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ZDNET’s key takeaways

• Not all “agentic AI” tools are truly agentic systems.
• Poor prompts and rogue agents can cascade into failures.
• Focus on measurable outcomes, not hype or ambition.

Imagine you’re a chief executive. Your AI strategy task force has just presented you with two strategic options.

The first one is safe. You can use agentic AI to reduce overhead and save 10% of overall human capital costs.

The second choice is daring. You can increase growth tenfold by using agentic AI to transform your company’s operations.

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The first choice will barely move the needle, but will help the AI initiative pay for itself. The second choice could blow the doors off your numbers and make you a legend in your board’s eyes. It could also get you fired.

Know that the superlatives are off the charts. KPMG estimates that agentic AI will unlock $3 trillion in annual productivity gains. Accenture makes the case that agentic AI is “no less than a new type of capital,” and “marks a shift in economic history.” Last fall, Gartner said, “organizations have a crucial three- to six-month window to define their agentic AI product strategy, as the industry is at an inflection point.”

So, what do you do?

Risk factors

Gartner may advise that you need to take action right now. Accenture advises you to go for 10x growth wins rather than 10% cost-savings wins. My advice is to be chill. While there is undoubtedly a ton of upside to agentic AI initiatives, jumping in without a solid strategy can result in failure.

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As it turns out, Gartner has a stat for that, too. The research said, “Over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.”

There are other reasons for these failures. Gartner said that most early-stage projects are experiments or proof-of-concept, which is as it should be. But these sorts of tests are just that. Tests are not guaranteed to succeed. That’s the point.

1. AI washing

On the other hand, organizations are often led astray by their vendors. Many vendors, jumping on the AI hype wagon, are engaging in what Gartner called “agent washing.” No, this isn’t James Bond in a shower. It’s a term derived from greenwashing, the practice of falsely portraying products as eco-friendly.

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In the case of agent washing, Gartner estimated that less than 13% of the thousands of agentic AI vendors are actually shipping agentic products. Most companies are rebranding existing products — ranging from AI assistants, robotic process automation, script-based services, and chatbots — as “agentic.” The assumption that these tools can perform autonomous tasks is faulty, leading to pilot projects based on these products that are destined to fail.

2. Runaway costs

Another gotcha is costs. Most AI implementations rely on external large language models for cognitive processing services provided by the likes of OpenAI, Google, and Anthropic. These services get linked to your applications through an application programming interface (API).

Think of the API like the socket in your wall. You plug your coffee maker into that socket, and you get power to generate that sweet, sweet brown elixir. The socket and plug are standardized interfaces (like the API). Your coffee maker is your application. The cloud service is the power company, to whom you pay a fee for usage.

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AI companies measure metered usage based on a metric called “tokens.” Generative AI uses tokens fairly sparingly. They’re consumed when a question is asked, and that’s it. Like a coffee maker making a cup of coffee, the power/token usage is minimal.

Now, contrast the power demands of a coffee maker to that of a server rack. The servers consume more power and use it constantly, 24/7. The power bill for a server rack will be considerably higher than for a coffee maker (even my overused coffee maker).

It’s the same with agentic AI, which runs almost constantly, with multiple agents at once, consuming tokens voraciously. As companies scale up their use of agentic AI, they’re finding their cloud bills are ballooning. There’s a reason OpenAI went from zero revenue in late 2022 to more than $20 billion in 2025.

3. Unpredictable results

Another pitfall is that AI projects are “non-deterministic,” meaning the same input can produce different outputs across runs, because the AI incorporates probability, randomness, and context sensitivity rather than following a fixed, repeatable execution path.

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This lack of predictability can be brutal when building and testing solutions, debugging failures, validating outputs, ensuring compliance, and maintaining consistent behavior across updates and deployments.

Madhav Thattai, EVP & GM of Agentforce at Salesforce, told me this in an email: “Software used to be solely deterministic: same input, same output, easy to trust. AI agents break that model, with the same input producing different outcomes. That demands a hybrid approach. Context, control, and governance can’t be bolted on post-deployment. The companies succeeding are designing those layers in from day one.”

