Nearly three-quarters of businesses are now using AI in at least one part of their operations as reported by McKinsey & Company in early 2024. When you think of AI, ChatGPT might be the first thing that comes to mind, but it goes far beyond that—from machine learning to predictive analytics to computer vision.With so much to explore, it’s easy for businesses to struggle with taking the first step to implementing AI.
If you’re wondering, “Where do we even begin?” or “How do we fit this with our goals?” you’re not alone. These questions often stall progress before it even starts. That’s where an AI strategy comes in. Think of it as your blueprint for integrating AI into your business in a way that makes sense from a business and operational perspective.
We get it—starting this journey is tough. That’s why we help businesses cut through the noise and create AI strategies that work. In this guide, we’ll walk you through a practical roadmap for building and using an AI strategy—whether you’re just starting out or fine-tuning your existing approach.
1. Vision: How AI Fits Into Your Business Goals
If you’re thinking about introducing AI into your business, the first step is to get clear on your goals. AI should solve real problems and create meaningful impact, not just be the latest tech trend. Start by asking what’s holding your business back. Are customer issues taking too long to resolve? Is revenue growth slower than you’d like? Identifying these challenges helps you figure out where AI can make the biggest difference.
Once you’ve pinpointed what needs fixing, think about where you’re headed. Are you aiming to expand your business or focus on innovation? For example, Visa used AI strategies like automated security checks and chatbots to tackle fraud threats while improving the customer experience. When AI is tied to a bigger vision, it can really drive results.
To achieve this kind of success, your AI initiatives need strong executive sponsorship and stakeholder buy-in. This alignment ensures everyone is on the same page about the AI roadmap—what’s needed, how it ties back to business goals, and the impact it can deliver. A unified vision not only sets the foundation for success but also accelerates execution.
If your team lacks the in-house expertise to build and implement the right solutions, partnering with AI specialists is a smart move. At this stage, we help businesses identify their most pressing challenges and implement AI solutions that save time, solve problems, and boost revenue. By working directly with decision-makers, we ensure AI initiatives align with the company’s overarching goals.
2. Value: Finding Use Cases and Justifying ROI
When exploring where AI can help, you’ll likely come up with a long list of issues you want to solve—but not all projects are equally valuable. Some might be “nice-to-haves,” while others tackle critical bottlenecks or cut costs, creating benefits across the board. This should be a first stop to define the value of AI solutions in your strategy. Another good exercise to narrow down the list, is plotting a value score (business impact) against a feasibility (ease of implementation) score for each idea. This helps you focus on high-impact, realistic projects with immediate benefits.
For example, a retail business might evaluate AI use cases like automating checkouts, optimizing inventory, or improving demand forecasting by scoring each on value and feasibility. Demand forecasting might score high on both because it uses existing sales data and delivers significant cost savings, making it an ideal starting point. By focusing first on high-value, high-feasibility projects, businesses can achieve quick wins and reinvest savings into more complex initiatives.
Another approach is to start small with low-risk, low-cost initiatives like adding a chatbot for customer support. Chatbots handle repetitive queries like “What’s your return policy?” or “Where’s my order?” and escalate complex issues to real people. Once live, measure its impact. For instance, if the chatbot answers 500 questions a month, calculate how much time and money that saves your team. Look for other wins, like fewer mistakes or reduced overhead.
Once you’ve had success with smaller AI projects, you can start tackling larger ones. A healthcare client of ours who wanted to overhaul how their customer service worked. We helped them turn their website into a platform that could handle 10,000 calls a day using AI tools. We also built a system that made sure calls went to the right person faster, which cut down wait times and improved how everything ran. This saved them money and brought in more revenue.
3. Adoption: Scale and Fine-Tune AI Initiatives
Once you’ve started using AI in smaller projects and are ready to scale, it’s not just about crunching the numbers or figuring out the next problem to tackle. You’ll need to bring in the right people—like team members in marketing or sales—who will actually use these AI tools. It’s also important to think about the technical side of things, like training your staff to work with the new technology and making sure you’re following any rules or regulations for your industry. To make this process easier, here’s a roadmap to guide you through scaling AI across your business.
Simplified AI Roadmap
- Understand Business Needs: Identify where AI can create value and build use cases aligned with business goals.
- Prioritize Opportunities: Focus on high-impact, feasible projects to maximize early success.
- Prepare Data and Models: Ensure your data is clean, accessible, and ready to support AI initiatives.
- Involve Stakeholders: Collaborate with teams across departments to align goals and secure buy-in.
- Train Teams: Equip employees with the skills to use AI tools effectively, from technical training to practical applications.
- Launch and Iterate: Start with small projects, measure results, and use feedback to refine and scale.
- Expand Impact: Use successful projects as a foundation to explore additional AI use cases.
