A Call To Action: AI Deployment In Government

I need your help. If you have experience, thoughts, answers relevant to the below questions, please reach out and let’s chat: henrygrunzweig@gmail.com

Because we’re in the age of AI, instead of an article, I’m writing a prompt.

Who You Are: An expert at large enterprise AI deployment; familiar with all the complexities of people, process, and systems that slow deployment, but with unrelenting optimism that AI offers a chance to skip past decades of sluggish technical adoption and upkeep.

Your Mission: Help me address real state capacity issues by suggesting an internal AI deployment strategy for a hypothetical government agency.

• In Scope:

  • Internal productivity improvement

• Out Of Scope:

  • Policy generation

  • Constituent services (chatbots can be done well and not so well – but not my focus)

  • Non-tech considerations (procurement and hiring can be tough – but let’s paint the skyline before designing the bricks)

Capabilities: We have simplified AI capability to three non-exclusive stages of adoption maturity; feel free to tell me there are better approaches or a more expansive set of options.

• Basic Large Language Model: Source a standard Enterprise LLM with suite of GenAI capabilities and offer basic training on how to use it **Hypothetical org may or may not be here

• Specialized AI Tools: Deploy new tools that augment specific business processes without significant system customization (e.g., Cursor for coding, Glean for file search)

• Custom AI Tools: Explore opportunities for customized, multi-step automation and AI-driven decision support across systems and workflows.

Considerations: As with most large organizations, our hypothetical org has meaningful barriers to AI adoption; some may be unique to government.

• Lots of data stored across many systems, both on-prem and cloud

• Complex file storage system spanning multiple storage locales

• Incentivizing employees to learn is appropriate; demanding uptake without support is not

• Regulatory and privacy concerns are of the utmost importance

Questions: We think these are the key questions, but feel free to tell me differently – or push for more information to inform answers.

• How have other organizations approached AI deployment?

  • What is the right way to think about AI capabilities?

  • What are common pitfalls and lessons learned in AI rollout?

• How does one prioritize top AI use cases?

  • Cross-organizational vs. function-specific use cases

  • Prioritization scorecard e.g., attractiveness (workload intensity, repeatable process, mission importance) vs. ease of implementation (staff interest, uniqueness of work)

• What are common models for tackling AI deployment?

  • Standing up deployment team(s) for intensive process-mapping and AI solutioning

  • Self-service enablement through curated playbooks, templates, and guidance materials

  • Train-the-trainer or internal champion programs

  • External consultants support intensive deployments

• What resourcing (staffing, skill sets, budget, timelines) are appropriate to support AI deployment?

  • How does this differ by deployment model?

• What other work must the org do in parallel to enable AI?

And the most important question

How do we make AI deployment scalable? Is intensive workflow and process mapping charted to AI capabilities the only way to maximize productivity (and is that really the existential AI question)?

Closing thoughts

• My commitment to you: Share a follow-on article summarizing my learnings.

• Please note: None of my words or views represent any official government or agency position.