4. Rogue agents

Think about what could happen when a trusted employee goes bad. The same could happen with agents, except agents are far faster than any employee. An unintended action, done at scale, can ripple through your entire organization at light speed.

My mom used to have a saying that frustrated me throughout my entire childhood. She said, “Do what I mean, not what I say.” Her expectation was that she was raising me right, so I should really know what she wanted, regardless of whether or not she articulated it correctly.

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Goal misalignment can be a real issue if an employee prompts an agent incorrectly. While you could probably create a checks-and-balances agentic supervision system, the more probable reality is that if you prompt the agent incorrectly, it won’t intuit your intent. It will just blast through your network, leaving rubble in its wake.

If you have a misinstructed agent somewhere in your logic chain, those failures will cascade into others, creating a domino effect that can leave you wishing you could hide out in the forest in a yurt for the next two years (or maybe that’s just me).

5. Data security and privacy risk

Security and privacy is another issue. Almost all deep AI agentic deployments involve using a non-premises LLM. This means that your data has to be sent to the AI somewhere in the cloud.

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The big AI companies do promise they won’t use your enterprise data for training, but the fact is, you’re still sending data to a system you don’t control. This could trigger all sorts of privacy, regulatory, and governance issues. Be sure to dig deep here before making any permanent implementation decisions.

I could go on and on about risk factors. There are some scary stories out there. McDonald’s lost hundreds of dollars on McNugget orders and also mixed bacon into ice cream. UT MD Anderson Cancer Center lost $62 million on a Watson deployment.

I’m not trying to scare you away from agentic AI. I want you to understand that deployment is risky. You need to be very strategic and deliberate. This is not a shiny new toy. This is a bet-your-company risk and opportunity.

Payoff strategies

You know what they say. “No risk, no reward,” right? We’ve discussed the risks, so now let’s look at how to reap the rewards of agentic AI installations.

Accenture identified a tiered approach to AI projects.

• Tier 1 – Agentic automation: This is the base level of AI implementation. Here, Accenture is talking about point solutions or what they call “simple human substitution.” This is where you might augment tech support with a subject-matter trained chatbot, or put an agent on the task of processing certain forms or inputs.
• Tier 2 – Table stakes: This is Accenture’s term for end-to-end process reinvention, designed to unlock value. The idea here is that you can save a lot and increase overall output, but you’re not differentiating your business from competitors.
• Tier 3 – Strategic bets: Yep, they said “bets” in a strategy statement. Accenture is pitching the idea that if you take a big chance, you might get back big rewards using their 10x metric. This is essentially reinventing your business based on AI capabilities.

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Is this approach practical or attainable? Sure. Maybe. As much as anything, I guess.

I think this pattern of so-called “strategic” analysis of AI opportunities is meant to generate excitement rather than tangible results. Accenture even said (and this is a direct quote), “If the company’s agentic AI agenda doesn’t excite investors, the ambition is not bold enough.”

1. Start with reality, not ambition

Let’s lift up on the gas pedal a little bit, shall we? Going full throttle right out of the gate will likely find you skidding off the road. Instead, use care and consideration. You can still find payoffs. Just do so in a way that has a better chance of overall success.

Start by looking at your current business processes. Almost all businesses have some processes that take too long, aren’t responsive enough, are too expensive, break all the time, or otherwise cause headaches. You don’t even need to do a business-wide deep dive analysis. These problem areas are, and have been, obvious for a long time.

2. Choose the right starting points

Be selective about your choices for trying agentic AI. Look for internal processes that are expensive to run, occur frequently, and follow fairly predictable patterns. Workflows that leak revenue, create bottlenecks, or depend on repetitive manual effort are especially strong candidates.

Proceed carefully when using agentic solutions to replace manual labor. You don’t want to scare employees that they’re going to lose their jobs. Instead, you want to empower employees to make deeper contributions by freeing them up from doing tedious busy work. Start with non-critical systems where mistakes are manageable and won’t ripple across the business.

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Look at those as low-hanging fruit. Some might be fixable using task-specific agents. Others might be mitigated by multiple agents working together in a single data environment. Still others might be solvable by simple algorithmic processes that don’t need AI at all.

Avoid areas filled with edge cases, ambiguity, or constantly shifting rules. Those situations are far harder for agents to handle reliably and are more likely to create problems than deliver value.

