headshot of Terri Musick

Teri Musick


Artificial intelligence, if agencies implement it strategically, could have a profound influence on how government agencies operate and interact with their user communities. Indeed, AI is already having an impact where agencies have begun to leverage its capabilities. 

AI is driving changes in hiring practices, work operations and the nature of government work itself, as agencies adjust processes to gain the efficiencies AI can bring.

AI may be one of the most paradigm-shifting technologies that federal agencies have encountered, and its rapid advance no doubt stirs mixed emotions for agency technology leaders. The fear of missing out on potential, balanced against the risk profile of a new and still evolving technology, can create some restless nights. 

Agile techniques can contribute to addressing the challenge of defining and implementing responsible AI in government operations. These techniques help mitigate risks by fostering a fail-fast and feedback-driven mindset, reducing the potential for bias and enabling the responsible use of AI.

However, the AI/Agile intersection works in both directions. As Agile can enable safer, mission-driven AI implementation, so AI can make Agile methodologies even more powerful and nimble. 

Responsible AI in government: Challenges and best practices

When considering the adoption of AI, it is important to prioritize mission-driven reasons. While AI can be powerful, it is not a universal solution. Therefore, consider use cases carefully in order to implement AI only where it is appropriate and likely to be most effective.  

When exploring key considerations and best practices for responsibly using AI in government operations and ensuring transparency and accountability, you have several factors to take into account. These include:

  • Transparency and explainability: Provide clear explanations of how AI decisions are made and ensuring that the decision-making process is understandable and auditable. 
  • Accountability and oversight: Define roles and responsibilities, implement appropriate governance structures and conduct regular audits.
  • Data duality and bias mitigation: Ensure that the data used to train AI models is of high quality, unbiased and representative of the population it serves. 
  • Human-centric design: Incorporate human-centric design principles to ensure that AI technologies are developed and deployed in ways that enhance public trust and deliver positive outcomes. AI should never replace the human aspect of the mission.
  • Collaboration and partnerships: Collaboration between government agencies, academia, industry and the public is essential for responsible AI implementation. 

Agile practices come into play as agencies need fast learning cycles in order to adapt and learn. Agile provides iterative practices; breaking AI solutions into small, modular components and manageable tasks enables you to continuously improve your AI solutions, with reduced risk. Should a component fail, the entire system or solution is not impacted. Agile’s continuous and early feedback loops allow stakeholders to provide input, identify potential biases or issues and suggest improvements early in the development cycle, thus reducing risk of deploying an unaccepted or biased solution. This feedback-driven approach ensures ongoing refinement, aligns AI systems with ethical guidelines and enhances transparency and accountability.

Agile practices provide the fast learning cycles that responsible AI requires and enables you to quickly adapt to changing circumstances and priorities, while focusing on mission success. 

Agile with an AI assist

Agile practices may themselves be an apt use case for AI. With minimal business process re-engineering, AI can help by automating repetitive tasks, enhancing data analysis for better data-driven decision making and forecasting potential issues or opportunities in Agile projects. 

Moreover, AI can provide feedback, recommendations and insights based on data analysis of Agile teams. By integrating AI-powered learning mechanisms into Agile practices, government agencies can increase efficiencies and productivity over time. This continuous learning cycle enables Agile teams to adapt to changing priorities, adopt best practices and drive innovation. 

Like any AI implementation, however, you have to strategically augment Agile practices with AI. Poorly applied, AI can open security risks and introduce errors. Here are some recommendations for successfully integrating AI into Agile: 

  • Remember the human factor: A powerful technology like AI can be a source of anxiety for employees. Incorporate change management and continuous learning and education so that your workforce learns how to navigate the complexities of the AI systems. 
  • Invest in AI training and skill development: Provide training and resources to Agile teams to build their AI capabilities and expertise. Encourage cross-functional collaboration between data scientists, AI specialists, and Agile practitioners to foster knowledge sharing and innovation. 
  • Establish clear objectives and key performance indicators: Set measurable goals to track the impact of AI augmentation on agility, efficiency, and service delivery outcomes. 
  • Ensure data quality and governance: Establish robust data quality standards, governance frameworks, and privacy protocols to ensure the ethical and responsible use of AI in Agile processes. Prioritize data security, confidentiality, and compliance with regulatory requirements. 
  • Promote stakeholder engagement: Solicit feedback, gather insights, and involve stakeholders in the co-creation of AI-driven solutions to enhance user satisfaction and service quality. AI does not replace stakeholder, user or client engagement.
  • Iterate and adapt continuously: Regularly review, assess, and optimize AI algorithms, models, and processes based on feedback, performance data, and changing requirements to drive continuous improvement and innovation.

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About this author

headshot of Terri Musick

Teri Musick


Teri Musick is a SAFe® Certified Program Consultant (SPC) and Agile SME with over 15 years the serving the federal government sector, 10+ years of direct Agile project experience and 25+ years leadership experience in the IT and telecommunications industries.