AI: Unlocking Value in the Mundane

Forget the AI hype; real value emerges from optimizing mundane internal tasks and boosting employee productivity. Strategic adoption, grounded in robust data and clear governance, will drive long-term success.

In the relentless churn of the 21st century’s technological revolution, artificial intelligence stands as a shimmering, often dizzying, beacon.

Each month seems to herald a new frontier, a fresh breakthrough promising to upend industries and redefine human capability.

For many business leaders, the sheer pace of innovation creates a palpable tension: how to embrace AI, not just for the sake of it, but with a clear understanding of where and how it genuinely delivers value.

The challenge, it turns out, isn’t just about keeping up, but about looking beyond the hype to the practical realities.

Serena Dayal, an Investment Partner at Athena Capital, offers a refreshingly grounded perspective amidst the AI frenzy.

In a candid conversation with PYMNTS CEO Karen Webster, Dayal highlighted a growing paradox: enthusiasm for AI often far outstrips any coherent strategy.

“I saw a cartoon on LinkedIn,” Dayal recounted, “It said, ‘What do we want?’ AI. ‘When do we want it?’ Now. ‘What do we want it for?’ We don’t know.”

This lighthearted jest, she notes, perfectly encapsulates the dilemma facing boardrooms globally.

The mandate for AI adoption is clear, yet the strategic roadmap remains, for many, frustratingly vague.

This phenomenon is, in large part, driven by the powerful “fear of missing out” – the FOMO factor.

No company wants to be perceived as lagging in the AI race, leading to a scramble for adoption that can be more reactive than strategic.

Dayal cautions against this rush, pointing out that what constitutes the “frontier” of AI today can become mainstream or even obsolete within a mere three to six months.

In such a volatile landscape, patience, far from being a weakness, emerges as a strategic imperative.

The smartest leaders, she suggests, are not those making grand, sweeping overhauls, but those engaged in active learning, experimenting deliberately with small, controlled pilots before committing to major system transformations.

The most fertile ground for these initial forays, Dayal argues, lies not in ambitious, customer-facing applications, but in improving internal employee productivity.

It’s a message that resonates deeply, particularly given her observation that many board members themselves lack access to their own corporate large language models, despite a keen desire to engage.

The logic is compelling: before AI can redefine how a business interacts with its customers, it should first optimize how its internal teams function.

This approach minimizes risk, builds internal expertise, and allows organizations to learn and adapt without exposing their core customer relationships to nascent, unproven technologies.

Indeed, the true promise of AI, Dayal believes, is found not in futuristic moonshots but in the unglamorous, everyday fabric of business operations.

“It is honestly in the really mundane stuff,” she states unequivocally.

Think high-volume, low-risk tasks that consume countless hours: drafting internal memos, summarizing dense data sets, generating routine reports, or assisting engineers with co-pilot tools.

These might not grab headlines, but their collective impact on efficiency and human capacity is profound.

By automating these repetitive, often tedious steps, AI doesn’t replace human intelligence; it amplifies it, freeing up professionals to focus on higher-value, more creative, and strategic work.

Even in complex domains like treasury and finance, AI is already proving its worth, offering CFOs enhanced visibility into payables and cash flow, making experts better at what they already do .

The significance of the “mundane” cannot be overstated.

It’s where real, durable value is forged.

When companies leverage AI to optimize their existing processes, they embed efficiency and insight directly into their operational DNA.

This foundational work, however, hinges on one critical asset: data.

“Data is the future and one of the most valuable assets that companies can think about shoring up right now,” Dayal emphasizes.

The work of cleaning, labeling, and structuring information, while less glamorous than model building, is absolutely fundamental.

Even “exhaust data”—the often-overlooked byproduct of daily operations—can be mined for immense value once an organization understands the treasure trove it already possesses.

The ultimate challenge, she concludes, isn’t about AI’s ability to answer questions, but about formulating the right prompt and ensuring the underlying data is robust enough to yield meaningful answers.

As innovation accelerates, the regulatory landscape inevitably lags.

This places a significant onus on boards and executive teams to establish their own foundational principles for AI ethics and employee usage.

The reality of “shadow AI,” where employees independently adopt third-party tools, necessitates clear guidelines.

Companies are responding by developing AI handbooks and benchmarking their approaches against existing compliance obligations.

The alternative—unmonitored and unauthorized AI use— exposes organizations to substantial data and reputational risks.

Governance, in this context, is not a barrier to innovation; it is the bedrock upon which sustainable, responsible innovation is built.

Looking ahead, Dayal encourages leaders to focus AI investments where they can build defensible “moats.” This means prioritizing computationally heavy, repeatable tasks over subjective creative work until models mature further.

Engineering functions, with their structured data and clear workflows, are prime candidates.

The next wave of differentiation, she predicts, will come from hyper-vertical applications in specialized fields like healthcare, legal services, and niche analytics.

These are areas where deep domain expertise combined with proprietary data can create unique, unreplicable advantages that general-purpose AI platforms cannot easily match.

From an investment perspective, “smart money” is flowing into the foundational infrastructure of AI: compute power, robust data platforms, and enhanced cybersecurity.

These are the “picks and shovels” of the AI gold rush.

While costs will continue to decline, the most substantial returns will accrue to those vertical applications that deliver concrete, defensible value.

For executives grappling with when and how to act, Dayal offers straightforward, timeless advice: “Probably the number one thing is data.

Shoring up your data, making sure you have everything you need, and then thinking really creatively with an open mind about what you can do with that data for your business and what you can do with AI.” The excitement around AI is entirely justified, but it can also be a potent distraction.

The most meaningful, enduring gains will not come from headline-grabbing “moonshots,” but from the disciplined, often unglamorous, work of integrating AI into the daily operational fabric of business.

The companies that approach artificial intelligence as a long game, a marathon rather than a sprint, will be the ones that truly thrive.

As Dayal succinctly puts it, “Just remember this is the first inning of a very, very long curve.” The potential of AI is vast, but its power begins with mastering the basics.

The future will belong to those who make the mundane extraordinary.