The opinions of contributing entrepreneurs are their own.
Key Takeaways
- AI is a multiplier, not a solution for everything. It amplifies what already exists, and success depends on centralized data, disciplined workflows and a clear strategy.
- An AI model is only as effective as the data it is trained on. When asked to make decisions based on this fragmented information, the results are just as scattered.
- Successful AI transformations start with a specific business challenge with a measurable ROI and a clearly defined outcome.
Artificial intelligence has become the North Star guiding modern business strategy. From backend systems to customer interactions, AI is now a core part of decision making, product development and strategic planning.
It is no longer a matter of whether you will use AI, but how intelligently you will apply it.
A McKinsey from March 2025 global research found that more than 75% of companies now use generative AI in at least one business function, and that companies with executive-level oversight see better results.
But before you lean too heavily on AI, it’s worth asking yourself: are you solving the right problem, or are you just hoping AI will solve it for you?
As AI takes on more responsibilities, companies can fall into the trap of thinking that AI is a silver bullet to their operational challenges. AI is just a force multiplier, not a solution for everything. It reinforces what already exists, whether that’s a solid foundation or a series of inefficiencies.
Related: Where Startups Go Wrong When Working with AI – and How to Avoid These Mistakes
The mirage of instant transformation
Much of the hype around AI comes from fast-growing startups with eye-watering valuations, with lean teams and streamlined operations.
Consider the growth of these AI-first companies: Cursor generates $500 million in ARR at a valuation of $9.9 billion; Perplexity has reached $200 million in ARR with a Valuation of $20 billion; and Anthropic leads by a staggering margin Valuation of $183 billion.
These are not overnight victories. They are built on centralized data, disciplined workflows and a clear strategy. If you want AI to deliver meaningful results, start by cleaning up your internal operations. That starts with your data, which must be structured, centralized and accessible. Your sales, customer, and operational data should all be in one place where AI tools can easily work with it.
Next, look at your processes. If you haven’t clearly documented how your business runs, whether it’s onboarding new customers or handling support tickets, AI won’t know what to replicate or improve.
And finally, expose your inefficiencies early. The better defined and structured your business, the more leverage AI can provide.
Which brings up a common problem…
Incomplete data produces inconsistent AI
Any AI model is only as effective as the data it is trained on. Think of it like a recipe: even the best techniques won’t save bad ingredients.
I’ve seen this up close in industries like restaurants, where data is spread across POS systems, reservation platforms, loyalty programs, and guest feedback tools. None of them talk to each other. When AI is asked to make decisions based on this fragmented information, the results are just as scattered, leading to inconsistent guest experiences and missed opportunities.
Related: Your AI Initiatives Will Fail If You Don’t Address This Critical Component First
AI scales what already works
The most practical use of AI today is to improve proven processes.
In marketing, AI can personalize your content, test campaigns and optimize engagement. In business operations, it can use sales trends to automate your planning or inventory planning. For customer retention, this can trigger timely, personalized follow-ups that drive repeat business.
These use cases are already delivering results across industries:
Car dealers are using AI to schedule test drives and automate financing, reducing friction in the buyer’s journey.
Real estate companies match prospects with offers and manage showings at scale, speeding up closing time.
Law firms qualify leads and make appointments in multiple languages to increase intake efficiency.
In all these cases, success depends on the underlying systems to which AI connects.
Focus on use cases with a clear ROI
The most effective AI transformations start with a specific business challenge, not with the technology itself. The question is not, “How do we implement AI?” It’s, “What can we improve, automate, or predict that would move the needle?”
That can mean you spend less time turning tables in a busy restaurant. Or anticipate shifts in customer demand in retail. For example, it could be improving support ticket routing in a SaaS company, automating test drive scheduling in automotive sales, or accelerating the matching of commercial office space to the right prospects. For global sales teams, this may even involve responding directly to leads in their native language to increase conversion rates.
What unites these examples is that they are all based on a real operational need, with a measurable ROI and a clearly defined outcome.
This approach is critical in industries such as hospitality, logistics and retail, where margins are razor-thin, labor is intensive and customer expectations leave no room for error. With the right data, AI can help companies in these sectors respond faster, reduce pressure on teams and improve the bottom line.
But it’s not just the big players that can make a profit.
Related: AI isn’t plug-and-play – you need a strategy. Here’s your guide to building one.
The AI advantage for SMEs
Small and medium-sized businesses are often better positioned to take the leap. Without the weight of legacy systems or endless approval chains, SMBs can experiment and implement AI tools with greater speed and flexibility.
And unless you operate in a highly regulated environment like healthcare or finance, you likely face fewer compliance hurdles than larger companies.
That agility is a strategic advantage.
AI is not the strategy – it is the multiplier
The winners in this next phase will be those who align AI with clear business priorities and use it to achieve measurable results, streamline operations and create a real competitive advantage.
Success with AI starts with intention. Define the business problem you are solving. Anchor your use cases in measurable outcomes and ensure that your data, no matter how limited, is accurate, accessible, and ready to power the system.
In short: don’t just adopt AI. Operationalize it deliberately.
Key Takeaways
- AI is a multiplier, not a solution for everything. It amplifies what already exists, and success depends on centralized data, disciplined workflows and a clear strategy.
- An AI model is only as effective as the data it is trained on. When asked to make decisions based on this fragmented information, the results are just as scattered.
- Successful AI transformations start with a specific business challenge with a measurable ROI and a clearly defined outcome.
Artificial intelligence has become the North Star guiding modern business strategy. From backend systems to customer interactions, AI is now a core part of decision making, product development and strategic planning.
It is no longer a matter of whether you will use AI, but how intelligently you will apply it.
A McKinsey from March 2025 global research found that more than 75% of companies now use generative AI in at least one business function, and that companies with executive-level oversight see better results.
The rest of this article is locked.
Join Entrepreneur+ today for access.
#dive #fully #question