/ / AI for Your Business—Is It Core?  How to Do It Right: An Angel Investor’s Perspective
/ / AI for Your Business—Is It Core?  How to Do It Right: An Angel Investor’s Perspective

AI for Your Business—Is It Core?  How to Do It Right: An Angel Investor’s Perspective

AI for Your Business—Is It Core? How to Do It Right: An Angel Investor’s Perspective

Everyone wants AI – but is it right for your business? Artificial intelligence (AI) is disrupting virtually every industry. Sure, there are some inarguable benefits: automation, smarter decision-making, and personalized customer experiences are just a few. But as startups and established businesses rush to integrate AI, I often find myself asking founders if they’ve thought it through – and, often, the answer is they have not.  Here’s why it’s critical to ask yourself: Is AI core to your business? And, if it is, are you doing it right?

4 Steps To Take When Evaluating if AI is Right For Your Company

AI is powerful, but implementing it effectively isn’t as simple as plugging it into your system and reaping the rewards. Like every other business decision, it has to align with your core business objectives and an airtight strategy for execution. Here’s how I’d approach it.

1. Assessing AI’s Role in Your Business: Is It Truly Core?

Ask yourself: does AI directly support the core mission and objectives of my business? AI should not be treated as a “nice-to-have” feature or a buzzword to impress investors. Instead, it should be a deliberate and strategic choice that directly contributes to your value proposition or operational efficiency.

There are two scenarios where AI is typically core to a business:

  • AI as the Product or Service: In some cases, AI is the business itself. Startups that develop AI-driven tools—such as predictive analytics platforms, machine learning models, or AI-driven automation systems—fall into this category. For these companies, AI is not just a component; it is the foundation of the business model.
  • AI as a Key Enabler of Your Business: In other cases, AI is a critical enabler of your core business processes – it could power your recommendation engine, drive customer engagement through chatbots, or optimize logistics and supply chain management. It’s not the product, but it’s central to how you deliver your product or service effectively.

If AI isn’t aligned with your core business goals, forcing it into your operations can lead to wasted resources and misaligned priorities. AI should be embraced if it is crucial to scaling your business, enhancing customer value, or enabling innovation—not simply because it’s the trend of the day.

2. Understand Your AI Capabilities: Do You Have the Right Expertise?

If you still feel that AI is core to your business, the next step is making sure you have the capabilities in place to support it. AI is complex, requiring a blend of data science, machine learning expertise, and a deep understanding of your industry-specific needs. Without the proper talent and infrastructure, AI projects are prone to failure or, at best, suboptimal performance.

Here are a few considerations:

  • Hire or Partner with the Right Experts: One of the biggest mistakes I see startups make is underestimating the expertise needed to build and implement AI solutions effectively. Hiring data scientists, machine learning engineers, and AI specialists is crucial. If hiring full-time staff is not feasible, consider partnering with AI-focused companies or research institutions. The right talent is essential for translating your AI ambitions into reality.
  • Invest in Training and Development: AI is an evolving field. Ensure your team has access to continuous learning opportunities to stay ahead of the curve. This might mean encouraging your engineers to take courses in deep learning, natural language processing, or other AI subfields that are relevant to your business.
  • Understand Your Data: AI relies on data—lots of it. Before you dive into building AI models, you need to assess the quality and quantity of the data at your disposal. Is your data clean, well-organized, and relevant to the problem you are trying to solve? Poor-quality data will lead to poor-quality AI outputs, no matter how sophisticated your algorithms are.

3. Focus on Solving Specific Problems with AI

Don’t try to use AI for everything. Focus on specific problems where AI can deliver the most value. It’s easy to get caught up in the hype and attempt to apply AI across the board, but this approach can dilute your efforts and underwhelming results.

The best AI implementations start by clearly identifying a problem that AI is uniquely suited to solve. For example:

  • Customer Personalization: AI excels at analyzing large volumes of customer data to deliver personalized recommendations and experiences. If personalization is core to your customer engagement strategy, AI can help you deliver relevant content, products, or services at scale.
  • Operational Efficiency: AI can automate repetitive tasks, optimize workflows, and predict outcomes based on historical data. If operational efficiency is a key business driver, AI might be core to streamlining your processes, reducing costs, and increasing productivity.
  • Predictive Analytics: Businesses that rely on forecasting—predicting customer demand, pricing trends, or equipment failure—can leverage AI’s predictive power to gain a competitive edge. If you’re in an industry where future outcomes drive decision-making, AI can be a game-changer.

By narrowing your focus to specific, high-impact use cases, you’ll be more likely to see tangible benefits from your AI initiatives.

4. Build for Scalability and Long-Term Value

The potential of AI isn’t fully realized until it’s applied at scale. It’s one thing to run AI models in a small pilot, but another to implement them across your entire business. Scalability should be at the forefront of your AI strategy from the beginning.

  • Invest in Scalable Infrastructure: AI requires significant computational power, storage, and data processing capabilities. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure offer scalable AI services that can grow with your business. Be sure to build your AI infrastructure with future growth in mind.
  • Measure and Optimize Performance: Like any business initiative, AI requires ongoing measurement and optimization. Monitor the performance of your AI systems, track their impact on key business metrics, and make adjustments as needed. AI models can degrade over time as new data is introduced, so continuous refinement is necessary to maintain accuracy and effectiveness.
  • Ethics and Responsibility Matter: As AI becomes more central to your business, it’s important to consider the ethical implications of its use. AI can introduce biases, affect decision-making in unintended ways, and raise concerns about privacy and security. Make sure your AI strategy includes safeguards to ensure fairness, transparency, and accountability.

Making AI work for your business, Not the other way around

AI can be a transformative force for businesses, but it’s not a one-size-fits-all solution. It has to align with your core business objectives, be implemented with the right expertise, and solve specific problems that deliver measurable value. As an angel investor, I advise founders to approach AI with ambition and caution. Done right, AI can unlock new levels of growth, innovation, and efficiency. But without a clear strategy, it’s just an expensive distraction.

So, ask yourself: Is AI core to your business? And, if it is, are you prepared to do it right? With the right approach, AI can be a powerful tool that drives your business forward into the future.

Enjoy the ride.