Why AI Fluency Matters-Even If You're Not Technical

I don’t write code. I’m not building models. My world is shaping culture, recruiting great people, and enabling them to do their best work. Yet I feel the impact of AI every single day — in smarter workflows, faster insights, and more strategic time.

In the past decade, “digital fluency” became a table stakes skill: you didn’t have to be a programmer, but you needed to know how to use Slack, spreadsheets, presentation tools, and the suite of productivity software. Today, AI fluency is on track to become the next frontier. It’s not about building models; it’s about knowing how to think with, work alongside, and steer AI systems in your daily work.

You don’t have to write code to feel the impact of AI in your life. In fact, many of the gains—and challenges—are playing out in non-technical functions: marketing, HR, operations, recruiting, sales, finance, strategy. Organizations are already structuring workflows, performance metrics, and expectations around AI capabilities.

Consider these data points:

• 🧠 In a study by MIT Sloan and collaborators, providing generative AI tools to knowledge workers improved performance by roughly 40 % over peers who didn’t have access.
• 💸 Among workers using generative AI, the St. Louis Fed found an average time savings of 5.4 % of weekly hours, translating into ~2.2 saved hours per 40-hour week.
• 👍Recent research has revealed that in customer service, deploying AI assistants increased issues resolved per hour by ~15 %.
• 📈 McKinsey estimates the long-term productivity potential of AI adoption across corporate use cases at $4.4 trillion globally.

If such improvements accrue mostly to technical roles, the gap widens. But if non-technical teams remain unversed in AI, they risk becoming bottlenecks, missing opportunities, or having deliverables filtered through a technologists’ lens. When AI becomes embedded in workflows and decision support, those who can’t engage with it fluently are implicitly marginalized.

There’s also a perception that “using AI tools” is somehow cheating or less legitimate. But that framing misses the point: AI is now part of our digital environment, like calculators or search engines. Refusing to embrace it is not noble — it’s a decision to slow down and fall behind.

Yet fluency doesn’t mean mastery or engineering. Rather, it’s about knowing when, how, and where to draw on AI: Which tasks are ripe for automation or augmentation? What prompts will yield value? When should you validate or override an AI’s output with human judgment? That kind of fluency becomes a force multiplier in roles across talent, marketing, sales, operations, creative, and strategic functions.

At Deepgram, our conviction is simple: AI fluency should not be an elite competency reserved for engineers — it should be woven into the fabric of every role. Here’s how we operationalize that belief.

We give people permission — even encouragement — to bring AI into their work, to test, iterate, and learn. Mistakes are expected; growth is rewarded. Employees aren’t penalized for “leaning on AI” in interviews. Instead, we expect candidates to show how they think, problem-solve, and collaborate with AI as part of their toolkit.

Rather than creating a separate “AI team” silo, we integrate AI tools across disciplines. In recruiting, for example, we use AI to synthesize candidate data, generate interview guides, and surface patterns from previous hiring decisions. In operations, we use AI to organize feedback, analyze usage logs, or triage support tickets. Each function is encouraged to map its own recurring work into AI augmentations.

We avoid one-size-fits-all training. Instead, we support role-based paths: prompting workshops, use-case labs, and peer learning cohorts. We ask: What does AI fluency look like for a recruiter? For a product manager? For a marketing associate? That way, people don’t feel overwhelmed or alienated — they begin from the domain they know.

Senior leaders model usage. We’ve made AI fluency part of performance conversations — not as a checkbox but as a lens on impact, innovation, and judgement. Encouragingly, many organizations now mirror this shift: BCG, for instance, requires AI competency to be part of performance reviews and regularly offers internal “AI builder” training.

All this is not just novelty — it’s necessary. Harvard Business Publishing’s research shows that organizations that allow experimentation in the context of real work — rather than abstract training — are far more likely to produce fluency at scale.

Scaling AI adoption across functions — not just within the engineering org — unlocks compounding returns.

Productivity and time savings When multiple teams become AI fluent, redundancies collapse. Cross-team collaboration accelerates. Time formerly spent on repetitive tasks — data cleaning, summarization, drafts, formatting — is reclaimed for more strategic work. Across firms, productivity gains from AI appear not just in hours saved but in qualitatively better outcomes.

Quality, creativity, and insight AI can surface patterns, deviations, and latent signals that humans might miss. For instance, given a recruiting pipeline, AI can flag misalignment in assessment pools; in marketing, it can suggest messaging pivots based on campaign trends; in operations, it can highlight anomalies in metrics. Fluency allows users to ask better prompts, interrogate outputs, and refine toward insights rather than accepting surface suggestions.

Scalability with lean teams One of Deepgram’s strategic levers has been doing more with less. With under 150 people, we’ve shipped products, supported customers, and grown revenue at scale. AI hasn’t replaced people — but it has freed them from busywork so they can focus on impact.

Learning and organizational agility AI fluency fosters a culture of continuous adaptation. Teams that experiment early become the frontier of innovation. New ideas emerge as micro-pilots across functions. Organizations with fluent workforces are more resilient to disruptive change. As BCG notes, fluency should sit alongside system thinking, problem framing, and judgment as core skills for future-ready leaders.

