Q&A: Dia Adams, Enterprise Data/AI Strategist, TCG

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Dia Adams is Enterprise Data/AI Strategist at TCG, a technology consultancy to U.S. government agencies, where she serves as Enterprise Data Strategist at the White House. She has two decades of experience in data and AI strategy and digital transformation, helping organizations such as Bank of America and Johnson & Johnson with projects in data analytics, AI, and machine learning. Her work has helped organizations to enhance data architecture, create lucrative applications, and implement business intelligence solutions across cloud data platforms such as Snowflake, Databricks, AWS, Microsoft Azure, and Google Cloud.

Dia’s executive leadership skills were shaped by mentorship from the late General Colin Powell, an experience that she says has enabled her to guide cross-functional teams in building data-driven cultures and fostering data innovation. She has expertise in change management, stakeholder engagement, and executive leadership, all with the aim of ensuring that technology, data governance, and data management are aligned with organizational goals for sustainable transformation.

Dia is a Fellow of the American Council for Technology and Industry Advisory Council (ACT-IAC), a non-profit organization that creates partnerships between government and industry to advance government technology deployments.

She is the author of the best-selling book Winning With AI: A Blueprint for Corporate Leaders (available on Amazon).

Futuriom conducted this interview via email. It was delivered on June 23, 2025.

Dia Adams, Enterprise Data/AI Strategist, TCG

Futuriom: Where are enterprises today in terms of implementing AI? Are we at the starting point or farther along?

DA: The last two years have marked a shift from early experimentation and pilot projects to more structured, strategic implementation. AI is now a budget priority for most large enterprises as well as the federal government, with spending on AI growing much faster than overall IT budgets. According to McKinsey, as of 2025, 78% of companies report using AI in at least one business function, up from 55% just a year earlier. The adoption of generative AI is particularly rapid, with 71% of organizations regularly using GenAI in at least one area. However, only 1% of enterprises consider their GenAI strategies mature, highlighting that while adoption is broad, deep integration and value realization are still in early stages.

Given this, I’d say we are past the starting point, as AI is now being rapidly adopted and given more resources. However, we are far from full maturity. Companies and government agencies are still figuring out what all the advancement in AI means to them. Experts are beginning to develop best practices and become more creative with use cases. These things will take time to become widespread.

Futuriom: What is the biggest mistake organizations make when approaching AI?

DA: I think the biggest mistake made in AI adoption is implementing AI haphazardly out of FOMO (fear of missing out), as opposed to starting with a clear business problem that AI is uniquely positioned to solve and that an organization is able to successfully tackle.

Many organizations overestimate their readiness for AI, often underestimating the technical, data, and cultural foundations required for success. An honest assessment of organizational capabilities is essential before launching ambitious AI projects. Too frequently, companies focus on the technical aspects of AI implementation while neglecting the human, cultural, and change management elements that are equally critical.

Successful AI integration demands collaboration between business and technical teams, ongoing learning, and transparent communication about how AI will impact roles and processes. The most effective AI strategies strike a balance between automation and human judgment, ensuring that technology enhances, rather than replaces, human strengths. Without a clear AI strategy tied to business goals, organizations risk wasting resources on fragmented or misaligned efforts. Defining what success looks like and how AI will support broader objectives is crucial for realizing meaningful value from AI investments.

Futuriom: AI implementations are complex and involve many different organizational constituents. How can multiple departments best be enabled to work together on AI projects?

DA: This is something I’ve given a great deal of thought to over time, because I firmly believe cross-functional communication and implementation are absolutely essential components for achieving success with AI initiatives. In my experience, one of the biggest challenges facing organizations is coordinating efforts across different departments and ensuring alignment on AI goals and execution. To address this, I have been actively working on developing a comprehensive framework designed to help organizations confront and overcome these challenges in a structured way. In fact, I wrote a whole book about it.

A key recommendation within this framework is the establishment of an AI steering committee composed of diverse stakeholders from multiple departments across the organization. This committee serves as a collaborative platform where members from various functions can come together to jointly undertake AI projects, share insights, and align on priorities. Moreover, it facilitates transparent and efficient reporting on any concerns, challenges, or value generated by AI efforts, enabling quicker decision-making and course corrections when necessary. By fostering this level of cross-departmental engagement, organizations can ensure that AI initiatives are not only well-coordinated and aligned with business objectives, but also able to adapt rapidly to feedback and evolving needs.

