Let me tell you a story about two bakers, who both opened shops on the same street.
No Loaf Breads is hyper-focused on efficiency. Its owner bought high-powered ovens, capable of turning out baked goods in a fraction of the time required by a conventional oven. He developed a streamlined system for ingredient prep, and an AI-enabled ordering process that had customers out the door as soon as they came in, piping-hot bread in hand. Without a doubt, he had the fastest buns in town.
Down the street, Doughscovery had had a different mission—value. This baker took a data-based approach to figuring out what customers really wanted. She employed an AI agent to help her track emerging food trends, generate creative new recipes, and analyze customer feedback. The pumpkin spice garlic bread may have been a flop, but customers lined up around the block for the matcha sourdough, skyrocketing Doughscovery to national fame.
Both bakers used AI to increase their productivity. But only one created something customers actually talked about, returned for, and were willing to wait in a long line to get their hands on.
Just like these bakers, businesses today face a choice in how they use AI agents. Will they optimize for speed alone, or will they focus on creating lasting value?
Agents have been one of the central tech topics of 2025, and so far, much of the conversation centers around their ability to work quickly. There’s a reason for this: agents are known for their ability to function autonomously to complete a goal from end-to-end, requiring little human intervention and allowing us to get more done with fewer resources.
But agents are so much more than time-savers. Below, I’ll dive into how businesses can create an AI strategy that doesn’t only boost productivity, but unlocks value.
Align Objectives With Agents’ Capabilities
Organizations are well aware of the transformative potential of AI agents—according to a survey among IT leaders by SnapLogic, 79 percent of respondents consider building and implementing AI agents to be a priority in the next 12 months, while 92 percent are confident that deploying AI agents will deliver meaningful business outcomes within the next 12 to 18 months.
In order to make the most of agentic AI, the first thing organizations have to do is map its capabilities against specific operational objectives; this ensures your approach is not only focused, but that it will yield quantifiable results.
To do this, I recommend identifying a few big-picture goals, which could include anything from reduced overhead to more comprehensive customer service. Then, assess the agentic AI landscape (which is changing quickly) to figure out how it can best be leveraged to achieve your objectives. By creating a detailed schematic that ties your goals to a specific AI capability, you will have a clear roadmap for success.
As an example, let’s go back to the bread: Doughscovery’s baker prides herself on creating loaves that people love. Rather than shoot into the dark, she implemented an AI agent to analyze customer reviews, feedback and social media trends to gauge their preferences. Using that data, she also worked with her agent to brainstorm ideas for new flavors—which leads me to my next point.
Emphasize AI-Human Collaboration
While it’s true that AI agents will take over some jobs, their real value is taking on repetitive, draining tasks, allowing humans to focus on higher-value strategic work. Crucially, leaders need to remember that agents are not “set it and forget it” systems. Above, I talked about how agents can use data to brainstorm new bread recipes, and this is an excellent example of their utility. But AI agents don’t have taste buds. As promising as a concept may sound, only a human can verify if it actually works.
An unappealing bread flavor is a silly example of the importance of human intervention in agentic operations. In reality, human-in-the-loop points remain a requirement not just to ensure quality, but safety and security. Consider an agent-assisted banking query, like a loan application. Agents can automate a substantial amount of the interaction, weighing factors like risk and compliance. But ultimately, the final decision requires human judgment. Without it, serious issues can arise.
Drive Strategic Decision-Making
With their ability to reason, agents excel at speeding the decision-making process, cutting the costs of experimentation and taking decisive action quickly. In the past, seven out of 10 executives said burnout impacted their ability to make decisions. Now, agentic AI is relieving much of that pressure, allowing leaders to focus on strategic decisions with big-picture outcomes.
AI agents don’t just accelerate decision-making—they enhance its quality. By sifting through massive datasets, identifying patterns, and generating insights in real-time, they help businesses move beyond gut instinct and into data-driven strategy. This is especially useful in high-stakes industries like finance, healthcare, and supply chain management, where fast, informed decisions can make the difference between profit and loss, or even life and death. But the true power lies in combining AI’s analytical rigor with human intuition, experience, and ethical reasoning. The best outcomes don’t come from AI alone, but from AI amplifying human expertise.
AI agents can make businesses faster, but speed alone isn’t a strategy. By aligning AI capabilities with objectives, fostering collaboration between humans and AI, and leveraging its insights for strategic decision-making, companies can create lasting value that customers will keep coming back for.
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