The moment businesses hear about AI, they seem to try everything. What they fail to realize is that this anxiousness does more harm than good. One cannot have a bunch of tools but no planned strategy and expect great results.
However, there is some good news. Preparing your business for AI is not as technical as it seems. All you need is a dedicated plan and smart execution. Read along as we discuss this in detail, with numbers and real-time examples, minus the whimsical promise of overnight results.
Stop Trying Ten AI Tools at Once, Pick the One That Moves a Real Number
The problem is not AI but how you are approaching it with all the unchecked access. To begin with, your writers are relying on ChatGPT. HR is hiring through some other AI; Now your designers need another premium subscription! It is an endless list.
So, what can you do? Forcing them to use the same tool doesn’t make sense. Yet, relying on multiple tools seems a bit scattered.
This is where a study published by PWC comes in with a solution. It says that relying on hundreds of small AI ideas doesn’t really make sense. Instead, one should:
- Pick 2 or 3 important business problems where AI can help create huge value.
- Make sure all the company's best resources are working on them.
- The idea is to completely redesign those workflows using AI.
- Once those projects succeed, expand AI to the rest of the company.
For example, imagine an e-commerce company. Now, instead of letting marketing use AI for social media posts, HR use it for writing job descriptions, and finance use it for spreadsheets, the leadership decides to use AI in one direction.
It decides to focus only on customer support or, say, inventory forecasting. They assign their best engineers, data scientists, and business leaders to redesign those two workflows from the very basics.
The next step in the process will be the moment these projects start showing reduced costs; they move on to the next vertical. For example, with AI handling customer queries, routing complex issues to human agents, and predicting inventory demand, the company will move on to the next immediate thing that needs attention.
In other words, the idea is not to try AI everywhere at once. The goal is to use it where it can create the biggest business impact and move along accordingly.
Your Data Is Probably Messier Than You Think Fix It First
Is AI not supposed to sort out the data problem for us? Well, only if you have the right numbers in the first place. We often tend to think AI will come in and save us from all the gaps and leaks. It could have, if AI were an actual human. But all it knows and understands are data points, and the moment you misplace them, it is all over.
And honestly, this is not something you can afford to ignore. Many AI pilot projects fail for this same reason. At the end of the day, we usually end up blaming the tool. But in reality, it was just working with a set of data that was never cleaned up or taken care of in years.
Therefore, before you bring in any AI tool that reads the customer records, sales numbers, or operations, you have to run a basic audit. Ask the important questions, like: Who owns each dataset? Is this data duplicated? To put it in simple terms:
Check whether your systems actually talk to each other and stay updated, or whether someone on your team is still copying numbers from one tool into another by hand. That second one happens more often, and as a founder, you know this more than anyone else.
Nobody Talks About AI Rules Until Something Breaks
Let us be honest. It does not feel that risky when you are using AI to summarize meetings or write an email or two. But actually the real problem is somewhere else. It begins when you rely blindly on AI to make decisions that can have real-life implications. Like affecting real customers, your finances, your business operations, etc.
That is why you need a few simple rules before implementing AI. The problem is that AI has been marketed to the point where many people believe it is simply there to solve every problem.
While AI does offer speed and scalability, it still needs guidance. Unless you tell it what to do, how to do it, and where to stop, it can create more problems than it solves.
This is exactly what the PwC study highlights. It says that the company that stays ahead in the AI race will be the one talking about responsible AI usage. According to the study, every business should have a few basic practices before relying on AI:
1. AI can handle the work, but there should always be a human reviewing important decisions before they are finalized.
2. Test every AI workflow before you deploy it in a live business environment.
3. Monitor your AI systems regularly so you can catch your mistakes before your customers point them out.
4. Most importantly, assign clear ownership for every AI function. Everyone should know who is responsible for performance monitoring or fixing some issue or the other. Without ownership, not only does confusion increase, but also the chance of errors.
5. The study urges you to keep records of important AI decisions so that if something goes wrong, you can trace it back to the root cause. In other words, it asks you to always have your guard up when using AI.
Stop Building a New App for Every AI Feature
You must have noticed this yourself, whether you are a founder, a writer, or a marketer. Every week, there seems to be a new AI tool promising to solve a different problem. The moment you get comfortable with one that writes reports, another one appears that can schedule meetings, take meeting notes, and do something else.
After a month or two, your team ends up using dozens of AI tools, but nothing is really in sync with anything else. The gap lies in the fact that we simply fail to acknowledge that using AI doesn’t mean your business will be automatically more productive.
