Top Trends in Data and Analytics — Trend #1/12: Adaptive AI Systems @ Gartner 2022

By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number of operationalized AI models by at least 25%.

This is an excerpt of the publication below, with the title above, focusing on the topic in question. For the full version of the article, please, refer to the original publication.

Top Trends in Data and Analytics, 2022

By Rita Sallam, Ted Friedman, and 37 more
11 March 2022

Excerpt by

Joaquim Cardoso MSc.
The Health Revolution

Multidisciplinary Institute for Better Health for All
May 27, 2022

They have the potential to transform your enterprise, and will accelerate in their adoption over the next three years.

You should decide whether to proactively monitor, experiment with or aggressively invest in key trends based on their urgency and alignment to strategic priorities.

The top D&A trends and technologies do not exist in isolation; they build on and reinforce one another.

We have selected our trends for 2022 in part based on their combined effects. Taken together, our top data and analytics technology trends for 2022 will help you meet your organization’s top strategic priorities to anticipate, adapt and scale value.

This, in turn, will enable you to encourage innovation and put in place success metrics and incentives that emphasize learning and reward innovation.

  1. Group 1: Activate Dynamism and Diversity
  2. Group 2: Augment People and Decisions
  3. Group 3: Institutionalize Trust

Group 1: Activate Dynamism and Diversity

  1. Adaptive AI Systems
  2. Data-Centric AI
  3. Metadata-Driven Data Fabric
  4. Always Share Data

1.Adaptive AI Systems

Analysis by: Erick Brethenoux, Soyeb Barot, Ted Friedman


By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number of operationalized AI models by at least 25%

By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number of operationalized AI models by at least 25%.


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

AI engineering orchestrates and optimizes applications to adapt to, resist or absorb disruptions, facilitating the management of adaptive systems.

AI engineering provides the foundational components of implementation, operationalization and change management at the process level in order to enable adaptive AI systems.

Adaptive AI systems support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise.

  • Flexibility and adaptability are now a fundamental business requirement — many organizations unfortunately learned this the hard way during the global COVID-19 pandemic. Reengineering systems has significant impacts on employees, businesses and technology partners. For many enterprises, these changes demand resilience by design and adaptability by definition.

  • The value of fully industrialized AI lies in the ability to rapidly develop, deploy, adapt and maintain AI across different environments in the enterprise. Given the engineering complexity and the demand for shorter time to market, it is critical to develop less rigid AI engineering pipelines.

  • Automated, yet resilient, adaptable systems will require composable D&A architectures with application development to assemble intelligent decision-making solutions.

  • Decision making is a core capability, and it is becoming more complex. Decisions are becoming more connected, more contextual and more continuous. Hence, D&A leaders need to reengineer decision making enabled by adaptive systems in the future.


  • Adaptive AI systems will require processes to be reengineered for automated decision-making. Increased automation will, in turn, require business stakeholders to ensure the ethical use of AI for compliance and regulations.

  • Adaptive AI systems, while enabling new ways of doing business, and by leveraging generative AI capabilities, will result in the creation of new business models, products, services and channels.

  • While adapting to context, real-time changes fostered by adaptive capabilities will promote new collaboration between organizations; this is how adaptive AI systems will enable cross-organizational change. It is possible to only change a few internal functions using adaptive AI systems, but that would amount to local optimization, which defeats the purpose.

  • The bringing together of the D&A, AI and software engineering practices will be critical in building adaptive systems. AI engineering is going to play a critical role: building and operationalizing composable architectures.


Data and analytics leaders should:

  • Make it easier for business users to adopt AI and contribute to managing adaptive AI systems by incorporating explicit and measurable business indicators through operationalized systems and institutionalizing trust within the decisioning framework.

  • Maximize business value from ongoing AI initiatives by establishing AI engineering practices that streamline the data, model and implementation pipelines to standardize AI delivery processes.

  • Place limits on the amount of time a system takes to make a decision in response to disruptions, while focusing on the critical components of the decision-making framework. 
    The new decision-making process should encourage the use of flexible initial decisions that can be amended as more information is gathered about the environment.

Changes Since Last Year

As part of the Top Data and Analytics Trends, 2021, we introduced a trend called XOps (see Top Trends in Data and Analytics for 2021: XOps).

We also expanded on this trend in Gartner’s Top Strategic Trends research by evolving XOps into AI engineering (see Top Strategic Technology Trends for 2022: AI Engineering).

AI engineering is a discipline that streamlines the AI development and operationalization life cycle by leveraging DataOps, ModelOps and DevOps, paving the way to build automated adaptive AI systems.

Over the past 10 years, AI-based systems have been built for efficiency and autonomy, but operationalization has remained brittle, even if AI engineering practices are on the rise.

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