The promise and pursuit of artificial intelligence (AI) are undeniable. AI is reshaping how organizations interact with customers, streamline operations, manage risk, and uncover insights that were previously unattainable (or at least more difficult to achieve). But amid the excitement, a hard truth remains: Without a solid foundation in data management, AI cannot deliver on its full potential. And without a workforce that understands at least conceptually how AI works, and what initiatives it is most suitable for to achieve business objectives, organizations risk falling into the trap of hype without impact.
This is where the concept of AI fluency comes into play.
SMEs, technical specialists, and coders/developers may be the ones tasked with defining and building AI models and fine-tuning algorithms, but AI fluency is about ensuring that everyone — from business executives to data stewards to staff at all levels — can understand, evaluate, and engage with AI effectively. As more organizations begin to embed AI into their strategic agendas, AI fluency is no longer a “nice-to-have.” It’s increasingly a key competency, as is effective, mature data management to drive responsible AI programs.
What Is AI Fluency?
“AI fluency” refers to the ability of individuals and teams to understand AI concepts, assess the value and risks of AI applications to meet objectives, and participate meaningfully in AI-driven initiatives. It does not require everyone to be a data scientist or an AI engineer, but it does involve a baseline understanding of how AI systems work, what they are capable of, how they rely on data, and what ethical or operational considerations they introduce.
AI fluency can be defined through a combination of:
- Conceptual Knowledge: Understanding machine learning, large language models, and predictive analytics at a high level.
- Business Context: Knowing where and how AI can drive business outcomes — and where it can’t.
- Data Literacy: Prioritizing data quality, comprehensiveness, lineage, definition, data privacy and ethics, and suitability for training AI models.
- Ethical Responsibility: Recognizing the risk of introducing bias from incomplete or inaccurate data, plus the societal, regulatory, and reputational implications of AI use.
- Collaboration and Alignment: Supporting shared language, objectives and workflows between technical and non-technical teams.
In essence, AI fluency helps bridge the divide between business ambition and technical feasibility. It’s a mindset and a strategic approach more than a technical skill set.
The Inseparable Link Between Data Management and AI
Behind every successful AI application lies a robust data ecosystem. Yet, too often, organizations leap into AI experimentation without first fully addressing critical data management challenges. These might include inconsistent data definitions, siloed systems of inaccessible data, incomplete metadata documentation, and a lack of governance.
Simply put, AI cannot succeed without good data management:
| Data Management Best Practice | Why It Matters for AI |
| Data Governance | Defines ownership, accountability, and usage rights — crucial for responsible AI use. |
| Data Quality Management | Ensures that models are trained on accurate, consistent, complete and timely data. Otherwise — garbage in, garbage out! |
| Metadata & Lineage | Supports explainability and compliance by showing where data came from and how it’s transformed. |
| Data Privacy Controls | Protects sensitive information and enables AI systems to comply with regulations like GDPR and CCPA. |
| Data Ethics | Embeds fairness, transparency, and social responsibility into AI design, and also monitors for potential bias coming from the data. |
| Data Literacy | Empowers users to understand the data being used in AI, as well as how to question outcomes or even detect errors. |
AI fluency and data management maturity are thus mutually dependent on one another. AI fluency enables teams to ask the right questions about AI use cases and risks, while data management best practices provide the foundation for trustworthy, scalable AI deployments.
Business and Data Professionals Must Collaborate
Historically, data management as a function has often been the responsibility of IT or compliance teams, and increasingly as a dedicated department, while business leaders have focused on innovation, strategy and growth. The increasing prevalence of data has also driven the need for data consumers in the business to expand their knowledge of data — how to identify it, access it, use it appropriately to support objectives, and protect it. But the rise of AI — especially generative AI and real-time decision-making — has forced this convergence even more.
Business professionals must understand how data is managed, and data professionals must understand that business objectives shape data priorities. This two-way literacy is key to creating AI solutions that are not only innovative, but also practical, ethical, and aligned with strategic goals to deliver the results required.