Optimization is a key part of getting AI to work for your business, and it starts with transparent communication and figuring out how to handle changes as they come up. The goal is to have a setup that’s flexible enough to add new tools when you need them. You could even bring in a Chief AI Officer to take charge and help your team navigate any major shifts along the way.
4. Barriers: Tackling Resistance and Technical Challenges
Before getting into AI adoption, it’s good to know about some common barriers that can slow workflows down and how to handle them.
Resistance to Change
One hurdle with AI adoption is the reluctance to change. People might worry about losing their jobs, feel overwhelmed by new processes, or stress about learning new skills. The key here is clear communication. Let your team know that AI is there to help, not replace them, and explain how it’ll make their jobs easier. Pair that with great training to boost confidence, and you’ll find that initial pushback starts to fade.
Lack of Understanding
A lot of businesses know what problems they want to solve, but they’re unsure where AI fits in. Sometimes, it’s because they don’t fully understand how AI works or think it’s too complicated to implement. That’s where getting outside help comes in handy. Consulting with an experienced AI development team can give you a clearer idea of what tools to use and how to make them work for your business.
Technical Limitations
Outdated systems can pose significant challenges for implementing AI, especially if they’re incompatible with newer tools and technologies. In some cases, middleware solutions can bridge the gap by enabling older systems to communicate with modern AI platforms. However, for more complex or heavily outdated setups, a system upgrade or even a full technology overhaul might be necessary to unlock AI’s full potential and ensure seamless integration.
Overestimating AI’s Capabilities
Sometimes businesses expect AI to do way more than it actually can. For example, assuming AI can handle all creative tasks like strategy or design is a recipe for disappointment. What AI does really well is recognizing patterns and organizing big datasets. Knowing where AI shines—and where it doesn’t—helps you get the most out of it without setting yourself up for frustration.
Data Requirements
For many AI solutions, like predictive models, having clean, reliable data is essential. This is particularly true for software projects in the oil and gas industry, where predictive models anticipate equipment failures during drilling, only if they are trained on a comprehensive dataset. This includes historical maintenance logs, performance trends, and environmental factors.
Without sufficient high-quality data, AI models risk delivering inaccurate or inconsistent results. Building a strong data foundation ensures your AI tools can provide actionable insights and drive meaningful outcomes.
5. Risks: Mitigating Potential Issues like Data Privacy and Bias
AI is powerful, but it comes with some risks that need careful handling. If you ignore them, it could lead to legal trouble, ethical concerns, and a loss of trust from your customers. Here are two notable ones to keep in mind:
- Data Privacy: AI often deals with sensitive data, so following privacy laws (like the GDPR) is a must. According to the Cavell Q2 2024 AI in Comms Report, 36% of companies are stepping up their focus on data security. For example, if you’re using customer data to make predictions, you need to anonymize it via encryption and access controls.
- Algorithmic Bias: AI learns from data, and if that data has biases, those biases will show up in the results. This can lead to unfair outcomes, like discrimination in hiring or college admissions. To avoid this, set up systems to review and audit your AI models regularly, employ algorithmic fairness techniques or synthetic data generation. It’s important to also keep an eye on changes that might introduce new biases.
Handling these risks can feel like a lot, but you don’t have to do it alone. Partnering with an experienced software team can make it easier. We can help with audits, privacy compliance, and tackling bias, so you don’t have to figure it all out by yourself.
6. Expertise and Talent: Building AI Capabilities
Generative AI tools like ChatGPT became widely known after its 2022 public release of GPT-3.5. And with newer AI models emerging, staying ahead means being flexible and open to learning. Whether it’s training your current employees, hiring new talent, or outsourcing certain tasks, you’ve got options.
- Internal Talent Development: Start by training your current team in the basics of AI. Help them understand what AI can and can’t do, teach them how to organize and manage data, and get them comfortable using tools like chatbots or predictive analytics. It’s a great way to build on the talent you already have.
- External Expertise: Finding experienced AI talent can be tough, so teaming up with external partners can bring a sense of relief. You can leverage our staff augmentation services to bring in skilled experts to help with tasks like building models, integrating AI systems, and even training your team hands-on. It’s a wonderful way to fill gaps and keep operations moving forward.
At the end of the day, continuous learning and upskilling matters a lot. Invest in workshops, certifications, and online courses for your team, and lean on external partners when needed. This combination will set you up with an impactful, future-ready AI strategy.
Final Thoughts
An AI strategy means balancing your team’s growth with external expertise to tackle challenges and find the right solutions. It involves defining company goals, addressing challenges (such as technical limitations and resistance to change), and managing privacy risks. Remember, AI isn’t a magic fix—but we can work with you to develop, monitor, and fine-tune your AI strategy, making sure it grows with your business and delivers lasting results.
