Imagine you are a chief executive. Your AI strategy task force has just presented you with two strategic options. The first is safe: use agentic AI to reduce overhead and save 10% of overall human capital costs. The second is daring: increase growth tenfold by transforming your company's operations with agentic AI. The first choice barely moves the needle but helps the AI initiative pay for itself. The second could make you a legend—or get you fired.

Superlatives about agentic AI are off the charts. KPMG estimates it will unlock $3 trillion in annual productivity gains. Accenture argues it is "no less than a new type of capital" and "marks a shift in economic history." Gartner claims organizations have a crucial three- to six-month window to define their agentic AI product strategy. But jumping in without a solid strategy can result in failure.

Understanding the Risks

1. AI Washing

Many vendors engage in "agent washing," falsely portraying products as agentic. Gartner estimates that less than 13% of thousands of agentic AI vendors actually ship genuine agentic products. Most rebrand existing tools—AI assistants, robotic process automation, script-based services, chatbots—as agentic. Assuming these can perform autonomous tasks is faulty, leading to pilot projects destined to fail.

2. Runaway Costs

Agentic AI consumes tokens voraciously as multiple agents run constantly. Cloud bills balloon. OpenAI went from zero revenue in late 2022 to more than $20 billion in 2025. Companies scaling up agentic use find their costs escalating rapidly.

3. Unpredictable Results

AI projects are non-deterministic: same input can produce different outputs. This lack of predictability is brutal for building, testing, debugging, validating outputs, ensuring compliance, and maintaining consistent behavior. Madhav Thattai from Salesforce notes, "Software used to be solely deterministic... AI agents break that model."

4. Rogue Agents

An unintended action by an agent at scale can ripple through an organization at light speed. Goal misalignment occurs if an employee prompts an agent incorrectly. Failures cascade into others, creating a domino effect.

5. Data Security and Privacy Risk

Almost all deep AI agentic deployments involve sending data to non-premises LLMs. Even if AI companies promise not to use enterprise data for training, you are still sending data to a system you do not control, triggering privacy, regulatory, and governance issues.

There are scary stories: McDonald's lost hundreds of dollars on McNugget orders and mixed bacon into ice cream. UT MD Anderson Cancer Center lost $62 million on a Watson deployment. Deployment is risky; you need to be very strategic and deliberate.

Payoff Strategies

1. Start with Reality, Not Ambition

Look at current business processes that take too long, are not responsive, are too expensive, or break all the time. These problem areas are obvious. Start with targeted improvements that can pay for themselves.

2. Choose the Right Starting Points

Select internal processes that are expensive to run, occur frequently, and follow predictable patterns. Workflows that leak revenue, create bottlenecks, or depend on repetitive manual effort are strong candidates. Avoid areas with edge cases, ambiguity, or constantly shifting rules. Start with non-critical systems where mistakes are manageable.

3. Put Guardrails in Place

Before moving from testing to production, implement guardrails. Keep humans in the loop for approvals and exception handling. Increase autonomy gradually. Continuously monitor behavior and costs. Mudit Garg from Qventus advises, "Organizations need adaptable governance that evolves as AI advances."

4. Scale What Works

Start with a single workflow. Demonstrate clear, measurable ROI. Expand into closely related processes. Only after proving multiple projects reliably, scale more broadly across the organization.

5. Measure Real Payoff

Talk to your people. Look for reductions in cost per task, faster cycle times, fewer errors, and measurable revenue captured or recovered. Garg states, "Success requires defining measurable outcomes early and prioritizing fewer, high-impact use cases."

What Not to Do

Do not start by attempting a full transformation. Do not deploy across multiple systems at once. Do not assume vendor promises are factual. Do not let anyone force you to move faster than your organization can absorb.

The companies that win with agentic AI will implement solutions where they succeed—sometimes deriving incremental cost savings, sometimes hitting home runs. Start with targeted improvements. Learn what works, what breaks, and what scales. Then expand those wins into broader systems. Agentic AI amplifies strengths of high-performing organizations and dysfunctions of struggling ones. Move carefully to avoid unleashing an untethered beast into your business model.

Source: ZDNET News