Why Every Business Needs an AI Strategy — And How to Build One

AI has become a huge area of focus for businesses. The general view among most decision-makers is that AI has significant potential, so everyone should be using it. That is a valid point of view—AI is highly effective at performing certain tasks for businesses. But unless you have a plan or an actual strategy, you could be setting yourself up for failure.

Building an AI strategy for business isn’t just about jumping straight into the latest tech trends or spending a boatload of money on tools. It's about designing a practical plan that uses AI to solve your real business problems, not hypothetical ones you think need to be solved. If you get it right, you’re going to see some incredible results. If done wrong, you will learn some expensive lessons.

But here's the good news: you don’t need a computer science degree to get started. All you need is a realistic idea of what AI can and can’t do for your unique business challenges.

Why Every Business Needs an AI Strategy

AI is here to stay. It's a multiplier that, when used right, can accelerate your business’s progress and leave your competitors in the dust. Here is how it can help your organization pull ahead in the market: 

Stay Competitive in the Digital Age 

Your competitors are already either experimenting with AI or rolling it out into their live systems. Think of AI chatbots, analytics, or even marketing—all of these areas are seeing strong use cases for AI enhancements. And the best part is that you don’t even need a massive technical team to manage your AI systems, putting AI in reach of even small companies. 

Unlock Efficiency and Innovation

AI handles repetitive tasks like a champ. This frees your team to focus on core business problems while the AI crunches numbers and handles automated data capture and scheduling. Reducing the time your employees spend on monotonous administrative tasks can boost their productivity in areas where human creativity is essential, such as business strategy.

Manage Risk and Govern AI Use

If you don’t set clear guidelines with AI in your business, then your employees will try to figure it out on their own. This isn’t necessarily a bad thing, but if you don’t tell them why they can’t paste the Secret_Strategy.xls spreadsheet into ChatGPT, then you are opening yourself up to risk. Unmanaged AI tools introduce security risks, so it is essential to clearly define acceptable uses within your organization. 

Align AI with Business Goals

Experimenting with AI is fun, but it doesn’t always translate into real business value. You need to start with your business objectives and align your AI strategy to achieve them—not the other way around. Aligning AI with business goals means every cent you spend on AI delivers specific results.

What are the Key Components of an Effective AI Strategy?

A solid AI strategy requires careful planning and analysis. Here are six components that you should be thinking about for your very own AI strategy.

Vision and Objectives

Start with an idea of what you actually want AI to do in your business before you commit to spending any money. Look at the top five pain points in your current processes that you think AI can help you solve. Look at your bottlenecked areas like invoicing and customer service, and see how AI could help you automate and speed up the processes that your staff are struggling with. Be specific about your goals and start measuring them to track your progress.

Data Infrastructure and Governance

AI loves data. If your data is sprinkled around in different systems with different formats, you will need to think about how to collate all that data into a central point so that your AI systems can access it. If that isn’t possible, consider creating MCP tools for your AI to use each endpoint as a dedicated tool, enabling it to know where to look for each type of data it needs. The idea is to improve your data quality over time so your AI can use tokens and compute resources more effectively without wasting your budget.

Technology and Tools

Once you have an idea of how you would like to use AI in your business, you’ll need to start researching which platforms and tools will work best for you. Don’t get caught up in the latest hype around AI—focus on the tech stack that will work for your exact needs. Shiny object syndrome is real, so don’t be distracted by every new development that floats across your news feeds. Start small with proven providers and scale up as your proof-of-concept plans show potential. Here's our list of top AI tools for developers as well as the best AI tools for business leaders.

Talent and Skills Development

Even though AI platforms like ChatGPT and Gemini are deceptively easy to get started with, there are new skills that you’ll need to learn to get the most out of them. Not everyone has to become an AI engineer or computer scientist. Still, fundamental AI capabilities will move the needle for general AI adoption in your business, such as prompt engineering training. Ensure you have AI knowledge champions who are interested in AI and are available to share information with their teams to accelerate upskilling across your departments.  

Change Management and Culture

AI has already changed how we get work done. Some people will dive into AI adoption headfirst and automate as much as they can from day one, while others will be resistant and unsure where to start. Make sure that you offer support to help people who are struggling with what AI is, and what they are supposed to do with it at work. You need to stay on top of communication with your teams and help them transition to this new hybrid intelligence model we all find ourselves in.

Ethics, Compliance, and Risk Mitigation

AI isn’t perfect. It can introduce subtle bias and security issues without you even realizing it. Your overall AI strategy must include ethical guidelines for the fair and responsible use of AI. You will also need to consider how you plan to monitor AI usage within the business and what is off-limits for sharing with an AI model. If you are in a regulated industry, you need AI compliance training to set expectations for your employees and clarify what they can and cannot enter into the AI models you provide.

How to Build an AI Strategy: A Step-by-Step Guide  

Here comes the hard part—implementation! Planning and strategizing are one thing, but how are you actually planning to roll out AI across the rest of the organization? The best way is to break everything down into bite-sized chunks, so you won’t feel overwhelmed when it comes time to start executing on your plans. 

Step 1: Self-Assessment

You can’t look at where you are heading if you don’t know where you are. Take stock of what is working well and what isn’t when you measure your current AI attempts. What data do you need to use? Which systems are you planning to use for this data? Are there processes that need to be automated, and can that data help you get there? 

