McKinsey’s Global Survey on AI found that 65% of respondents said their organizations were regularly using generative AI in at least one business function in early 2024. That single number explains why so many managers are being asked to ‘get fluent in data’ quickly even if they’ve never written a line of code, or pursued a formal doctorate in business to build that fluency.
That survey was fielded online from Feb 22 to Mar 5, 2024, with 1,363 participants, and the results were weighted by each respondent nation’s share of global GDP. In this article we’ll translate what ‘data-smart leadership’ looks like in everyday work, show you a surprisingly practical government benchmark for AI adoption and connect it to what a BI-focused DBA can train you to do when the next decision hits your desk.
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Paid to ask better questions
The most useful upgrade from ‘manager’ to ‘data-smart leader’ is simple: you get better at choosing the question before anyone builds the analysis. When you do that well, you stop treating dashboards like vending machines and start treating them like decision support.
It’s worth remembering how valuable analytics capability has become in the US job market. The Bureau of Labor Statistics (BLS) lists the median annual wage for data scientists at $112,590 (May 2024) which is a strong signal of what organizations will pay for people who can extract meaning from data.
BLS also projects employment of data scientists will grow 34% from 2024 to 2034 and estimates about 23,400 openings per year on average over that decade. Read that as encouragement: the demand isn’t just for specialists, but for leaders who can sponsor the right work, interpret it responsibly and act on it with confidence.
Your job isn’t to become the data scientist. Your job is to be the person who can clearly state what ‘better’ means, how you’ll measure it and what trade-offs you’re willing to accept before the team spends weeks analyzing the wrong thing.
That mindset is also how you earn trust with technical teams. When your question is crisp, your analysts can move faster, your stakeholders argue less and your decisions come with a paper trail that makes sense six months later.
Your AI ‘weather report’ is already public
Once you start leading with better questions, the next step is learning to calibrate: what’s happening outside your company and what does ‘normal progress’ look like in the real economy?
In March 2024, the US Census Bureau announced it was releasing Business Trends and Outlook Survey (BTOS) data that includes an Artificial Intelligence supplement. BTOS matters because it’s a repeatable government-run pulse check designed to track business conditions and it provides a consistent way to watch AI adoption over time rather than relying on headlines.
The Census Bureau’s research paper on tracking firm AI use explains that BTOS is collected every two weeks and draws from a sample of about 1.2 million employer businesses across all 50 states, DC and Puerto Rico (with some sector coverage details and exclusions). That scope is exactly what makes it useful for leaders: it’s broad, regular and built for comparison.
The same Census research reports that the share of firms reporting AI use rose from about 3.7% in September 2023 to about 5.4% in February 2024. It also reports an expectation of roughly 6.6% AI use by early fall 2024.
You don’t need to treat those percentages as a scoreboard where ‘higher is always better.’ Instead, use them like a weather report: that helps you set a pace your organization can sustain while still moving forward.
And BTOS can keep you honest about differences across industries. Census research highlights meaningful variation by sector, including far higher reported usage in Information (one figure cited is 18%) than in sectors such as Construction (one figure cited is 1%).
This kind of benchmark doesn’t just help you justify doing more. It can also help you defend doing less if your team needs to build data quality, governance or training before you scale anything.
What a BI-focused DBA trains you to do on Monday morning
McKinsey’s same early-2024 survey found overall AI adoption (beyond generative AI specifically) jumped to 72% of respondents’ organizations using AI in at least one business function. That’s the practical backdrop for a BI-focused DBA: it should train leaders to guide decisions in a world where AI and analytics show up across teams, budgets and risk conversations.
A DBA is advanced study but the payoff should feel very down-to-earth at work. You’re building an operating model for evidence-based leadership: how problems get framed, how evidence gets collected, how results get interpreted and how you communicate what’s true without overselling it.
It’s becoming the person who can run a decision meeting where data, risk and outcomes all have a seat at the table. In McKinsey’s write-up they also note that risks organizations consider with gen AI include issues like inaccuracy, data privacy, bias and IP infringement which is exactly why leaders need a repeatable way to evaluate more than just performance metrics.
Here’s a simple script you can use (and refine as your BI maturity grows) and it’s the kind of habit a BI-focused DBA should sharpen rather than replace:
- What decision are we making and what would we do differently if the answer changes?
- What’s the minimum evidence we need and what data do we already trust enough to use?
- What method fits the question, for example trend analysis, experiment, forecast or qualitative research?
- What could make the result misleading, such as data gaps, bias or incorrect assumptions?
- How will we explain the conclusion clearly to non-technical stakeholders, including what we don’t know yet?
And consider, when AI is already showing up in business functions across the economy, do you want to be the person who approves analytics work or the leader who can clearly define success, challenge shaky evidence and explain the decision clearly?
Lead the decision, not the dashboard
Put the pieces together and you get a hopeful picture, not a scary one.
BLS shows analytics capability is valuable and in demand in the US economy. The Census Bureau gives you a public, methodical way to benchmark AI adoption over time with broad coverage and frequent measurement. McKinsey’s 2024 survey puts a number on what many managers already feel: AI use is common enough now that leaders benefit from a structured way to evaluate value, risk and execution.
The out-of-the-box move is to treat data-smart leadership as a daily practice, not a department. Choose one decision you own this month, run it through the checklist above and keep notes on what made the conversation clearer and what created confusion.
Lead the decision and the dashboards will become genuinely useful tools rather than noisy status screens.
