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AI in the public sector: A goal-oriented journey

The public sector has a unique opportunity to harness the power of artificial intelligence (AI) for the benefit of citizens. Successful AI implementation isn't about adopting the latest technology. It's about a strategic, goal-oriented approach, recognizing that AI is a continuous journey, and it all begins with structured data.

Structured data: The foundation of AI

The quality of AI is directly proportional to the quality of the data it lives on. Structured data is the bedrock upon which successful AI is built. According to an MIT Sloan study, it is likely that 80-90% of your organization’s data is unstructured. This unstructured data obscures insights between redundant, obsolete or trivial data, uncategorized and essentially unusable. Public sector organizations must prioritize structuring their data, breaking down siloes and building a foundation of clean, contextualized, relevant data.

Without high-quality, contextualized and structured data, even the most advanced AI algorithms will produce flawed or misleading results. Investing in data structure is essential for any public sector organization looking to leverage AI.

Structured vs unstructured - what’s the difference?

Unstructured data

Definition: Qualitative data found on paper, in siloed digital systems, in audio, photo, or video files, etc.

Structured data

Definition: Quantitative data featuring metadata, numbers, and searchable values.

Unstructured data

Format: Inconsistent and highly varied even among similar file types

Structured data

Format: Consistent formats with meaningful context across all file types

Unstructured data

Search/retrieve: Hard to find without manual searching

Structured data

Search/retrieve: Easily searchable within seconds, as all data lives within a centralized database

Unstructured data

Storage: Requires significant storage space both physically and digitally

Structured data

Storage: Requires zero physical footprint and optimized digital storage space in databases

Defining the destination

AI can be an effective tool, but its effectiveness depends on how it's used. In the public sector, this means clearly defining the problems you're trying to solve and the outcomes you want to achieve.

Before diving into AI solutions, the organization needs to answer basic questions: What are our biggest challenges? What specific improvements do we want to see? This goal-oriented approach ensures that AI investments are strategic and deliver real value.

What specific goals will AI help you reach?

The AI journey: A continuous path

AI is not a "one and done" project. It's an ongoing journey of learning, refinement, and adaptation. As new data becomes available and circumstances change, AI models need to be continuously evaluated and updated. This process, involving testing, feedback, and improvement, is crucial for ensuring that AI remains relevant and effective over time. Embrace the journey mindset, and you'll be well-positioned for long-term success.

Taking the first steps

Embarking on the AI journey can seem overwhelming, but it doesn't have to be. Start small, focus on achievable goals, and build from there. Consider these initial steps:

Identify a pilot project

Choose a specific problem area where AI could make a tangible difference.

Assess your data landscape

Evaluate the quality and accessibility of your data.

Explore potential solutions

Research different AI technologies and providers.

Build internal expertise

Develop the skills and knowledge needed to manage and maintain AI systems.

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Unlock the transformative potential of AI

With a clear vision, a commitment to continuous improvement, and a focus on structured data, public sector organizations can unlock the transformative potential of AI and create a better future for the communities they serve.

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