These questions will help you refine your efforts and focus on real-world benefits you can tackle first. This baseline assessment will help you get started. Understand what you are trying to do and what you will need to get started before you start allocating a budget for AI tools.

Step 2: Aim for Big Impact

The best way to get everyone on board with AI is to blow them away with the ‘wow factor.’ Look at tasks that everybody dreads, or mind-numbing repetitive tasks that nobody wants to do, and automate them. Things like customer service and administrative housekeeping come to mind. 

Consider what could benefit everyone in the company if it were implemented automatically. Think about how much time this would free up each day, week, and month—and then focus on that. Starting with these tasks will get more people excited about AI and help you build momentum for larger projects.

Step 3: Choosing the Right Techstack 

Integration is a massive part of getting AI into your systems. The platform you choose should be able to integrate with your existing systems without extensive technical modifications. Do you want a self-hosted AI, or are you looking at a cloud provider? 

Think about data privacy, and if your budget will allow you to spin up private instances of different AI models so that your data won't be trained on. Make the systems as user-friendly as possible to maximize adoption rates.

Step 4: Governance Policies

As tempting as it is to release AI to the whole company all at once, you need to think in practical terms about how to govern AI use. AI feels like the Wild West sometimes, where everyone is running amok, and there’s no sheriff in town. 

You need to be specific about who can roll out AI tools to users across the company, and those tools must be sanctioned. Data shared with public AI models could be used in their future training sets, so everyone needs to understand what can and can’t be shared with specific models. 

Step 5: Planning and Implementation

You’ve done all your research, and you’ve settled on an AI platform—great job! But now comes the hard part: implementation. Starting small is the best way to gauge how useful your AI solution will be without incurring high upfront costs. Measure results from small experiments and expand your user count as you start seeing results. This will reduce your risk and let you learn as you go. Remember to budget for training, not just your AI platform, for best results.

Step 6: Measure Success 

The only way to truly gauge how well your AI investment is doing is to measure some clear metrics. You will want to measure against real-world business objectives, such as tracking how many hours your team spends each week on manual work, then compare that against a hybrid approach to see how much time is actually being saved. Productivity closely correlates with savings, so measure those wins and keep tracking your successes (and failures).

What Roadblocks to Watch Out For

It isn’t all smooth sailing when you are implementing something as game-changing as an AI strategy. But with a bit of preparation, you will be ready for some of the growing pains that come along with it. 

• Overpromising and Underdelivering: The AI hype is very real. Tech demos don’t always materialize in the real world, so don’t trust the marketing campaigns until you have access to the AI for yourself. Start with modest, achievable goals, and ramp up your capabilities once you have found your stride.

• Lack of Executive Buy-In: Not everyone will be as enthused as you are at the prospect of spending more money on new tech. AI adoption needs to be supported from the top down, especially given the resources you will need to get your project off the ground. Make a business case, show some figures, and take the execs on the journey with you.

• Data Silos and Poor Quality: AI needs access to as much data as you can throw at it to train your own models. If you don’t have access to all the data you need, your solution will fall short of your expectations. All of your departments need to be on board and have the required data available for your AI to perform at its best.

• Skill Gaps and Resistance to Change: Teams encountering AI for the first time may be unsure how to use it. This is a great opportunity to upskill and train your departments on how to use AI and how it will make their work easier. For those who have the initiative, Agentic Coding training is a game-changer. It allows non-software developers to build custom tools and apps that make sense in their specific areas of the business, unlocking incredible potential.

• Ethical, Privacy, and Regulatory Risks: AI can be a significant challenge for compliance requirements, especially regarding privacy and bias. Keep on top of your specific regulatory burdens, and ensure your solution accounts for all requirements before you enable AI across the organization. 

How to Measure the Success of an AI Strategy for Business

How you measure success will depend on what you are trying to achieve in your business. Don’t track only one or two metrics; look at the bigger picture and track as much data as you can. You might be surprised at the domino effect that operational efficiencies in one area can have on other parts of the business. 

Key metrics and KPIs for AI include: 

• Financial Metrics: Track savings from automation and assess potential revenue gains from process improvements. This all turns into a return on investment and shows how AI is benefiting the business. Be cautious with your calculations and remember to include all your costs.

• Operational Metrics: Track productivity improvements and error reductions in your processes. Speed increases are an excellent way to demonstrate how well AI is improving daily operations. Time savings are among the most impactful metrics to track. Teams can showcase new initiatives they have launched, leveraging AI to eliminate the monotonous, repetitive work that used to bog them down.

• Qualitative Metrics: Collect feedback from your users and customers if you offer client-facing AI services. Track your wins and compare manual processes with the automated AI solutions you have rolled out. These numbers speak to management teams and show how well the solution is doing with efficiency gains and time savings. Also, track any negative feedback; if something isn’t working as expected, now is the time to fix it.

Conclusion

Successful AI strategies are a balanced mix of ambitious plans with practical expectations. Start out small with achievable, low-hanging fruit, and work your way up to mindblowing projects. The secret is to keep building on each success.  

Want to learn more about implementing AI responsibly in your organization? Check out this AI fundamentals training to build the skills and knowledge your team needs for real-world AI strategy execution. 

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