In fact, it can create disconnected workflows where no one has access to what the other person is doing. Not to mention the risk of duplicate work and so on. So, before you know it, you are part of a complex system where you do not know where it begins or where it ends.
That is why, once again, if you go back to the PwC study, you will see that the report urges companies to take a different approach.
It says there is no need to treat every AI use case as a separate project. Instead, what works better is having a centralized AI setup where every tool is part of one business workflow.
To put it simply, the goal is to connect AI to your business and not have multiple sections across multiple AI tools. In other words, you must:
- Have a centralized AI platform instead of managing multiple scattered AI projects.
- Reuse AI templates and workflows instead of creating new solutions every time.
- Make sure AI tools work together and share data across different business verticals.
- Most importantly, every AI tool should have the same business goals, ethics, and the ultimate workflow process.
In other words, it does not make sense to have a large number of AI tools if they are not producing results. The businesses that benefit the most are the ones where AI supports the entire business instead of just an individual employee or a single team.
Your Team Cannot Use AI Well if You Never Taught Them How
This is probably the easiest problem to solve, yet it is also one of the most common problems in every digital marketing setup. Imagine you announce to become an AI-first company, but you never define what AI-first actually means.
Now your content writer relies on ChatGPT to write blogs. Your SEO executive uses another AI tool for keyword research. Your designer uses a different platform. Somewhere down the line, your campaign manager is using another AI tool to analyze performance.
We know we have already discussed how every AI tool should contribute to business growth. But if you do not tell the people using those tools how to achieve those goals, you will end up stuck in the same loop.
Everyone is using AI. Everyone is working toward the same business goal. But nobody is using it the same way.
In real life, it looks something like this:
- Your blogs have a completely different writing style.
- Your SEO team follows a very different strategy.
- Confidential information gets uploaded to AI tools without any checks.
- Some employees trust AI too much, while others remain skeptical of it.
Overall, you end up in a mess simply because you decided to become an AI-first company without defining a clear workflow.
Teaching your team to use AI does not mean teaching them which buttons to click. It means teaching them the right approach, the right way of thinking, and the right workflow for using AI.
That is why it is important to sit down with your team and give them the right training before expecting results. It can include simple things like:
- Which AI tools has your company actually approved for use?
- What kind of tasks is AI allowed to help with?
- What kind of information should someone be allowed to upload to AI?
- Who is responsible for reviewing the content AI generates?
You cannot expect a miracle the moment you sign up for a premium AI tool. It takes a clear workflow, an organized thought process, and a lot of patience.
Do Not Wait for a Client to Ask About Your AI Policy
As a founder, marketer, or writer, you have to be clear about one simple thing. AI is not just another software subscription.
The moment your team starts using AI, there will be a lot of questions that come along with it. For example, can employees upload client information into ChatGPT? Should financial data be shared with AI? Who is responsible if AI generates the wrong answer and it causes real-life implications for a customer? And what exactly are we approving at this point?
Now, if you have no one answering these questions for you, your clients will catch up to this gap.
That is why, the moment you start relying on AI, you should set a strict set of rules. In simple terms: Unless you have a tangible framework that protects the business interests of your clients along with your own, using AI blindly is going to create more problems than the time it saves.
AI Costs More Than Just the Monthly Subscription
Lastly, and most importantly, before you jump into buying more premium AI subscriptions, you need to understand one thing. We often think the cost of AI is just a monthly subscription, but in reality, that is not how it works.
This is exactly what the Deloitte study points out. From a business perspective, using AI is no longer just about paying for a premium subscription In fact, the study found that 86% of organizations expect their AI infrastructure budget to increase over the next three years.
Why? Because the more you rely on AI, the more employees will need access to premium AI tools. You will also spend more time training people, reviewing AI-generated work, building the workflows we discussed earlier, and making sure everything is in place and everyone is in sync with one another.
So it is not only about the minimal monthly fee you pay; it is way bigger and more impactful than that. So before you jump into the AI first brigade, you need to ask a few questions:
- Does everyone on the team really need a paid AI subscription?
- Is this AI tool saving enough time to justify what we are paying for it?
- Is there a clear way to measure whether this AI investment is improving revenue, productivity, or business outcomes?
The problem is that most people miss out on treating AI as any other business investment. They ignore tallying the monthly value it creates and eventually end up in a bigger mess. It is not really about how you only have to pay a minimum subscription but how that subscription is making significant change in your business process.