Yet it’s not all sunshine. There are risks of overreliance, erosion of craftsmanship, or a creeping complacency in accepting AI outputs without scrutiny. Research warns of a phenomenon called workslop — AI-generated content that looks polished but lacks substantive meaning — which can waste time and degrade team trust. Another recent study observed that while generative AI collaboration boosts task output, it can dampen intrinsic motivation in tasks where AI isn’t involved.

That is why fluency must be paired with critical judgment, domain oversight, and a culture of questioning.

In every era of technological change, the question isn’t whether machines will take over, but which tasks will migrate and which will remain human. With AI, the shift is not black-and-white: it’s fine gradients of outsourcing, augmentation, and human supervision.

Tasks ripe for automation These are repeatable, structured, high-volume tasks. Examples: formatting, summarizing large texts, draft generation, routine data pulls, basic classification, initial email drafts, pattern detection, triage of common queries. These are not zero-value tasks — they matter — but they are increasingly commoditized.

Tasks demanding human center What remains distinctively human: framing novel problems, strategy, ethics and interpretation, relationship-building, persuasion, context-sensitive judgment, emotional intelligence, vision, creativity under ambiguity, domain mastery. AI might assist in all of these, but humans must lead the interpretive direction.

In practice, the optimum configuration is hybrid: machines do the grinding; humans do the orchestration. That means the value proposition for non-technical workers is not survival but evolution: your role changes — from doing the mechanics to designing the orchestration, interpreting the outputs, and steering the feedback loops.

This division is not static either. The boundary of what machines can do evolves. Today’s augmentations may become tomorrow’s automation. That’s why fluency matters: those who move fast up the learning curve map control zones, know how to question outputs, and adapt as the frontier shifts.

Complementing this view: recent research finds that AI tends to increase demand for human skills that complement it — digital literacy, teamwork, resilience — more than it substitutes core work entirely.

But we must also face a hard truth: some roles and tasks will be redefined. Organizations must lean into reskilling, transition planning, and empathy. The future of work isn’t zero-sum; it’s reallocated. Those who can adapt will thrive.

Looking ahead, here are some trajectories I expect — and that we at Deepgram are orienting toward:

1. Tool-first design mindset Rather than “bolt on AI,” workflows will be designed around AI. In other words, instead of treating AI as an assistant, many processes will be structured so that AI is the default collaborator. The human’s role becomes: audit, refine, supervise, contextualize.

2. Democratization of AI tooling

We’ll see AI tools embedded deeply in everyday platforms — recruiting systems, CRMs, HR software, analytics dashboards. Non-technical workers will access powerful capabilities without needing to engage with code or complex model interfaces.

3. AI fluency as hiring and performance criteria Much like digital fluency today, AI fluency will become a baseline expectation. When hiring, I’ll ask: “How did you use AI in recent work? How did it inform your decisions? How did you detect errors or biases?” When evaluating performance, I’ll expect increased effectiveness, not just output. At Deepgram we’ve already begun that shift; in the broader market, firms like BCG are formalizing it.

4. Micro-experimentation, internal AI “sandboxes” Organizations will create safe environments—”AI labs,” hack weeks, side-project time—where non-technical employees can explore tools, test use cases, then bring high-potential experiments into production. This is akin to the 20% time or side project models that historically yielded innovation.

(Note: we even hosted an internal hackathon for technical and non-technical people alike during our company offsite. Nobody was allowed to type code. They could only use AI coding tools and natural language.)

5. Hybrid AI-human teams Teams will become cross-disciplinary hybrids: people with domain expertise, AI specialists, data curators, and “AI translators.” Even in non-technical domains, roles will emerge to govern prompt design, validate models, monitor outputs, ensure ethics, and maintain guardrails.

6. Heightened demand for judgment, context, ethics Because AI can generate plausible but flawed outputs (the “AI trust paradox”), human oversight becomes non-negotiable. The future worker won’t just accept the machine’s output — they’ll interrogate it, spot bias, contextualize, and correct it.

7. A changing wage and value landscape As AI automates lower-margin burdens of roles, compensation and recognition may shift toward those who master higher-value, complementary work: interpretation, design, strategic insight, and creative orchestration. This aligns with data showing premium in job postings for “AI-complementary” skills.

8. Continuous learning as a default Given that tools will evolve quickly, fluency won’t be a one-time checkbox — it’ll be a mindset. The best non-technical workers will treat AI exploration like gym training: consistent, iterative, and evolving.

Conclusion

Again, I don’t write code. I’m no software engineer or AI researcher. My priorities are creating the best work environments, recruiting talented people, and transforming their diligence into results. Nonetheless, I feel the impact of AI every single day — from smarter workflows to more efficient analyses.

Non-technical people need AI fluency not because technology is a novelty, but because it is becoming the substrate of modern work. At Deepgram, we build that fluency by embedding AI into culture, workflows, training, and accountability. The gains scale when more teams become fluent: productivity rises, innovation accelerates, and lean orgs punch above their weight.

But this is not about handing over work to machines. It’s about better partitioning: machines should handle bulk, repetitive, pattern-intensive tasks. Humans should lead in judgment, design, ethics, relationships, and novelty. The frontier will shift — the shape of roles will evolve. But fluency gives non-technical workers the agility to adapt, not be sidelined.

If you’re not experimenting with AI tools yet, start now. Don’t wait until fluency becomes a prerequisite. Because by then, the delta between fluent and non-fluent will already be vast — and some may find they're left behind.