You may have missed it, but there’s no doubting the headlines about AI transforming business.
Apparently, almost all of our competitors are automating everything. And if you don’t have AI in place, you’ll be left behind.
But there are unshared depths beneath the headlines.
Startups and small businesses that implement AI do not necessarily experience miraculous transformation. Others stumble at awkward middle ground: false starts, rapidly dwindling budgets, and tools that don’t work together.
But what you may be surprised to learn is that this can be necessary, and even healthy, within your organization.
find a pattern
When you talk to business leaders and managers, you’ll see a pattern emerge. Those who struggle with AI make three common mistakes:
- There is no clear owner. AI tools are added to the stack because it seems like the smart thing to do, right? But no one is responsible for the outcome. Marketing may explore one tool and operations may implement another. Managers are stuck integrating everything manually.
- Data confusion. Customer information is stored in places like Stripe, HubSpot, Microsoft Excel, and someone’s personal to-do app. AI tools require clean, structured data to work. Garbage in, garbage out is one of the oldest adages in computing, and unfortunately, it hasn’t changed in the age of AI.
- Unrealistic expectations. The team hopes that AI will solve problems they haven’t yet identified, or at least not clearly defined. They try to buy into a tool before they understand the process the tool is supposed to improve.
what actually works
Successful AI startups are doing three different things.
They start small, specific and certain. One founder automated invoice processing before tackling customer segmentation. This can be easily done through accounting apps like Sage Accounting and its AI assistant, Sage Copilot.
Another company built simple meeting notes and a knowledge base using only Microsoft Copilot, the built-in AI tool in Microsoft Teams.
Small victories build competency and confidence.
They clean the house first. Before implementing AI, integrate data sources and document core processes. It’s a tedious task, but it’s essential. You can’t automate what you don’t understand.
And they assign ownership. Someone on the team is responsible for AI implementation, not as a side project, but as an actual responsibility with clear metrics. Many companies are getting serious about AI at a time when they should be getting serious about it. Are you one of them too?
standby cost
But while some companies are working on AI, many are lagging behind.
Delay is not a neutral path. It feels like it makes sense, but it actually doesn’t. AI has compressed perhaps a decade of incredible developments into the last few years. It is evolving rapidly.
Ten years ago, waiting and watching might have worked with new technologies like the cloud, but it won’t work here. (And remember, most of us ended up embracing the cloud, no matter how long we waited.)
While you’re waiting for perfect clarity or the right tools, your competitors are learning through iteration. They are gaining important skills through experimentation and implementation. They are building organizational capabilities around experimentation, data hygiene, and process improvement.
Don’t run away from this under the wrong impression. Companies that found success with AI were the beginnings of disruption. Admittedly, they had some false starts. But at least they’re starting.
Why we need a shift in thinking
Remember, successful AI adoption is not just about finding the perfect tool at the right time.
It’s about building a culture that can quickly absorb new technology. It values experimentation over perfection, clarity over complexity, and iterative learning over big bang launches.
Your first AI implementation will likely be disappointing. That’s fine. The fifth one works better. Your 10th may actually change something.
The question is simply whether we are building the organizational capacity to implement it effectively. Start now, not someday.
