Building Blocks for Your AI Transformation

AI can work wonders for your organization, no matter its size. With the right strategy, even a small team and a modest budget can deliver impressive results. With some clear steps and the right focus, you can use AI to improve customer experiences, make data work harder, and give your team tools that help them do their jobs better.

Think of this article as a practical guide to starting with AI—no tech speak, just useful steps.

1. Start with a Clear AI Game Plan

Before you start, take a moment to assess the current state of your systems and processes. This insight will become your roadmap moving forward. Your AI implementation strategy should begin with small, specific projects where AI can solve existing problems, streamline tasks, or improve decision-making. Identify small use cases where a narrow problem is directly connected to a high-potential, attainable solution that creates business value.

For example, if you’re in retail, start with inventory forecasting; if you’re in finance, consider using AI to detect fraud. Start with achievable use cases, then scale based on success and new data insights. The key is to learn, adapt, and apply these lessons across bigger projects as you progress.

2. Get Your Data in Shape

The success of any AI project hinges on good data. This means identifying what data you have, cleaning it up, and ensuring it’s stored in a way that AI systems can access easily. Preparing the data can take longer than building and training the models, especially when errors, noise, or missing values are present. Data cleaning and harmonization may be necessary in such cases to make the data model-ready. Set up data governance—like monitoring data quality, compliance, and privacy—so you can use this data confidently.

For example, a healthcare provider using patient data for AI must prioritize both accuracy and privacy due to regulatory standards. This will further help stakeholders to build trust in the AI models.

3. Bring the Whole Team Onboard

AI success isn’t just about tech; it’s about people. Everyone, from executives to end-users, should understand the benefits of AI and how it will improve their work. Engineers and end users need to be able to align data with business needs to help identify high-potential use cases.

Let’s say you’re rolling out an AI tool to enhance customer service—make sure customer service teams understand the “why” behind the tool and can provide feedback on how it impacts their work. Transparency builds trust and ensures AI becomes a positive part of the work culture. For instance, in the retail industry, AI can simplify repetitive tasks, such as handling basic customer inquiries through chatbots, freeing up customer service agents to focus on more complex and rewarding issues. By demonstrating these tangible benefits, your team will see firsthand how AI not only simplifies their work but also elevates their ability to deliver exceptional service.

4. Embrace Agile Approaches

AI projects benefit from the flexibility and rapid learning that Agile methodologies bring. By setting up regular feedback loops and iterating based on real-time results, you avoid wasted resources and quickly identify winning approaches.

For instance, an e-commerce company might use Agile to continuously tweak an AI recommendation engine based on new customer behaviors.

5. Test Your Ideas with PoCs

A proof of concept (PoC) is helpful for testing AI ideas before going all-in. For example, if you’re considering a chatbot, a PoC will let you assess whether it handles customer queries effectively. If it works, you can move to a larger rollout with confidence. Through an AI PoC, your company can ensure that the idea can be brought to life from a technological perspective and gain experience, skills, and confidence in AI models. This stage is all about learning and minimizing risk. Here’s one of our favorite examples:

6. Deploy and Maintain Models with MLOps

Just like DevOps, MLOps (Machine Learning Operations) keeps AI models running smoothly and adjusts them as needed. MLOps will also ensure AI models and solutions continue to provide value over the long term. Models should be monitored for accuracy, and they should be able to “re-learn” as conditions change. For example, an AI used for credit scoring must adapt to new economic trends over time. However, setting up and maintaining such pipelines can be complex, requiring machine learning and operational workflow expertise. Partnering with a trusted provider like Exadel can simplify this process, ensuring a smooth start and reliable long-term performance.

7. Build the Right Teams

AI needs diverse skill sets. A project may require data scientists to design models, engineers to implement them, and domain experts to align with business needs. You’ll also need project managers to keep things on track and communicate results clearly.

Some companies choose centralized AI teams, while others integrate AI talent across departments so they’re closer to day-to-day needs. Involving outside resources can help you build and execute AI transformation faster by educating in-house teams on new and changing technology, helping identify use cases and suitable data, building and training models, or setting up and orchestrating an end-to-end transformation program. As part of this, you could consider using the vendor’s balanced team as a core, scaling later with the client’s employees to ensure sustainability.

Whichever approach you take, you should aim to ensure your team has the technical skills and business knowledge to create practical, impactful solutions that meet your company’s goals.

It’s the Right Time to Get Into AI

If you’re on the fence about diving into AI, consider this: organizations already embracing AI are seeing impressive benefits, from faster processing times to enhanced decision-making and customer service. In fact, 63% of global executives report improved performance directly linked to digital and AI initiatives, with another 67% planning to increase AI investments over the next three years. For many, digital transformation—including AI—is now a top priority, with 74% of organizations ranking it as one of their biggest initiatives (KPMG, 2024; McKinsey, 2024).

So, if you’re looking to future-proof your business—start now. Keep things simple, focus on one achievable step at a time, and remember: you don’t have to do it alone. Whether you start small with internal support or bring in outside expertise, taking those first steps today could set you up for incredible success down the road.

1. Start with a Clear AI Game Plan: Assess current systems, begin with small, specific projects, and scale based on success.
2. Get Your Data in Shape: Aim for clean, accessible, and compliant data for AI success.
3. Bring the Whole Team On Board: AI success relies on buy-in and understanding across all levels of an organization.
4. Embrace Agile Approaches: Employ iterative, flexible methods for AI projects to adapt quickly.
5. Test with PoCs: Validate AI ideas with low-risk testing before committing to full implementation.
6. Deploy and Maintain Models with MLOps: Highlights the importance of maintaining AI models for long-term value.
7. Build the Right Teams: Pull together a mix of technical, operational, and domain expertise, with optional external support.
8. Enjoy your Journey Forward!