Boosting AI Success in Government: Key Strategies for Agencies
Clear objectives, readily accessible data, and a modern infrastructure are essential for government agencies to maximize their returns on investment in artificial intelligence, writes Rob Carey, President of Cloudera Government Solutions.
Artificial intelligence offers a powerful opportunity for the public sector, going beyond the private sector to augment human capabilities, improve the efficiency of citizen services, and boost productivity in new and innovative ways aligned with its missions.
Many government agencies are already tapping into AI across various areas including cybersecurity, logistics, supply chain management, financial management, personnel management, and regulatory compliance, just to name a few. AI enables them to manage complex machinery, improve cybersecurity units and identify cyber threats in real-time by helping teams analyze big data faster and more accurately.
When combined with reliable data, AI delivers significant value—including cost savings—by automating administrative tasks and optimizing operations and logistics, thereby broadening the impact of every taxpayer dollar spent.
The challenge, however, is that AI deployments in the public sector often fail to meet investment expectations and generate the desired results. AI is not a “plug-and-play” solution and a successful implementation relies on several key components, beginning with the trustworthy data that AI tools utilize. Additionally, teams need a clear deployment plan, and many government organizations are struggling to implement the modern IT infrastructure needed for sustainable AI operations.
With the new administration’s focus on AI-based innovation, government agencies should fast-track their deployments and demonstrate their ability to leverage technology to deliver value. With this in mind, here are three essential components:
Align on Clear Problems
Before implementing any AI solution, agencies should agree on specific problems they are trying to solve. AI technology needs clear direction. Even AI systems need pre-defined goals.
Agencies that are new to AI may wish to start with a chatbot for their website or a virtual assistant to route support calls. Those with advanced AI capabilities may develop predictive models for data analytics or build an AI that can retrieve relevant data from documentation or contracts.
The more specific an AI use case is, the easier it becomes to measure its success and prove its value, which builds confidence within the organization for larger AI deployments.
Think Through Data Accessibility
In addition to having clear objectives, ensure agencies have accurate and reliable data to ensure AI systems’ effectiveness.
The location and structure of the data also influence its trustworthiness. Having isolated datasets creates challenges in consistency, security, and governance. This problem is familiar to government agencies because data is often stored across different environments, which can often make or break successful AI deployments.
Since government programs are typically funded individually, data management and infrastructure often adapt to discrete projects rather than the organization as a whole, making data accessibility a major hurdle.
Many government agencies do not have a streamlined approach to data operations, and strict access-based controls and governance policies make it hard for different departments to share useful information. Disparate data sources can hinder AI’s potential.
To improve AI deployments, agencies need to access and apply the vast amounts of structured, semi-structured, and unstructured data at their disposal.
Invest in Proper Data Architecture
Modern data architecture greatly impacts the success of AI deployments. Governmental agencies can not harness the power of AI with legacy technology. Agencies must collect and store large volumes of data rapidly, efficiently process it, and integrate it into existing analytics workloads or AI applications. In addition, securing data every step of the way and monitoring the integrity of all data infrastructure is critical.
Without these capabilities, AI tools and models degrade over time, impacting the ROI and performance of governmental AI initiatives. Fortunately, modern hybrid architecture, like data lakehouse technologies, delivers much of the enterprise data infrastructure needed for AI.
An open data lakehouse combines the secure storage of data lakes with the querying power of a data warehouse. These platforms store different types of data with nuanced access controls and enable complex analyses. For complicated analytics or AI use cases, data lakehouses may supply data to other data infrastructure. Implementing a data lakehouse is crucial for strong AI performance.
In conclusion, AI isn’t a silver bullet for all government challenges. However, when applied with a specific objective in mind, and implemented using a modern architecture and accurate, trustworthy data, AI can transform government operations for the better.
Rob Carey is the president of Cloudera Government Solutions.
