersive experiences. The concept of the metaverse goes beyond gaming and entertainment, heralding a new era where businesses, social interactions, and digital commerce merge seamlessly.

By 2027, Gartner predicts that over 40% of large organizations worldwide will use a combination of Web3, AR cloud, and digital twins in metaverse-based projects aimed at increasing revenue.

This prediction reflects a future where the metaverse becomes an integral component of corporate strategy, driving innovation and revenue growth. Organizations will increasingly leverage Web3’s decentralized networks, augmented reality clouds that overlay digital information onto the physical world, and digital twins that simulate real-world objects and systems. These technologies will provide businesses with unprecedented opportunities to create, test, and deploy solutions in a fully simulated environment before they manifest in the real world.

Superapps: The Rise of All-In-One Platforms

Superapps are redefining the landscape of applications by amalgamating the functionalities of an app, platform, and ecosystem. Gartner’s insights suggest a future where superapps dominate the digital experience, offering users a one-stop shop for a myriad of services and features.

By 2027, Gartner predicts that more than 50% of the global population will be daily active users of multiple superapps.

This trend towards superapps indicates a shift in user preferences towards more integrated and holistic digital experiences. Users are gravitating towards platforms where they can access a diverse range of services—from messaging and social media to financial transactions and business applications—all within a single interface. This not only streamlines the user experience but also opens avenues for businesses to offer more personalized and contextually relevant services.

Superapps also provide fertile ground for third-party developers, who can create and deploy mini-apps within the ecosystem, thus enriching the platform’s offerings and drawing in a broader user base. This flexibility makes superapps particularly appealing in markets where mobile penetration is high, and digital services are rapidly consolidating.

The future envisioned by Gartner speaks to a digital ecosystem where convenience, functionality, and integration are paramount. As superapps evolve, they will likely redefine the boundaries between software applications, becoming a crucial element in the digital strategy of businesses aiming to capture and retain the attention of their audience.

Adaptive AI: Intelligent Flexibility in Tech

Adaptive AI is an advanced form of artificial intelligence that can modify its own algorithms in real-time to adapt to new circumstances, enabling it to evolve as it encounters new data. This form of AI stands in contrast to traditional AI models, which are static and can become outdated as the environment changes.

Adaptive AI systems aim to continuously retrain models and learn within runtime and development environments based on new data to adapt quickly to changes in real-world circumstances that were not foreseen or available during initial development.

The impact of this trend is substantial as it emphasizes the critical need for AI systems to be agile and flexible. In the rapidly changing landscape of business and technology, the ability of AI to adjust to new data, trends, and operational demands is essential. Traditional AI models require retraining by data scientists and AI experts, which can be time-consuming and impractical in fast-paced environments.

Adaptive AI systems can be likened to self-learning organisms that can adjust their behavior based on continuous feedback. This allows them to stay relevant and effective without human intervention, making them ideal for scenarios where real-time decision-making is crucial, such as in financial markets, autonomous vehicles, or cybersecurity.

One of the main challenges that adaptive AI addresses is the dynamic nature of data and patterns. As businesses and environments evolve, so too does the data that AI systems need to analyze. Adaptive AI can recalibrate its algorithms to these shifts, ensuring that insights and decisions are based on the latest information.

This adaptability is not just about maintaining effectiveness; it also involves enhancing the trustworthiness and security of AI systems. By constantly updating their knowledge and rules, adaptive AIs can reduce the risk of biases, outdated information, and security vulnerabilities that might compromise their decisions.

Gartner’s Outlook: By 2026, entities that incorporate adaptive AI into their operations will surpass their competition in model implementation by at least 25%.

Digital Immune System (DIS): Enhancing Reliability and Revenue

The concept of a Digital Immune System (DIS) takes its cue from the biological immune system, which protects organisms from pathogens. In the digital realm, a DIS refers to a composite set of capabilities that enhance the resilience, stability, and security of digital solutions.

By 2025, organizations that invest in building digital immunity will reduce system downtime by up to 80%.

Here’s what stands at the core of a Digital Immune System:

1. Automated Testing and Quality Assurance: Just as a biological immune system learns to recognize and respond to threats, a DIS learns about the system’s normal operations and can automatically identify anomalies. This involves rigorous automated testing to preemptively discover and address faults before they become failures.
2. Proactive Monitoring and Anomaly Detection: With continuous monitoring, the DIS can detect issues in real-time. Using machine learning algorithms, it can learn from historical data to predict potential system disruptions and take preemptive actions.
3. Software Supply Chain Security: In today’s interconnected world, software is often assembled from multiple sources, which can introduce vulnerabilities. A DIS includes measures to secure the software supply chain, ensuring that all components are verified and trustworthy.
4. Rapid Incident Response: When incidents occur, speed is of the essence. A DIS includes tools and protocols for quick incident resolution, minimizing downtime and impact on users.
5. AI and Automation for IT Operations: AI tools analyze vast amounts of operational data to anticipate issues, while automation handles routine and complex tasks to maintain system health without human intervention.
6. Chaos Engineering and Resilience Testing: By intentionally injecting failures into the system in a controlled environment, organizations can test their digital immune system’s response and fine-tune it for real-world scenarios.

