Artificial intelligence is everywhere in government right now. Agencies are piloting chatbots, testing generative AI tools, and encouraging employees to explore new ways of working. The excitement is understandable. These tools have the potential to save time, improve service delivery and reduce repetitive tasks.
But there is an important question we should be asking:
Are we transforming the way we work, or are we simply experimenting with new technology?
There is a difference.
Access Does Not Equal Adoption
Many organizations have made AI tools available to their workforce. What they have not always done is define how those tools fit into daily operations and what is free range data to use in the AI tools.
Employees are often left to figure out:
- Which tool should I use?
- What tasks are appropriate for AI? What are the use standards and use cases set for AI?
- How do I know if it is improving my work? How do I measure improvement?
- How should I share successful practices with my team?
Without clear expectations, AI becomes another application on the desktop rather than a meaningful part of the workflow. It reminds me of the lost hope of the Microsoft full office suite.
Technology purchase and implementation is not a challenge solely in government. It is something many organizations are experiencing as AI capabilities evolve faster than implementation strategies.
Lessons From Technology Implementations
Earlier in my career, I worked on enterprise software implementations. When a new system was introduced, the software itself was only one part of the project. We also spent time understanding how people actually completed their work.
We conducted interviews, observed workflows, gathered feedback, identified bottlenecks, and measured system performance under real operating conditions. Those observations helped shape future releases and improve the product over time.
Today’s AI rollout feels stuck in the beginning stages. Employees are discovering what works through daily use, while organizations are learning which tasks benefit most from AI assistance. The opportunity now is to take those lessons and move beyond experimentation.
Generative AI Versus Functional AI
Generative AI is excellent at helping people create content. It can summarize reports and newsletters, draft emails, organize meeting notes, and answer questions. Those are valuable capabilities that can aggregate information and cut time in vapid tasks.
Functional AI goes a step further. Instead of simply helping someone complete an individual task, functional AI improves how work moves through an organization.
For example, imagine an agency that receives hundreds of public inquiries each week. Generative AI might help staff draft responses more quickly but functional AI could help classify incoming requests, route them to the appropriate office, identify recurring issues, and provide leaders with data to improve future services.
One supports productivity while the other improves the entire process. The best part is that functional AI can incorporate existing tech tool suites such as Microsoft 365.
Building AI Into the Workflow
The strongest AI implementations begin with operations, not technology. Before introducing another tool, agencies can ask a few simple questions:
- Which tasks consume the most staff time?
- Where do employees experience repetitive work?
- Which processes create delays for customers or stakeholders?
- How will we measure whether AI is actually improving outcomes?
All of the questions center around resource management and utilization which should be easy to answer if the agency was astute at that. If not, this is a good reason to begin to learn. These conversations will help ensure AI supports existing missions rather than creating additional work.
Governance Matters
Successful AI adoption also depends on governance.
Governance simply means having agreed-upon guidance for how AI should be used, who is responsible for oversight, and how success will be measured. This should not be punted to the employees or the designated SME out of a department. Employees should not have to guess when AI is appropriate or develop their own standards independently.
Clear guidance creates consistency, builds confidence, and helps organizations scale successful practices across teams.
Moving From Curiosity to Capability
Government has always adapted to new technologies. AI is no different.
The agencies that gain the greatest value will not necessarily be those with the most AI tools. They will be the ones that intentionally connect technology with people, processes, purpose and measurable outcomes.
Experimentation is an important first step. Every innovation begins with learning.
The next step is making AI part of the way work gets done.
When agencies focus on workflow, governance, and operational improvement alongside technology, AI becomes more than a productivity tool. It becomes a practical way to strengthen public service for employees and the communities they serve.
Ája Hardy brings more than 20 years of experience transforming the healthcare landscape by bridging technology, innovative business solutions, and effective strategy. She has a proven history of driving growth and optimizing operations across the medical device, pharmaceutical, and government sectors with leading organizations like Deloitte, Huron Consulting, and Cerner/Oracle Health. A natural “new venture whisperer,” Ája served as a FUSE Executive Fellow for the City of Cleveland, where she forged key economic development partnerships with Google, Kiva, and Square. This success led to the launch of a startup accelerator with VC firm gener8tor, which she directed. Today, as a TAP Advisor at the FDA, she continues to shape the future of healthcare by focusing on novel technology innovation, policy, and strategy.
Leave a Reply
You must be logged in to post a comment.