For example:
- A marketing executive exploring customer personalization with AI must know whether the data feeding that model is complete, current, and compliant.
- A legal or compliance officer evaluating AI for fraud detection must assess whether the system is auditable, explainable, and free of bias.
- A product manager using AI for feature recommendations must understand model limitations, intellectual property (IP) conflicts and what is relevant to customers.
This kind of collaboration is only possible when both groups speak the language of data and AI fluently.
Practical Steps Toward AI Fluency and Data Readiness
So, how can organizations advance these disciplines together? Below are key steps for developing AI fluency while strengthening data foundations:
1. Establish a Unified Strategy for Data and AI
Treat data management and AI adoption as part of a single, integrated digital strategy — and with clear objectives. Define how AI supports business outcomes and identify the data capabilities required to achieve those outcomes. This includes data integration, governance, security, and access.
2. Invest in Education Across Roles
AI fluency begins with awareness and understanding of the concepts, not just technical training. Provide learning opportunities that relate to different roles. An executive managing business applications and risk will have very different approaches than an operational team member exploring workflow integration or a sales manager identifying up-sell opportunities. Cross-functional workshops can enable data professionals and business leaders to collaborate and co-design AI use cases.
3. Benchmark Your Maturity
Identify capability gaps and prioritize improvements for both data and AI. Rely on established data frameworks, such as the EDM Association’s DCAM (Data Management Capability Assessment Model) and CDMC (Cloud Data Management Capabilities), to assess your current data ecosystem, identify opportunities to improve data maturity, and implement data management best practices. Get involved with EDM Association’s AI, Data & Analytics Controls (ADAC) Workgroup to help define best practices for managing risk in AI and analytics. And explore AI maturity models to help track fluency, governance, and readiness.
4. Operationalize Data Ethics and AI Governance
Ethics policies are critical, but make sure they are activated! Organizations should embed ethics into every stage of AI development, including:
- Diverse data sourcing and validation
- Bias and fairness testing
- Human-in-the-loop processes
- Transparent documentation and model explainability
Cross-functional AI governance teams can help oversee risk and ensure accountability. Ongoing monitoring and analysis are also key to identify and quickly address any concerns and prevent proliferation of any identified issues.
5. Modernize Data Infrastructure to Underpin Responsible AI
Legacy data architectures often hinder AI initiatives. Modern data platforms — cloud-native, scalable, and integrated with governance tools — make it easier to:
- Ingest and prepare high-quality data
- Automate compliance checks
- Monitor model performance over time
This technical backbone is a critical enabler of both AI innovation and responsible data use.
A New Era of Data-Driven, AI-Enabled Business
The rise of AI has catalyzed a fundamental shift in how organizations think about data. Where data once was a byproduct of business operations, it is now the fuel for competitive advantage, the foundation for trust, and the driver of intelligent automation. And it’s only going to continue to evolve and grow exponentially.
But unlocking the promise of AI requires more than ambition. It demands AI fluency across the workforce and mature data management across the enterprise. The two go hand in hand.
Organizations that embrace this dual mandate — investing in both people and process — will be best positioned to:
- Build trustworthy AI applications
- Navigate regulatory requirements
- Create differentiated customer experiences
- Drive operational efficiency
- Accelerate innovation with confidence
…and ultimately achieve objectives that drive better business outcomes! In a world where data powers everything and AI transforms and accelerates everything, fluency in both is no longer optional. It’s essential.
Learn more:
- Explore DCAM, and read the case studies.
- Learn more about the Data Excellence Program.
- DCAM and the Data Excellence Program are available to EDM Association member organizations. Visit the EDM Association website or contact the team to learn more or inquire about membership.
This quarter’s column contributed by:
Jim Halcomb, Chief Research & Development Officer, EDM Association
Jim Halcomb is a strategy, data management, and cybersecurity executive with 30 years of international business experience. Jim leads EDM Association’s Communities of Practice, Best Practices Frameworks (DCAM & CDMC) and Training.