The goal of a DIS is not only to protect against and respond to disruptions but also to learn and adapt from them, thereby improving over time. It goes beyond traditional cybersecurity and IT practices by embedding resilience and self-healing capabilities directly into digital products and platforms.

For businesses, the implications are far-reaching. In a digital-first economy, customer experiences and business continuity heavily rely on the underlying technology’s robustness. An effective Digital Immune System can thus be a significant differentiator, ensuring customer satisfaction, loyalty, and ultimately contributing to a resilient business model that can withstand and rapidly recover from the unexpected.

Applied Observability: Data-Driven Competitive Edge

Applied Observability isn’t just about monitoring systems but is a strategic approach that integrates monitoring with analysis and action. It enables organizations to make better decisions and respond more rapidly by using the data generated by various digital interactions.

Applied observability enables organizations to exploit their data artifacts for competitive advantage.

At the heart of applied observability are the following key aspects:

1. Data Artifacts Utilization: Data artifacts are the byproducts of digital interactions, like logs, metrics, traces, and events. These artifacts are used to gain insights into system performance and user behavior.
2. Accelerated Decision-Making: With applied observability, the goal is to streamline the process from data collection to decision-making. This involves correlating data from different sources and providing actionable insights in real-time.
3. Orchestration and Integration: Instead of siloed monitoring tools, applied observability emphasizes an integrated approach. It orchestrates data from various sources, providing a unified view that helps in understanding complex system interactions.
4. Proactive Response: By analyzing data artifacts, organizations can predict and prevent issues before they affect users. This proactive approach is crucial in maintaining system reliability and user satisfaction.
5. Strategic Importance of Data: Applied observability elevates the role of data in organizational strategy. It ensures that the right data is available at the right time, empowering rapid actions based on confirmed stakeholder actions.

In the context of a broader digital strategy, applied observability is about more than just technology; it’s about using data to create a culture of continuous improvement and learning. As businesses become more digital, the ability to observe, analyze, and act on data quickly becomes a critical capability for success.

For example, in e-commerce, applied observability can help in understanding customer behaviors and preferences, leading to personalized experiences and targeted offerings that can boost sales. In manufacturing, it can predict equipment failure, reducing downtime and maintenance costs.

AI Trust, Risk, and Security Management: Safeguarding AI

AI Trust, Risk, and Security Management, or AI TRiSM, focuses on establishing frameworks and practices that ensure the trustworthiness, security, and governance of AI systems. It’s about embedding security, risk management, and trustworthiness in the fabric of AI solutions to foster responsible AI use.

A Gartner survey indicates that organizations actively managing AI risks achieve better outcomes and derive more value from AI projects.

Key components of AI TRiSM include:

1. Model Reliability and Trustworthiness: It’s crucial to ensure that AI models perform consistently and make decisions that can be trusted by their human users. This requires regular monitoring and validation of AI outputs.
2. Security Measures: As AI systems process and store large amounts of sensitive data, securing these systems against breaches and unauthorized access is essential.
3. Data Protection: Ensuring privacy and compliance with regulations like GDPR for AI systems that handle personal data is a must. AI TRiSM involves practices that safeguard user data throughout its lifecycle.
4. Ethical Considerations: AI TRiSM addresses the ethical implications of AI systems, ensuring that they do not unintentionally discriminate or cause harm.
5. Cross-Functional Collaboration: To manage AI risks effectively, a collaborative approach is required. This means different units of the business—like IT, legal, compliance, and operations—must work together.
6. Governance Frameworks: Implementing clear governance frameworks helps in managing and mitigating the risks associated with AI systems, ensuring that they adhere to organizational and societal norms.

A Gartner survey indicated that 41% of organizations had experienced an AI-related breach or incident, which underscores the need for robust AI TRiSM practices. Despite this, the survey also showed that organizations which actively managed AI risks not only mitigated these issues but also moved more AI projects from proof-of-concept to production, reaping greater business value.

Take, for example, the deployment of AI in financial services for credit scoring. AI TRiSM will ensure the algorithms used are transparent, do not discriminate, and that the data they use is secure and used in compliance with financial regulations. It also means that if something goes wrong, there is a clear process for rectifying issues.

Implementing AI TRiSM is not just about preventing negative outcomes; it’s also about building confidence in AI systems so that their benefits can be fully realized. When stakeholders trust that AI is used responsibly and securely, they are more likely to support its adoption, which can drive innovation and growth within the organization.

