Three strategies to improve AI performance within government agencies
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Clear objectives, accessible data, and modern infrastructure are essential for agencies to maximize returns on AI investments, writes Rob Carey of Cloudera.
Artificial intelligence isn’t just a boon for the private sector; the public sector can also leverage AI to augment human capabilities, improve efficiency delivering citizen services, and increase productivity in novel ways to support its missions.
In fact, many government agencies are already tapping into AI for a variety of use cases. Cybersecurity, logistics management, supply chain management, financial management, personnel management and a host of compliance topics just to name a few.
From helping military leaders maintain complex machinery and cybersecurity units to identifying cyber threats in real-time, AI enables teams to analyze big data faster and more accurately to support decision-making.
And when in combination with trusted data, it can generate significant value for government agencies—including cutting costs. Whether that be automating administrative tasks or optimizing operations and logistics, AI can expand the impact of every taxpayer dollar.
The problem, however, is that AI deployments in the public sector often fail to generate positive return on investment or encouraging results.
AI isn’t a plug-and-play solution that works in every context without risk.
Successful deployment hinges on multiple foundational components, starting with the trustworthy underlying data that AI tools use. Teams must also have clear direction on how they’ll deploy and fine tune their AI solutions, and many government organizations struggle to implement the modern IT infrastructure needed for long-term AI operations.
With the new administration putting a strong emphasis on AI based innovation, like the Stargate initiative, government agencies should be prepared to fast-track deployments and illustrate their ability to leverage the technology to deliver value.
With this context in mind, below are three foundational components government agencies should implement to successfully accelerate AI deployment.
Align on Clear Problems
Before adopting any AI solution, teams should agree on a specific problem or set of problems they are trying to solve. While AI technology is advancing at an incredible rate, it’s not ready to tackle abstract or broad issues; it needs guidance. Even AI agents need pre-defined goals to be effective.
For agencies with less AI experience, starting smaller is better. Implement an AI chatbot on a website or a virtual assistant to route support calls.
Those with more developed AI or machine learning capabilities can build predictive models for data analytics or build an AI that can retrieve data in existing documentation or contracts.
The tighter the use case, the easier it is to measure success and prove the value of an AI solution. This helps build confidence within the organization that are useful for larger AI deployments.
Think Through Data Accessibility
In addition to having clear objectives in mind, agencies need reliable and accurate data to ensure AI systems function effectively. If the data fed into an AI model is biased or incorrect, the outputs will be as well.
The location and structure of data play a crucial role in determining its trustworthiness. Siloed architectures, where datasets are isolated and cross-validation is limited, create challenges in consistency, security (who has access to this data), and governance – making them particularly prone to hosting untrustworthy data. This is a familiar problem in government agencies as data is often siloed or across different environments, which can often make or break successful AI deployments.
Because government programs are typically funded individually, data management and data infrastructure are set up or adapted for discrete projects rather than the organization as a whole, making data accessibility a major hurdle.
Many government agencies lack a holistic or streamlined approach to data operations, which, coupled with strict access-based controls and governance policies, makes it hard for different departments to share useful information. While these safeguards are important, disparate data sources can hinder AI progress.
To improve AI deployments, government agencies need to be able to access and apply the vast amounts of rich structured, semi-structured, and unstructured data at their disposal.
Invest in Proper Data Architecture
The right implementation of modern data architecture also has great impact on the success of AI deployments.
Governmental bodies can’t harness the power of AI with legacy technology. Agencies must be able to collect and store large volumes of data quickly, process it efficiently, and then integrate it into existing analytics workloads or AI applications. On top of that, being able to secure data every step of the way and monitor the integrity of all data infrastructure involved is critical.
Without these capabilities, AI tools and models degrade over time. This is what eats into the ROI and overall performance of AI initiatives in governmental organizations. Fortunately, modern hybrid architecture, like data lakehouse technologies, provides much of the enterprise data infrastructure needed for AI.
An open data lakehouse combines the flexible and secure storage of data lakes with the querying power of a data warehouse. These platforms can store different types of data with highly nuanced access controls while enabling complex analyses. For those with more complicated analytics or AI use cases, data lakehouses can also serve data up to other data infrastructure. Implementing a data lakehouse ais a crucial step in ensuring strong AI performance.
In conclusion, AI isn’t a silver bullet for all government challenges and solutions, despite growing pressure to implement it to boost efficiency and productivity. 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.