Throughout history, civilization has been shaped by transformative inventions, extraordinary discoveries, and breakthrough technologies—the wheel, penicillin, and the internet, to name a few.
Today, businesses and individuals alike are confronting the next world-changing force: Artificial Intelligence (AI). Like every major innovation before it, AI generates excitement—but also fear, misunderstanding, and relentless debate.
This article does not attempt to judge AI’s effectiveness, examine its legal implications, or argue whether it belongs in one industry or another. Nor does it seek to predict AI’s impact on every organization or individual.
One reality is undeniable: Artificial Intelligence, in some form, is here to stay.
Successful AI adoption hinges on three critical considerations:
- Preparation
- Realistic Evaluation: Does AI belong in a specific organization or function?
- Reasonable and Defined Sizing: How will AI be introduced in ways that are practical, actionable, and usable?
This article intentionally sets aside points #2 and #3 for a future discussion and focuses entirely on Preparation. That said, these three elements are often pursued simultaneously. For example, conversations about whether to adopt AI—and where it may provide value—should begin during the preparation phase. Decisions about where AI will be deployed significantly influence the design of a strong data program and the relevance, structure, and governance of that data.
Before an organization adapts algorithms or maps out an AI adoption strategy, it must prepare. Even the most sophisticated AI models will fail if they are powered by data that is not:
- Clean
- Accurate
- Complete
- Consistent
- Timely
- Relevant
- Organized
- Well governed
The margin for error is effectively zero. When strategic decisions rely on AI-generated outputs derived from flawed, incomplete, or inaccurate data, the consequences are real and measurable. Poor data quality leads to unreliable AI results, erodes trust, exposes organizations to reputational and financial risk, and results in missed opportunities.
The solution is about as exciting as cleaning out a cluttered closet. It is not glamorous. It is time-consuming, often frustrating, and sometimes costly. But it is absolutely essential. Organizations that align their data with these foundational standards before deploying AI are far more likely to achieve meaningful, reliable results. After all, data is only as good as its quality.
So where should organizations begin?
With preparation of a business case that answers two fundamental questions: What value are we trying to create and is our data strong enough to support AI?
Key queries include:
- Are we capturing the right data—and enough of it?
- Is the data clean and accurate, and how do we ensure it remains that way?
- Is the data outdated or focused on products and services we no longer support, making it irrelevant or untimely?
- Is it consistent enough, and without bias, for AI algorithms to interpret effectively?
- How is the data organized?
- Do we have appropriate controls for data access, storage, and protection?
Additional considerations must also be addressed:
- Do we have the processes and procedures needed to conduct a successful data review and for automation?
- Have we accounted for the change management required to support both AI adoption and a heightened focus on data quality?
- Do we have the right expertise in place—data specialists, process engineers, process mappers, change managers, sponsors, and leadership—to execute this effort successfully?
The organizations that lead in the AI era will not necessarily be those with the largest budgets or the most advanced models. They will be the ones that treat data quality as a strategic imperative and invest in preparation. Companies that approach data quality casually will fall behind. Those that prioritize excellence in data preparation will unlock AI’s full potential.
So—how is your data looking these days?
BASG helps clients do the essential “housecleaning” to ensure their data quality, data management, and data access and protection are ready to meet the demands of innovative technology and future growth.
