There has never been a louder time to buy AI.
Every week, there is a new platform, a new demo, a new âgame-changingâ feature, and a new sales pitch promising to transform your business, your workflow, your productivity, your content, your data, your meetings, your inbox, and possibly your entire personality by Thursday.
Some of these tools are genuinely impressive. Some are useful. Some are probably going to become a meaningful part of how organizations work.
And some are expensive wrappers around the same large language models everyone else is using.
That is not meant as a criticism of AI. I am very pro-AI when it is applied thoughtfully. But the current AI landscape rewards curiosity more than brand loyalty, and it rewards intentional testing more than long-term commitment.
Right now, the smartest organizations are not necessarily the ones signing the biggest AI contracts. They are the ones learning how to evaluate AI tools responsibly, test them with the right people, and build workflows that can survive when the market shifts again.
Because it will shift again.
AI Is Real, But the Market Is Still Messy
The challenge with AI adoption is that two things can be true at the same time:
- AI can be incredibly powerful.
- Many AI implementations can still fail.
Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing issues like poor data quality, weak risk controls, rising costs, and unclear business value. By 2026, Gartner said that figure had reached at least 50% (Source).
MITâs 2025 âGenAI Divideâ research found something equally sobering: despite $30 to $40 billion in enterprise generative AI investment, only about 5% of integrated AI pilots were producing major measurable value. Most had little or no measurable profit and loss impact (Source).
That does not mean AI is failing. It means AI adoption is hard.
It also means organizations should be very careful about confusing a great demo with a great implementation.
A polished sales presentation can make almost any AI tool look magical. The real test is what happens when that tool meets your actual business environment: your data, your team, your workflows, your approval processes, your legal requirements, your customer needs, and your internal comfort level with change.
That is where the magic either becomes useful, or where the trick falls apart.
A Failed AI Pilot Is Not Always a Failure
One of the more useful recent examples comes from McDonaldâs.
McDonaldâs tested AI drive-thru ordering with IBM across more than 100 restaurants beginning in 2021. In 2024, the company ended the test after accuracy issues became clear (Source).
Some people framed that as an AI failure.
I see it differently.
That is what a responsible pilot is supposed to do.
McDonaldâs identified a use case, tested the technology in the real world, learned from the results, ended a vendor relationship that was not delivering the needed outcome, and continued exploring AI through other initiatives, including work with Google Cloud.
That is not failure. That is discipline.
The goal was not to marry one AI vendor forever. The goal was to improve a business workflow.
That distinction matters.
In a market moving this quickly, organizations should not commit too deeply to one platform before they understand whether the tool can support the workflow, whether employees can realistically adopt it, whether the business value is measurable, and whether the vendorâs roadmap aligns with where the organization needs to go.
Commit to the Workflow, Not the Vendor
This may be the most important mindset shift in AI strategy right now: Commit to workflows, not tools.
The tool is the interface. The workflow is the asset.
When organizations understand the âhowâ behind a process, they become more resilient. If one tool falls behind, pricing changes, security concerns emerge, or a better competitor appears, the organization can adapt.
But if the organization treats the tool like a magic wand, it becomes dependent on that vendorâs interface, roadmap, limitations, and pricing model.
That is where AI adoption can get risky.
The danger is not just choosing the wrong tool. The danger is building an entire operating process around one tool before you know whether that tool is durable enough to support the business long term.
The Rise of AI Aggregators
Another reason to stay flexible is that the AI market is moving toward aggregation.
More tools are emerging that allow users to access multiple models from a single interface. Instead of forcing users to pick one model or platform, these tools let teams test different models for different use cases, compare outputs, and build workflows that are not fully dependent on one provider.
That matters because the leading AI models are constantly leapfrogging each other.
One model may be better for writing this month. Another may be better for coding. Another may be better for research. Another may handle long documents more effectively. Then the next update comes out and the leaderboard changes again.
It reminds me of one of those carnival games where you spray water into a target and watch little cars race across the track. One car jumps ahead. Then another catches up. Then another takes the lead. Then the order changes again.
Except with AI, there is no finish line.
That is why becoming overly loyal to one AI tool, especially this early, can be limiting. Familiarity is good. Dependency is not.
Better AI Onboarding Starts Before the Contract
The demo and evaluation phase should not be treated like a formality. It should be treated like the first stage of implementation.
That means bringing the actual users into the process early. Not just executives. Not just procurement. Not just the innovation team.
The people who will use the tool every day need to test it against real scenarios.
- Can they understand it?
- Can they imagine using it?
- Does it fit how they already work?
- Does it improve the process, or does it create another system to manage?
- Does it produce outputs that are usable, editable, accurate, secure, and worth the effort?
This is also where organizations can start testing enablement. If employees are confused, skeptical, overwhelmed, or unclear on the value during the pilot, that is useful information. It may mean the tool is wrong. It may mean the onboarding plan needs work. It may mean the use case is not clear enough.
All of that is better to learn before signing an annual or multi-year agreement.
The Consumer Lesson Applies Too
This mindset is not just for companies.
Individuals should also avoid premature AI loyalty.
If you are testing mainstream AI tools, consider subscribing for a month instead of committing to a full year just to save a few dollars. Go in with a plan. Test the same use cases across multiple tools. Try your real workflows. Save your prompts. Compare the outputs. Notice which tools make you faster, clearer, more creative, or more confident.
Then cancel and test another.
That may sound inefficient, but in this stage of the market, it is one of the best ways to build real AI literacy. The point is not to become a fan of one brand. The point is to understand what these tools can do, where they fall short, and how they fit into your own process.
The Real Strategy Is Staying Nimble
AI is not a passing trend. But many AI tools will be. That is the tension organizations need to manage.
The opportunity is real, but so is the noise. There are powerful tools in the market, but there are also overlapping platforms, inflated claims, immature features, security concerns, unclear ROI models, and plenty of sales pressure.
This is not a reason to avoid AI. It is a reason to approach AI with more intention.
Test before committing. Pilot with purpose. Involve the people who will use the tools. Study the roadmap. Understand the workflow. Measure the value. Stay flexible.
The organizations that succeed with AI will not simply be the ones that pick the ârightâ vendor in 2026.
They will be the ones that build internal knowledge, habits, governance, and adaptability to keep evolving as the technology evolves.
Because the biggest risk in AI is not choosing the wrong tool. It’s becoming so dependent on one tool that you lose the ability to change when something better comes along.
Sources & Further Reading
- Gartner: 30% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025
- MIT Project NANDA: The GenAI Divide: State of AI in Business 2025
- Associated Press: McDonald’s Ends AI Drive-Thru Test with IBM