AI & Work: How Productivity Became the New Baseline

4. AI & Work: How Productivity Became the New Baseline

When we talk about AI in the workplace, the conversation often circles around speed. Faster reports. Quicker scheduling. Streamlined operations. But speed is no longer the headline—it’s the baseline. In a world where every team has access to similar tools, productivity is the floor, not the ceiling. The real Differentiator is how organizations move beyond “doing more with less” to “achieving outcomes that were once out of reach.”

Today, nearly every organization has access to similar AI-powered tools. Automated workflows, instant analytics, and generative assistants have levelled the playing field. This shift means that simply “doing more with less” isn’t enough to stand out. The new challenge—and opportunity—is moving beyond productivity to possibility: achieving outcomes that were once out of reach.

From Faster to Smarter: Microsoft reports that AI copilots don’t just cut reporting time—they help leaders make better, faster decisions. Teams can now analyze patterns across millions of data points and move from hindsight to foresight.
From Efficiency to Creativity: Coca-Cola has used generative AI to co-create campaigns with its consumers, moving marketing beyond volume to deep, emotional engagement. Freed from repetitive tasks, creative teams can focus on storytelling and brand building.
From Output to Impact: Walmart has deployed AI in supply chain orchestration, not only accelerating inventory tracking but also preventing out-of-stock scenarios before they occur—saving millions in lost sales and improving customer satisfaction.
From Personalization to Transformation: HubSpot’s AI-driven marketing tools allow small teams to deliver personalization at a scale once reserved for enterprise players, reshaping what’s possible for SMBs in competitive industries.

In this landscape, productivity is the floor, not the ceiling. The organizations that will define the next decade aren’t the ones who adopt AI to keep up, but those who embed AI to break through—unlocking new strategies, new markets, and entirely new ways of working.

4.1 From AI Literacy to AI Agency: The Workforce Shift

Few years ago, knowing how to use AI tools felt like a competitive edge. Learning prompts, exploring automation, and experimenting with platforms defined what we called AI literacy. It was about understanding how to make AI work—at least at a basic level.

But the game has changed.

Today’s leading organizations are moving beyond basic AI literacy toward what’s known as an AI agency—the capability to design workflows, delegate tasks, and oversee outcomes that AI systems deliver.

This isn’t about learning the next trending app.
It’s about imagining how work itself happens, where humans guide intelligent systems, and AI executes with speed and precision.

Teams with AI agencies don’t just complete tasks; they deliver outcomes.

AI literacy says: “I know how to use this tool.”
AI agency says: “I know how to redesign the process so the tool delivers results without my constant oversight.”

This marks a shift from using technology to orchestrating intelligence.

Case Study: BCG’s Enterprise GPT and the Rise of AI Agency

Boston Consulting Group (BCG) offers a powerful real-world example.
The firm built an enterprise-grade GPT platform that enables consultants to create custom AI agents for specific client projects.

In one engagement, a team needed to interview 30 engineers, transcribe hours of discussion, identify key themes, and produce a strategic presentation—a task that once consumed two full weeks.

With their new AI system, the process was completely restructured:

AI handled transcription and summarized the interviews.
It surfaced key insights, drafted slide outlines, and organized supporting evidence.
Consultants focused on validating the findings and sharpening the strategic story.

The result: a turnaround time of just 2–3 days with equal or higher quality.
Instead of spending hours formatting slides, consultants devoted their time to analysis, creativity, and client impact.

This is what an AI agency looks like in practice—humans defining the goal, AI executing the process, and teams achieving more in less time.

4.2 Raising the Bar: Turning AI Efficiency into Competitive Advantage

Today, nearly every organization uses AI in some form. Efficiency is no longer a Differentiator — it’s the baseline. The true advantage now lies in how well you use AI.

The danger of settling for “good enough” outputs is that it makes your business indistinguishable. When everyone can generate a decent report, response, or proposal in seconds, quality becomes the real competitive edge. Customers and stakeholders now expect precision, creativity, and personalization—delivered at a speed that AI makes possible.

Teams that develop AI agencies understand this shift. They don’t accept average results from automation; they design intelligent systems that deliver exceptional outcomes consistently. They use AI not just to save time, but to raise the bar.

JPMorgan Chase, one of the largest U.S. banks, deployed AI-powered assistants to support its customer service operations. The system automatically drafts responses to common customer emails and queries, which are then reviewed and finalized by human agents. Previously, responses could take more than a day, but with AI assistance, turnaround times dropped to just a few hours. According to reporting in The Financial Times (2023), JPMorgan has been rolling out generative AI to improve both speed and personalization in customer interactions. Early results showed marked improvements in efficiency and customer experience, with agents spending far less time on repetitive typing and more time resolving complex financial issues. This hybrid model — AI drafts plus human oversight — mirrors the shift to faster, higher-quality service delivery in financial services.

Case Study: Coca-Cola – AI-Powered Marketing Campaigns

Coca-Cola embraced generative AI not just for ad copy but for full campaign orchestration. In 2023, the company launched its “Create Real Magic” platform in partnership with OpenAI and Bain & Company.

Instead of simply generating slogans, Coca-Cola trained AI models on its iconic brand assets and consumer engagement data. This allowed marketing teams to co-create visuals, tailor messaging to different audience segments, and run hyper-personalized campaigns at scale.

The result was not only more resonant creative output, but also a measurable boost in digital engagement metrics, significantly higher click-through and share rates compared with traditional campaigns. Coca-Cola’s CMO described this as “a leap forward in how we connect creativity, technology, and our brand voice,” moving beyond AI literacy into true AI agency in marketing.

Contributor:

Nishkam Batta

Editor-in-Chief – HonestAI Magazine
AI consultant – GrayCyan AI Solutions

Nish specializes in helping mid-size American and Canadian companies assess AI gaps and build AI strategies to help accelerate AI adoption. He also helps developing custom AI solutions and models at GrayCyan. Nish runs a program for founders to validate their App ideas and go from concept to buzz-worthy launches with traction, reach, and ROI.