2 weeks Ago
Sanjay Bhatia, founder of Runday.AI, is a seven-time AI serial entrepreneur with three exits. getty We've come a long way from Hollywood films portraying AI as robots taking over all electronics and turning against humans.
With the major leaps we've made, especially in the last few years, AI has become a prerequisite for any business looking to gain a significant competitive advantage. Whether it's chatbots fielding customer questions at 3 a.m.
or machine learning algorithms analyzing trend data and forecasting, AI is not just a shiny plot twist in an action movie. It's a practical tool in both personal and business use cases. A 2025 Ascendix report found that 82% of companies in the world are using or exploring AI.
This includes 92% of Fortune 500 companies that have already adopted it, and this number will only continue to rise exponentially. One particular area where many businesses are recognizing AI's value is decision-making—especially data-driven decision-making. We live in an economy where data is the hottest commodity, and due to the sheer amount of inputs companies can harness before making any kind of decision, we must close a big efficiency gap when it comes to using the right insights to call the right shots.
This is where AI agents come in: Custom AI "support" analyzes and formulates insights faster than humans, giving companies a better overview of operations, customers and market trends. It's easy to fall into the trap of picturing AI agents as lines of code that churn out numbers. In reality, they're extremely nuanced.
The smartest AI agents out there use a combination of large language models with custom knowledge or databases to have consistently applicable context, seek out patterns, make intelligent decisions and continuously iterate on the quality of their output along the way. AI agents can be as invisible or visible as the use case requires them to be. For example, AI powers Netflix's entire recommendation system to make tailor-made content recommendations to users.
This system, hidden behind layers of Netflix-branded UI, makes decisions on which shows to recommend based on a variety of data sources, ranging from users' own consumption habits to demographic data and industry trend reports. At the same time, an AI agent that's a chatbot as a tool is more conversational in the experience it provides—whether it's a marketing executive working on a company's budget allocation for the quarter or a venture fund looking to diversify the portfolio with more data-backed actions. The case is simple: accuracy, efficiency and speed.
AI can streamline large and small decisions, noticing trends inaccessible to the naked (human) eye and making predictions in a fraction of the time. AI can also increase the speed at which actionable insights are readily available for review. Instead of monthly or quarterly manual reports, AI agents can provide near-real-time data for informing faster decisions, especially in cases of timely pivots to match the pace of the market.
Accenture announced in October 2024 that "the number of companies that have fully modernized, AI-led processes [had] These types of wins are not reserved for companies with massive budgets. We're at an inflection point where the playing field is evened out. With the pace at which organizations are adopting and embracing AI, smaller companies can also tap into building custom and use case-specific AI agents that can improve internal decision-making and deliver a better customer solution.
Amazon is well-known for its dynamic pricing strategies (sometimes multiple times a day) to adjust to market demand, competitor pricing and user data signals. AI-driven dynamic pricing allows the company to adapt faster than traditional retailers, which means it can capture more sales while maintaining optimal profit margins and continue as a category leader. On the other side of the spectrum, JPMorgan Chase uses AI to analyze large volumes of transaction data, quickly flagging any unusual or suspicious activities to future-proof the organization's ability to fight fraud and deliver a better experience to their end users.
Even though AI adoption is an inevitability, rolling out company-wide AI modernization initiatives isn't always going to be smooth. The biggest "make-or-break" reason isn't necessarily the tech itself; it's ultimately the decision-makers who need to opt in to start leveraging AI tools for better outcomes. Some employees will worry about job security, while others will be reluctant to trust a new system they don't fully understand.
A few ways to remove friction in the rollout include: • Education: Provide training sessions, create spaces for open dialogue and show practical ways AI can reduce busywork rather than replace your team's jobs. • Starting Small: Pick a specific area (e.g.
, improving sales forecasts) and begin testing AI there with a controlled group of stakeholders. Track the results, share the wins and note any lessons for company-wide visibility. As teams witness improvements, they're more likely to adopt AI in other areas—as long as they don't see a threat to their job security or position.
This gradual approach also allows leaders to refine how data is collected and ensure it meets the standards intelligent agents need. Whether you're a team of 10 or 10,000, the impact of AI will be equally valid. Enter the game while the playing field is level.
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