Government agencies lag well behind other sectors in the adoption of generative artificial intelligence (GenAI), however, 60% of government respondents believe GenAI will drive innovation – and those that have begun using it are already seeing improvements in employee satisfaction, compliance, operational costs, and time savings.
This is according to a new global study – Your Journey to a GenAI Future: A Strategic Path to Success for Government – conducted by SAS and Coleman Parkes Research.
Despite trailing other sectors by 10% (44% vs 54%) in the current use of GenAI, the success of these early adopter agencies suggests enormous potential for the technology. Those benefits could arrive soon, with 84% of government decision-makers saying their organisations are planning to invest in GenAI in the next fiscal year – and 91% of those respondents already having dedicated GenAI budget.
“While they haven’t been first to adopt GenAI, government agencies are poised to increase productivity and transform citizen services with this technology,” says Grant Brooks, vice-president of US Public Sector and Healthcare at SAS.
“Deploying AI in a measured and responsible manner is crucial, but when properly planned and governed we can be confident that GenAI will bring significant value to our nation’s citizens and communities. We’ve partnered with government agencies through every major technological breakthrough of the last half century and we’re excited to see what we can do together with GenAI.”
According to the survey, all sectors shared top concerns about data privacy, data security and AI governance. However, government respondents had larger concerns (52%) about cultural resistance to change compared to other concerns (46%) and believe compatibility with legacy systems could be a challenge.
Additionally, the promise of GenAI in government may be imperilled by inadequate regulatory preparedness and lack of understanding of GenAI, relative to other industries. While many organisations have rushed to put GenAI guidance in place, only 52% of government organisations have a policy stating how employees are and are not allowed to use GenAI at work – compared to 61% across all sectors.
The study found that government agencies set aside less of their budgets for governance and monitoring than other sectors: 64% have allotted one-tenth or less of their GenAI budgets to governance and monitoring. Additionally, 50% of public sector respondents said they either don’t have a framework or that it’s ad hoc or informal, in comparison to 39% across the board.
GenAI regulation is moving quickly, so keeping up with it while unlocking the technology’s value is a universal challenge. However, government may be less prepared than other sectors, as 51% of government leaders say they’re fully or moderately prepared to comply with current and upcoming GenAI regulations compared to an average of 58% across all sectors.
Awareness is also a concern, as only 35% of public sector employees are familiar with their organisations’ adoption of GenAI – far less than the 46% average. These lagging indicators could be the result of a problem at the leadership level, as only 38% of senior government decision-makers say they understand GenAI and its impacts on business processes well or completely compared to 48% across all sectors.
There is good news though. Lower adoption rates in government corresponded with slightly lower policy preparedness and personal understanding, indicating that there is value in learning by doing. Government organisations that are implementing GenAI now are already seeing a range of benefits, in many cases outpacing other sectors. More government decision-makers than the cross-sector average say that implementing GenAI has improved employee experience and satisfaction (94%) or created operational cost and time savings (84%).
“It’s natural that government would have some reticence to adopt GenAI, but the public sector pioneers are already showing that the more it’s used, the more confidence will grow and innovation will accelerate,” says Jennifer Robinson, global government strategic advisor at SAS. “Large language models, digital twins, and synthetic data hold tremendous potential for government agencies once they have the processes and policies in place to maximise them.”
Synthetic data is artificial data that accurately mimics real data. It reproduces the same statistical properties, probabilities, patterns and characteristics of the real-world data set from which the synthetic data is trained and has been found to be as much as 99% statistically valid.
For example, synthetic data of simulated traffic flows could help transportation departments test a road improvement with what-if scenarios even if they only have a few months of traffic data. Since it can mimic sensitive data, it can be created to train and test a system that processes health records, student records, or tax information.
However, the study found that 32% of government decision-makers would not consider using synthetic data. This exceeds the mere 23% of respondents across industries who are averse to its use.
“Synthetic data is particularly relevant for government agencies that must follow strict data privacy regulations,” says Robinson. “Governments can use synthetic data for various purposes including research, testing, and analysis, while mitigating risks of violating privacy regulations or exposing sensitive information.”