Gartner hype cycle for digital government services identifies 6 technologies to have transformational benefit within 5 years

There are six technologies that will have transformational benefits within five years for digital government services, according to Gartner, Inc. These include digital employee experience (DEX), AI code assistants, generative AI (GenAI), generative design AI, predictive analytics in government and workstyle analytics.

“Delivering services digitally continues to be a priority in government transformation plans,” says Daniel Snyder, Sr Director Analyst, KI Leader. “These technologies will help government CIOs bridge the gap between digital innovation goals and executing against strategy objectives. Collectively, they will be a crucial element for executives in aligning technology innovation to mission outcomes to keep pace with growing citizen expectations.”

This Hype Cycle for Digital Government Services presents innovative and emerging technologies and practices essential for governments to implement and mature digital transformation initiatives that ultimately lead to improved business outcomes.

Digital employee experience

Through 2027, multidisciplinary digital workplace teams that blend business and technology roles will be 50% more likely to deliver positive outcomes than those formed by IT alone.

DEX strategies use experience best practices, such as personas, journey mapping and voice of the employee, to boost digital dexterity, attract and retain valuable talent, and help employees deliver against business outcomes. A holistic, coordinated approach to DEX across IT and non-IT partners can minimize digital friction, reduce silos and maximize workforce digital dexterity and well-being. Workers who say that their relationship with IT involves more than break or fix support are twice as likely to recommend their organization as a good place to work.

“Ultimately, a DEX strategy empowers government employees to adopt new ways of working by minimizing technology friction, supporting meaningful adoption, applying usage and performance metrics, and making ongoing adjustments,” said Snyder.

AI code assistants

AI code assistants, integrated into developer tools, use pretrained models to interact with software developers through natural language and code snippets. They generate, analyze, debug, fix, refactor code, create documentation and translate code between languages. These assistants can be customized to an organization’s specific code base and documentation, which is key for government entities from the local to federal levels dependent on specific data to their region.

“AI code assistants, unlike their predecessors, enhance developer velocity and expedite problem-solving by explaining and debugging code issues,” said Snyder. Because of this, Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants, up from less than 10% in early 2023.

GenAI

Business focus is shifting from excitement around foundation models to use cases that drive ROI. Most GenAI implementations are currently low-risk and internal. With the rapid progress of productivity tools and AI governance practices, organizations will be deploying GenAI for more critical use cases in industry verticals and scientific discovery. In the longer term, GenAI-enabled conversational interfaces will facilitate technology commercialization, democratizing AI and other technologies.

Generative design AI

Generative design AI is a technology that utilizes AI to autonomously generate design options based on parameters and constraints specified by the user. This AI uses algorithms to iterate rapidly through numerous variations, optimizing designs to achieve the best possible outcomes while adhering to predefined goals, which are often a key component of government services, and improving efficiency in the design process.

The technology already exists as feature-level support for user experience designers and front-end developers, such as intelligent design suggestions, and will transition rapidly to full digital product design and front-end development capabilities.

Predictive analytics in government

Predictive analytics in government exploits machine learning, modeling and simulation. It uses internal and external data to inform public policy development, optimize government processes and improve real-time decision making. The responsible and ethical use of predictive analytics in government requires care and due diligence to limit the impact of biases inherent in all datasets.

Swift, accurate, safe decisions are critical to outcomes in all branches of government. Predictive approaches allow consequences of decisions to be considered ahead of time and enable plans to be adapted accurately as needed. This delivers better outcomes at lower risk than a reactive approach. To maintain the public’s trust and ensure accountability, it should be deployed transparently.

Workstyle analytics

Through 2027, IT leaders who align digital workplace investments with current and desired levels of maturity and overall digital ambitions will reduce the waste associated with untimely and unsuitable activities by 50%.

Workstyle analytics (WSA) is a discipline that synthesizes IT, HR and business data about how employees work to understand and optimize the complex relationship between technology investments, employee experience and business outcomes.

To drive better business and workforce insights and outcomes, organizations must aggregate and analyze data to understand how technology investments impact employees and business results, as budgets and spending are foundational to digital government services.