The time has come for agencies to apply AI consistently and strategically across the enterprise and maximize their ability to be data-driven organizations.
Putting Data and AI to Work
The following strategies can help lay a secure, scalable, and reliable foundation for data and AI initiatives.
Align AI with your Goals
Avoid ad hoc purchases that meet a one-time need. Determine where AI and machine learning (ML) can have maximum impact on critical business problems and overall goals. Then prioritize long- and short-term investments around those areas.
“The most successful solutions for AI/ML are where the outcomes of those solutions are intimately tied to a department’s key strategic goals,” says Ajali Sen, innovation architect and AI strategy senior manager for Accenture.
Look for solutions with pre-built use cases that can be customized to your organization’s unique data and functions. Start with a pilot to fine-tune the solution, prove its business value, and train AI models — and have a plan to scale the pilot across the enterprise.
Establish Data Governance
If data is not properly managed, team members can’t use it, AI models will be faulty, and an organization might unknowingly violate ethical or regulatory standards.
Enterprise-level data governance addresses the entire lifecycle of data management. It includes processes, technology, and a network of data stewards to make sure data entry, use, sharing, storage, security, and more are managed uniformly across the enterprise.
Governance should incorporate procurement and ethics policies related to responsible, equitable, and transparent AI use.
“The adoption curve is extremely steep with this technology, so government organizations must set up governance quickly,” says Dan Boxwell, health and public service managing director for North America at Accenture. “Otherwise, use and adoption will rapidly outpace the organization’s ability to be clear with their teams and their employees about how the technology will be used.”
Use Cloud Tools
Leading cloud platforms provide tools that can tailor solutions to your organization’s data and AI needs. They are scalable, agile, and interoperable across multiple cloud and hybrid solutions. Foundational platform tools include:
- Extract/transform/load (ETL). Converts data at scale. ETL solutions that run on a fully managed serverless environment make it easier to discover, prepare, move, and integrate data from multiple sources for analytics, ML, and application development.
- Data lake. Enables cost-effective storage and management of high volumes of structured data (e.g., rows and columns), unstructured data (e.g., emails and documents), and binary data (e.g., images and videos) in a centralized repository.
- Data warehouse. Provides extremely high-performance data processing and querying. Advanced solutions have built-in tools to deliver real-time and predictive analytics.
- Interactive query service. Allows teams to easily analyze structured, unstructured, and semi-structured data on Amazon Simple Storage Service (S3) data object buckets.
Start now to build a strong technical bench that spans AI/ML, data analytics, and other roles. The market for niche data science and AI skills sets is very competitive, and upskilling in-house staff takes time.
To build skills and reduce the data science team’s workload:
- Write job descriptions for data science skills and budget for new roles.
- Identify needed skills and staff who are eager to be pioneers. Train them first so they can help evangelize AI and support others as they learn.
- Provide opportunities to practice new skills under supervision.
- Use AI and automation to offload mundane tasks and support data science teams in doing higher-value, more satisfying work.
- Partner with experts who can work alongside in-house teams to fill skills gaps, provide oversight as workers learn new skills, and develop AI/ML solutions that support the data science team.
Find Expertise to Get Started
State and local governments are in a period of intense exploration and rapid expansion of data and AI. Few organizations have the internal resources to plan and implement a thorough data and AI approach on their own. To succeed, turn to resources such as Amazon Marketplace to find proven consultants and AI solution vendors.
