Challenges for government adoption of AI
Blog: NASSCOM Official Blog
From transportation solutions to video-streaming applications, artificial intelligence (AI) permeates almost every aspect of our lives. This includes government, where AI is increasingly making an impact.
The public sector has a high potential for artificial intelligence (AI) to have a transformative impact. After all, governments have access to tremendous amounts of data, and government operations affect each of us in small and large ways every day. So far, AI adoption among government entities appears to be uneven and generally lags behind the private sector.
SAS conducted a survey of more than 300 executives across a variety of industries. It found a decidedly mixed picture of AI in government, likely owing to an environment that is often risk-averse, subject to myriad legislative hurdles and vast in its reach. The survey, published by SAS, Accenture Applied Intelligence and Intel and conducted by Forbes Insights, showed signs that we have reached the moment at which AI expands beyond discrete use cases and experiments into wider adoption in some agencies.
IBM estimated in 2017 that 90% of the world’s data had been created in the past two years. The problem is, our organizations, both public and private, were not created to handle and take advantage of this volume and variety of data. Most organizations have a very rudimentary understanding of their data assets (i.e. the data they hold and the infrastructure that holds that data) and trying to answer even basic questions such as how many databases exist within the organization, which database contains what information, or how data is collected in the first place, can be challenging. This is a significant problem given data is the fuel that powers modern AI solutions.
A parallel roadblock is that most organizations do not have data governance processes in place, such as established data owners, an enterprise data champion, such as a Chief Data Officer; tools for their employees to safely and efficiently access and take advantage of enterprise data, or practices to manage and ensure data privacy and integrity. Organizations that do not possess the capabilities to understand and manage their data cannot take advantage of AI.
Pursuing their missions every day, government agencies spend much of their time focused on operational issues. That time-consuming focus is required in government departments and offices that are held accountable for achieving clearly defined missions. If they fall short, the consequences can be devastating – for the citizens they serve, as well as for the government organization itself. Not to mention, in some cases, a leader’s career.
In that context, it’s easy to see how AI remains a second-tier priority for some government leaders who have operational roles. In the face of pressing requirements to deliver critical services, AI may appear to be a luxury that is just out of reach. This presents government leaders with a paradox. Many have no time to fully embrace AI due to everyday demands, but those AI advances could be instrumental in unlocking real, measurable operational improvements that have the effect of reducing strains on resources and giving them more time to fulfill their mission.
In addition, government personnel in non-technical roles, such as department directors, policy-makers, and procurement officials do not always have enough understanding about data and AI. This includes technical knowledge and most importantly knowledge of the legal and ethical implications of using vast amounts of data, where the main concern is privacy. This makes it difficult for them to feel comfortable investing in the technology, or be aware of existing laws that have a direct effect on AI projects, such as data and privacy legislation.
Embarking on AI projects without having a full understanding of applicable local laws threatens constituents’ rights, such as privacy, and the government’s long-term ability to deploy AI with full public support. This can have as large an effect on AI procurement as a lack of technical AI skills.
Government agencies that do have AI in-house knowledge face an added complexity: lack of communication. Silos between functions make it hard for AI resources and their colleagues, such as policy-makers, to have frequent touchpoints and take full advantage of each other’s knowledge.
However, even though AI implementation within most segments of the public sector is lagging behind their private-sector counterparts, this may present an unexpected opportunity. As other industries have experimented, failed, learned, and progressed in their efforts with AI, government leaders can benefit from the insights and best practices gleaned from these experiences. That presents a significant opportunity for the government, suggesting eventual broader adoption.
Regardless of its trajectory, it seems clear that AI will expand among government entities as the capabilities become more powerful, and leaders hone their ability to deploy them. While what’s next will vary from agency to agency based on their operating environment, it’s a safe bet that the factors that set successful AI adopters apart, as found in the research, will figure prominently among public sector organizations.
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