Industry Cloud Platforms: Customized Acceleration

Industry Cloud Platforms are transforming the cloud services landscape by providing tailored solutions designed to meet the unique needs of different business sectors. They offer a mix of software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS), with a focus on the specific requirements of individual industries.

By 2027, Gartner predicts that more than 50% of enterprises will use industry cloud platforms to accelerate their business initiatives.

Here are some aspects of Industry Cloud Platforms:

1. Customization: Industry Cloud Platforms are designed with a deep understanding of industry-specific workflows, regulations, and business processes, allowing organizations to use these platforms with minimal customization.
2. Modularity: They provide modular capabilities, making it easy for businesses to select and combine different services to create solutions that fit their exact needs.
3. Integration: These platforms facilitate integration with existing systems and third-party services, supporting a seamless flow of data and processes.
4. Innovation: By leveraging industry-specific insights and templates, these platforms can significantly reduce the time and effort required to develop new applications, thus accelerating innovation.
5. Compliance: Industry Cloud Platforms often include built-in compliance with industry standards and regulations, which helps reduce the complexity and risk for businesses in highly regulated sectors like healthcare and finance.
6. Ecosystems: They foster ecosystems where third-party developers and partners can create and sell their own applications and services, further enhancing the platform’s capabilities.
7. Scalability: With cloud infrastructure at their core, these platforms can easily scale to handle increased demand, making them suitable for businesses of all sizes.

Consider the healthcare industry, which has specific needs for patient data privacy, regulatory compliance, and complex billing processes. An Industry Cloud Platform for healthcare would come equipped with tools and services tailored to these needs—secure data storage, compliance with health regulations, and revenue cycle management features—thus allowing healthcare providers to focus on patient care rather than IT complexities.

The prediction that over half of all enterprises will use these platforms indicates a significant shift towards vertical cloud solutions. This shift is driven by the recognition that while general cloud services provide a strong foundation, the complexities of different industries often require specialized solutions that are “ready to use” right out of the box.

Platform Engineering: Optimizing the Developer Experience

Wireless Value Realization: Beyond Connectivity

Wireless Value Realization is the strategic utilization of various wireless technologies to advance business objectives, improve operational efficiency, and foster innovative services. The focus here is not just on the ubiquity of wireless connectivity but on leveraging this connectivity to drive tangible business outcomes and insights.

Gartner forecasts that by 2025, 60% of enterprises will be using five or more wireless technologies simultaneously.

Here are the pivotal aspects of Wireless Value Realization:

1. Diverse Technology Portfolio: Enterprises will not rely on a single wireless technology but will use a combination of different wireless solutions to cover a broad range of applications and environments, from indoor Wi-Fi to mobile services and low-power networks.
2. Beyond Connectivity: The role of wireless networks is expanding beyond providing basic connectivity to becoming platforms that can deliver business insights through built-in analytics.
3. Energy Harvesting: Future wireless systems will be designed to harvest energy directly from the network, which could revolutionize the way devices are powered, making them more sustainable and efficient.
4. Business Intelligence: Integrated analysis capabilities will allow wireless networks to not only connect devices but also provide valuable data about their use and performance, contributing to business intelligence efforts.
5. Strategic Asset: The strategic use of wireless technology will allow businesses to optimize their operations, reduce costs, and introduce new business models, turning networks into significant assets for the organization.
6. Integrated Ecosystems: Enterprises will likely integrate various wireless technologies into their IT ecosystems to facilitate new types of services, such as IoT deployments, that can enhance customer experiences or streamline processes.

An example of Wireless Value Realization might involve using Wi-Fi analytics in a retail environment to understand customer movement and behavior, then leveraging that information to optimize store layouts and increase sales. Similarly, in an industrial setting, using a combination of low-power wide-area networks (LPWANs) and real-time location systems (RTLS) can help in monitoring assets and automating inventory management.

The forecast by Gartner suggests that businesses will increasingly recognize the intrinsic value wireless technologies bring to the table. With the right strategy, organizations can not only streamline their operations but also unlock new opportunities for growth and innovation. By 2025, the anticipation is that a significant portion of enterprises will be simultaneously utilizing a variety of wireless solutions, tailored to specific needs and environments, thus maximizing the value derived from their wireless technology investments.

Conclusion

These technology trends offer a roadmap for businesses looking to navigate the rapidly changing digital world. They are not just about keeping up with the latest gadgets or software. They are about finding new ways to work smarter, safer, and more sustainably.

If you’re pondering how to align with these trends to undergo a full digital transformation, Webuters is prepared to assist in sculpting a technology pathway that adheres to your digital evolution aspirations and strategic visions. Let’s Talk!