Futuriom: We understand that AI projects rely on good data. What specific product types, employee expertise, or services do you suggest organizations procure to best organize their data for AI?

DA: To best prepare their data for AI, organizations should take a holistic approach that combines essential services, robust products and platforms, and specialized employee expertise. Engaging with data strategy consultants can help organizations assess their data maturity, design business-aligned data strategies, and implement best practices for AI readiness. Migrating legacy data systems to cloud or hybrid environments is also crucial, as it provides the agility and scalability needed for modern AI workloads. Additionally, ongoing training and change management initiatives are vital, ensuring that teams stay current in data literacy, AI ethics, and governance, which supports long-term organizational success.

On the technology front, enterprises should invest in unified data platforms that handle both batch and streaming data, such as modern data warehouses like Snowflake and BigQuery, or data lakehouses like Databricks. These platforms enable real-time analytics and scalable AI model training. Powerful data integration and ETL tools such as Matillion, Informatica, or SnapLogic, are essential for automating and standardizing data ingestion, transformation, and quality checks, helping to eliminate silos and ensure consistency. As AI workloads expand, organizations are increasingly adopting AI-native infrastructure, moving from traditional data lakes to intelligent, compute-optimized “AI lakes” designed specifically for performance and scalability. Data governance and observability solutions, like Collibra and Alation, play a critical role in providing data lineage, cataloging, and compliance, ensuring the trustworthiness and reliability of data used in AI projects.

Equally important is employee expertise. Data architects and engineers are foundational, as they design and maintain scalable, secure, and flexible data architectures, whether centralized, hybrid, or decentralized. MLOps engineers bridge the gap between data engineering and machine learning operations, ensuring the smooth deployment, monitoring, and updating of AI models in production. Data stewards and governance leads focus on data quality, privacy, and compliance, building trust in AI outcomes by ensuring data is accurate, accessible, and ethically managed. Finally, domain experts with deep business knowledge are essential for defining requirements and validating that AI-ready data aligns with real organizational needs.

By integrating these services, products, and skills, organizations can create a strong data foundation that maximizes the value and reliability of their AI initiatives.

Futuriom: What leadership roles (such as chief data officer or chief AI officer) do you think make the biggest impact on successful AI adoption in government?

DA: I believe chief data officers (CDOs) have made the most significant impact on successful AI adoption in government, by serving as the central leaders for data governance, strategy, and readiness. Established in 2018 via the Evidence Act [the Foundations for Evidence-Based Policymaking Act of 2018], CDOs treat data as a strategic asset and ensure data quality, accessibility, and usefulness. The CDO role has rapidly expanded to encompass AI leadership as well. CDOs now play an important role in shaping agency-wide frameworks that integrate both data and AI governance, aligning these efforts with federal core missions and executive priorities.

The CDO Council, an inter-agency collaborative coalition of federal data leaders, has amplified the impact of CDOs by promoting knowledge sharing, establishing best practices, and clarifying roles across government.

CDOs drive successful AI adoption in government by building strong data foundations, aligning data and AI strategies, managing risk and ethics, and fostering cross-agency collaboration, all of which are extremely important for delivering trustworthy and impactful AI solutions in the public sector.

Futuriom: What suggestions would you give to prospective tech employees looking for work in the AI field?

DA: For those seeking a career in AI, strong data literacy should be a top priority. The ability to collect, clean, and interpret data is fundamental, as data underpins all AI systems. Data literacy empowers individuals to ask the right questions, interpret AI outputs accurately, and make informed, data-based decisions.

Once a solid foundation in data literacy is established, aspiring AI professionals should develop technical skills in programming languages such as Python or Java, and gain a strong understanding of machine learning, deep learning, and data science concepts. Experience with frameworks like TensorFlow, PyTorch, and generative AI applications (such as ChatGPT, Gemini, and Claude) is also increasingly important. Familiarity with big data tools like Hadoop and Apache Spark, as well as prompt engineering for generative AI, will further set candidates apart.

Additionally, expertise in cloud platforms, especially AWS and its services like SageMaker, Redshift, and Glue, is highly valued, as more organizations deploy AI solutions in the cloud.

AI work can be daunting and requires a lot of grit. A mentor is always helpful. I’m happy to speak with anyone interested in developing a career in the field. Feel free to reach out to me on LinkedIn.

Futuriom: Thank you, Dia!