Tom Greiner, senior managing director of Accenture’s health and public-service technology division, brings over three decades of experience implementing complex information technology systems for government agencies. In a comprehensive interview with Business Insider, Greiner discussed the evolving landscape of cloud computing and generative AI adoption in the public sector.
Cloud Technology Evolution: Greiner explains that many public-sector clients are transitioning from on-premise data centers to hybrid and multicloud environments. The COVID-19 pandemic accelerated this shift, with cost efficiency being a primary driver—cloud solutions for backup, recovery, and security logs prove significantly cheaper than traditional infrastructure. Federal agencies particularly embrace multicloud and hybrid-cloud environments, with some even experimenting with polycloud architectures where workloads span multiple cloud providers. Generative AI is further accelerating cloud adoption as agencies test different foundation models hosted across various platforms.
Security and Regulatory Concerns: Implementation challenges vary by region. European stakeholders prioritize data sovereignty, questioning where data resides and who can access it. In the United States, clients focus on cloud security capabilities, requesting managed SOCS services and active threat hunting. For sensitive data, Greiner describes implementing “moving-target defense"—cloud environments that operate for limited periods before relocating, limiting adversary reconnaissance opportunities. Regulatory confusion persists, particularly around Europe’s GDPR, where countries interpret requirements differently. Greiner notes the federal government’s reduced emphasis on the NIST Cybersecurity Framework has increased market friction and costs.
Generative AI Adoption: Demand for AI in the public sector remains high, though generative AI adoption shows both optimism and caution. 2023 focused on small experiments, while 2024 emphasized institutional frameworks. Organizations established Centers of Excellence to evaluate different models, provide responsible AI coaching, and share best practices. Unlike cloud’s initial “free-for-all” approach, generative AI implementation prioritizes fair and equitable deployment from the start.
Practical Applications: Greiner highlights successful implementations, including a chatbot for DC’s Department of Health using AWS Bedrock with retrieval-augmented-generation (RAG) technology. Recent projects incorporate knowledge graphs for improved contextualization. Public health represents a prime use case, where AI-powered conversational tools could handle 80% of patient prep questions, addressing critical nursing shortages. Budget constraints drive interest in automating mundane tasks and freeing staff for higher-value work.
Key Quotes
COVID was an accelerator for that. Dollars and cents is also a driver. There’s a need to treat the taxpayer dollar respectfully.
Tom Greiner explains the dual forces driving cloud adoption in the public sector—the pandemic’s urgent digitalization needs combined with fiscal responsibility to taxpayers, highlighting how crisis and economics converge to accelerate technology transformation in government.
With generative AI, I think there’s both optimism and caution. 2023 was a year of small experiments and exploration. In 2024, we needed to think about it institutionally.
Greiner describes the maturation of generative AI adoption in government, showing how agencies moved from ad-hoc experimentation to systematic, organization-wide frameworks—a critical evolution that distinguishes sustainable AI integration from temporary pilot projects.
Cloud started as a bit of a free-for-all, and then common infrastructures and security capabilities came later. With generative AI, organizations are trying to solve citizens’ problems but also to start with direction on how to do it in a fair and equitable way.
This quote reveals how lessons learned from cloud adoption are shaping generative AI implementation, with agencies prioritizing responsible AI frameworks upfront rather than retrofitting governance later—a significant shift in technology adoption strategy.
Helping someone coming in for a surgery to ask all the questions they would want to ask in a conversational sort of way could probably off-load 80% of the patient prep and free up a nurse for other tasks.
Greiner provides a concrete example of AI’s workforce augmentation potential in healthcare, demonstrating how conversational AI can address critical staffing shortages while improving patient experience—a win-win scenario for resource-constrained public health systems.
Our Take
Greiner’s insights reveal a public sector at an AI inflection point, balancing innovation with accountability. His emphasis on responsible AI frameworks from day one contrasts sharply with the tech industry’s “move fast and break things” mentality, suggesting government implementations may ultimately prove more sustainable and trustworthy. The focus on hybrid and multicloud architectures reflects sophisticated infrastructure thinking that acknowledges no single vendor solution fits all government needs—a pragmatic approach private enterprises should emulate.
Particularly noteworthy is the Centers of Excellence model for generative AI, which facilitates knowledge sharing across agencies while maintaining security and ethical standards. This collaborative approach could accelerate adoption while minimizing redundant experimentation. The healthcare applications Greiner describes address real workforce crises, positioning AI as a practical solution rather than futuristic speculation. As public-sector AI adoption matures, these implementations will establish precedents for transparency, fairness, and citizen-centric design that could influence broader AI governance frameworks.
Why This Matters
This interview provides crucial insights into how government agencies—often slower to adopt new technologies—are embracing cloud computing and generative AI. The public sector’s cautious yet progressive approach offers valuable lessons for AI implementation across industries. Unlike private companies driven by competitive pressure, government agencies prioritize responsible AI frameworks and equitable deployment from the outset, potentially establishing best practices for ethical AI use.
The emphasis on hybrid and multicloud strategies reflects the complex security and sovereignty requirements unique to government work, influencing how cloud providers design their offerings. Greiner’s observation that agencies are pursuing “quick wins” with AI—automating tasks employees dislike—demonstrates a pragmatic adoption strategy that balances innovation with risk management.
The nursing shortage example illustrates AI’s potential to address critical workforce challenges without replacing human workers, instead augmenting their capabilities. As public-sector AI adoption accelerates, these implementations will impact millions of citizens’ interactions with government services, making this a significant indicator of AI’s mainstream integration into society. The establishment of Centers of Excellence and responsible AI frameworks may become templates for other sectors navigating generative AI deployment.
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