3. Put guardrails in place

As you move from testing to production deployment, put guardrails in place. Be sure to consider and implement the guardrails before you scale.

Keep humans in the loop early on, especially for approvals and exception handling, so agents don’t run unchecked. This might be harder than the AI companies promise. When Claude Code suddenly began splitting work among agents, I found that they ran far faster than I could track, often got stuck, and were otherwise troublesome. My fix was to eliminate simultaneous agents, at least until I could better manage them.

Increase autonomy gradually as you gain confidence in performance. Don’t just rush in and try to turn on full agentic automation right away. This might require you to resist the pressures of investors and other key players, but hold your ground. You wouldn’t want to turn over your production line to the impulsive ne’er-do-well nephew of your biggest investor. Likewise, you shouldn’t hand over your process flow to AI agents before they’re ready for prime time.

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“Organizations need adaptable governance that evolves as AI advances. While human oversight remains important today, frameworks should anticipate greater AI autonomy and include clear, future-ready safeguards,” Mudit Garg, CEO and co-founder of hospital AI software company Qventus, told ZDNET in an email, “Many health systems that developed AI governance frameworks a couple of years ago are already having to restructure them to accommodate today’s AI capabilities.”

Be sure to continuously monitor both behavior and costs, because with agentic AI, small issues can compound quickly if left unattended. Here’s a corollary: If you can’t monitor something, or haven’t figured out how to yet, wait until you can before setting agentic AI loose.

Salesforce’s Thattai also had thoughts on AI governance. “Businesses are assembling agents across models, vendors, and tools. Governance has to be open and composable enough to meet them there. But openness without oversight is just sprawl,” he said. “Agents need to be built on standards with tight governance, consistent visibility, and monitoring across the entire agent lifecycle. Trust is non-negotiable.”

4. Scale what works

Once you’ve identified a viable use case, keep the initial project very limited. Start with a single workflow. Make sure you can demonstrate clear, measurable ROI. From there, expand into closely related processes where the patterns and data are similar.

Wait until you’ve proven you can reliably execute on multiple projects before you try to scale more broadly across the organization.

5. Measure real payoff

How can you tell it’s working? First, talk to your people. They’ll tell you if they love or hate the new systems. Once you’ve gotten the measure of worker sentiment, look at other metrics that can measure success in clear, operational terms. Look for reductions in cost per task, faster cycle times, fewer errors, and measurable revenue captured or recovered.

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“The biggest challenge is proving ROI at scale. Many health systems lack clear performance benchmarks and face long implementation timelines, compounded by reliance on legacy EHR systems,” said Qventus’ Garg.

Keep in mind that if you can’t tie a process to a tangible, measurable result, you can’t prove you’ve added value.

“Success requires defining measurable outcomes early and prioritizing fewer, high-impact use cases, moving from 80% to 95% accuracy rather than spreading across 1,000 shallow applications,” Garg said.

And what not to do

Keep these cautions in mind as well: Don’t start by attempting a full transformation. Don’t deploy across multiple systems at once. Don’t assume that what a vendor tells you they can do is actually what they can deliver. Don’t let anyone force you into moving faster than your organization can effectively absorb.

The path to rewards

At the beginning of this article, I gave you a choice. But it doesn’t really make sense to pick between a safe 10% efficiency gain and a risky 10x transformation. The companies that win with agentic AI will implement solutions in the contexts where they will succeed, sometimes deriving incremental cost savings and sometimes hitting home runs.

Start with targeted improvements. If all goes well, they’ll simply pay for themselves. Learn what works, what breaks, and what scales. Then, over time, expand those wins into broader systems that reshape how your business operates.

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Agentic AI is powerful. It can absolutely change a business’s trajectory. That can be for good or not so good. Back in December, I discussed how AI is an amplifier, that it “magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.”

So, what do you do? 

My recommendation is that you move carefully so you don’t unleash an untethered beast into your business model. Start with pilot projects, build on them, and slowly scale up over time. As you do, you may find opportunities that let you take your business to the next level, or even beyond.

If you could apply agentic AI to one frustrating workflow today, what would it be? Let us know in the comments below.

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