Harnessing AI for internal communications in government
Agency leaders can use AI to tackle challenges head-on as both force multipliers to improve efficiency and effectiveness of IT communications, and as drivers of innovation that can unlock novel ways for non-technical employees to partner with you.
Artificial intelligence (AI) and machine learning (ML) can play a critical role in stakeholder communications, from rooting out misinformation to sending out critical alerts and monthly newsletters in dozens of languages. It can speed up data access, free up resources for more important tasks, and meet your various audiences where they are. But what happens when you aim this helpful tool at your internal communications, especially when it comes to IT and change management?
Let’s examine how generative AI and emerging AI-assisted tools have the potential to improve internal communications in your government agency, best practices for implementation, and some of the risks to look out for. We’ll start with some common challenges, and how, in the future, AI will help address them:
"By leveraging AI technologies, agencies can enhance their IT, internal communications, and change management capabilities, improve efficiency, and respond more effectively to mission challenges."
Bridging communications gaps between teams
In large government agencies, internal and external communication between IT staff and non-technical employees can be challenging. Often, IT staff are not equipped to articulate their work’s impact on non-technical employees. On the other side of the equation, non-technical employees have limited attention, and the channels to reach them are cluttered. How do we improve interactions and information sharing from one team to another?
How AI/ML can help:
- AI-powered Natural Language Processing (NLP) can sift through large volumes of text or technical data, extracting key information and summarizing reports, making it easier for staff to access relevant, contextual insights.
- When used for automated data analytics, AI algorithms can analyze complex datasets, identifying trends and anomalies, which can be crucial for timely decision-making.
- Beyond performance improvement, AI can drive innovation and lead to brand-new functionalities. For example, user-friendly, conversational chatbots have emerged as a new pattern for user interaction.
Employee information overload
From news, reports, and e-mails to social media, chats, and meeting invitations, sifting through the immense volume of information thrown at employees each day can be a challenge. Add on a layer of potential misinformation, and that challenge now becomes a risk. It’s not uncommon for employees to become overwhelmed or disengaged, which can ultimately impact productivity. This also leads to them missing out on information that is crucial or relevant to them. How do we add context and clarity to communication to help it cut through the clutter? This ensures each employee receives the most relevant information, eliminating information overload and keeping them engaged with what matters to them.
How AI/ML can help:
- AI-powered algorithms can detect patterns and anomalies in data, helping us identify potential instances of misinformation early on and enabling us to address false narratives swiftly before they gain traction.
Cross-team coordination and minimizing routine tasks
All too often, different departments and teams within federal agencies operate in their own silos, making it challenging to coordinate efforts, share information, or make unified decisions. Additionally, coordinating and curating data for internal comms can take away from mission-critical tasks.
How AI/ML can help:
- For crisis communication across teams, AI-powered automated messaging systems can quickly and easily disseminate crucial information and updates, ensuring that all relevant personnel receive timely and accurate communications.
- With AI-powered chatbots and virtual assistants, teams can facilitate internal communication by providing quick access to information, scheduling meetings, and routing inquiries to the appropriate departments.
- AI-enhanced collaborative platforms can integrate various legacy and new communication tools, enabling seamless sharing of data and real-time collaboration across teams.
- GenAI with access to curated, agency-specific data will automatically generate contextualized internal comms, like newsletters, announcements, and training materials. It can also ensure consistent messaging and allow communications teams to focus on delivery strategies versus delivery activities.
Knowledge retention and training
With inevitable staff turnover and/or the retirement of experienced personnel, public health agencies face an ongoing challenge of quickly and efficiently retaining and transferring knowledge. There are also challenges in manually keeping staff up to date with the latest effective practices and technologies.
How AI/ML can help:
- When used with a knowledge management system, AI can help create and maintain comprehensive knowledge bases, capturing existing institutional knowledge and making it accessible to new and existing employees.
- AI-powered knowledge bases will use natural language queries to provide accurate and relevant information about policies, procedures, and technical documentation to employees in a conversational manner.
- AI can also be used as an intelligent document retrieval system that can categorize and retrieve documents quickly and efficiently. This way, valuable information and institutional knowledge aren’t lost, and can be easily accessed when needed.
- Through personalized learning, AI can tailor training programs to individual learning styles and knowledge levels, providing personalized content and assessments to improve retention.
What to look out for
Now, while using AI for IT, internal communications, or change management can offer efficiency and improved data management, there are always risks to consider. Note that this is by no means a comprehensive list, just some common considerations.
First is data privacy and security, especially if AI systems process sensitive or embargoed data, which can be vulnerable to breaches or misuse if not properly protected. This can be addressed with robust encryption, access controls, and regular audits to safeguard data and ensure compliance.
From a change management standpoint, establishing credibility and trust is also crucial for agencies using AI to develop products and services, and gain buy-in from employees. One approach to building trust is adopting Explainable AI (XAI) that provides a greater, more transparent understanding of how the models work and gives your agency accountability and confidence in what teams produce. Another is sharing success stories of teams working with AI. By showcasing teams that are using AI, backed by evidence and success stories, your agency can build interest that will lead to greater adoption of the most successful use cases.
Next is inherent bias and fairness. Unfortunately, many AI models can inadvertently perpetuate or exacerbate biases present in the data they are trained on. This can lead to unfair treatment or misrepresentation of certain groups in internal communications and decision-making.
With a growing emphasis on the ethical use of AI, agencies must integrate responsible AI principles into the way they apply AI to use cases, including strategic communications. This includes being transparent about how AI is used, the data it processes, and the measures in place to protect privacy and ensure fairness.
"Agencies should develop and communicate clear policies on responsible AI use and involve stakeholders in discussions about ethical considerations."
Agencies should also adopt a holistic approach to risk management that considers not just cost, security, accuracy, and ROI for AI, but also qualitative socio-economic factors and potential impacts to civil rights for vulnerable communities. In the context of strategic communications work, AI applications must be not only beneficial, but ethically and socially responsible.
Additionally, the human element is critical—especially expertise in communications AND AI technology, to regularly evaluate AI models, and ensure that all models are using diverse and representative data sets. By having a diverse team in place during the development and oversight of AI systems, you can be sure that different perspectives are considered.
Speaking of the human element, there’s another risk: over-reliance on automation. By relying too heavily on AI for communication, a reduction in human oversight and critical thinking can occur. Neither AI nor effective communication is “set it and forget it.” This can be particularly risky in domains including public health and disaster management, where nuanced judgment and empathy are often needed. To stay in front of this particular risk, maintain a balance between automated and human-driven communications, ensure that critical decisions and sensitive communications are reviewed by human professionals, and provide training for staff on how to collaborate with AI tools properly and effectively.
Addressing these risks requires a careful and thoughtful approach, balancing the benefits of AI with the need for ethical and responsible usage, as well as human expertise.
Bringing AI expertise to mission delivery
At ICF, we see AI/ML as tools used to tackle challenges head-on as both force multipliers to improve efficiency and effectiveness of IT communications, and as drivers of innovation that can unlock novel ways for non-technical employees to partner with your agency.
For the last seven years, ICF has been actively managing innovation driven by AI/ML. And since 2022, with the rapid acceleration of generative AI and the broadening availability of AI-assisted tools, we have proactively adopted responsible AI principles and an AI readiness design framework. With it, we guide agencies through the conceptualization, strategy, and implementation of artificial intelligence and machine learning as part of both internal and external communications.
By leveraging AI technologies, agencies can enhance their IT, internal communications, and change management capabilities, improve efficiency, and respond more effectively to mission challenges.
Discover how we’re helping federal agencies use generative AI to advance outcomes.
