A number of pilot projects in recent years have proven the value of artificial intelligence (AI) in government and paved the way for broader adoption. At the same time, state and local governments are managing ever-increasing amounts of data to make more informed decisions and improve operations and services.
The time is now for government agencies to consistently and strategically apply AI across the enterprise to maximize their ability to become data-driven organizations.
Leverage data and AI
The strategies below will help you build a secure, scalable, and reliable foundation for your data and AI efforts.
Tailor AI to your goals
Avoid ad-hoc purchases that satisfy one-time needs. Determine where AI and machine learning (ML) can have the greatest impact on your business's key problems and overall goals. Then prioritize long-term and short-term investments around those areas.
“The most successful AI/ML solutions are those whose outcomes are closely tied to the department's key strategic objectives,” said Ajali Sen, Innovation Architect and Senior Manager, AI Strategy, Accenture. .
Look for solutions with pre-built use cases that can be customized to your organization's unique data and capabilities. Start with a pilot to fine-tune your solution, prove business value, and train your AI model, and plan to scale the pilot across your enterprise.
Establishing data governance
If data is not properly managed, team members may not be able to use it, AI models may be flawed, and organizations may unknowingly violate ethical and regulatory standards.
Enterprise-level data governance covers the entire data management lifecycle. This includes the processes, technology, and network of data controllers that ensure data entry, usage, sharing, storage, security, and more are managed uniformly across the enterprise.
Governance should incorporate procurement and ethics policies related to responsible, fair and transparent use of AI.
“The adoption curve for this technology is so steep that agencies need to establish governance quickly,” said Dan Boxwell, managing director of health and public services for North America at Accenture. Masu. “Otherwise, usage and adoption will outpace an organization’s ability to make clear to teams and employees how to use the technology.”
Use cloud tools
Leading cloud platforms offer tools that allow you to tailor solutions to your organization's data and AI needs. They are scalable, agile, and interoperable across multiple cloud and hybrid solutions. Basic platform tools include:
- Extract/Transform/Load (ETL). Transform data at scale. ETL solutions that run in a fully managed serverless environment make it easy 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 large amounts of structured data (such as rows and columns), unstructured data (such as emails and documents), and binary data (such as images and videos) in a central repository.
- data warehouse. Provides extremely high performance data processing and querying. Advanced solutions include tools that provide real-time predictive analytics.
- interactive query service. Easily enable your team to analyze structured, unstructured, and semi-structured data on your Amazon Simple Storage Service (S3) data object buckets.
Learn AI/ML skills
Start building a strong tech bench across AI/ML, data analytics, and other roles today. The market for niche data science and AI skill sets is highly competitive, and upskilling internal staff takes time.
To build skills and reduce the workload of your data science team:
- Create a job description for your data science skills and budget for your new role.
- Identify staff with the skills you need and a desire to be trailblazers. Train them first so they can popularize his AI and support others to learn.
- Provides an opportunity to practice new skills under supervision.
- Use AI and automation to reduce mundane tasks and empower your data science team to do more valuable, more satisfying work.
- Partner with experts who can work with your internal teams to close skills gaps, oversee your employees' acquisition of new skills, and develop AI/ML solutions to support your data science team.
Find your expertise and 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 be successful, use resources like Amazon Marketplace to find proven consultants and AI solution vendors.
