How to develop an AI strategy: 6 tips from ITRex

No matter how advanced your AI strategy is, it’s meaningless if your team isn’t on board. AI doesn’t just change processes—it transforms roles, skillsets, and how teams collaborate. So, gaining employee buy-in is the first and foremost step in implementing AI within your organization.

AI adoption is more than just a systems upgrade—it’s an organizational change. The cultural aspect of AI is often overlooked, but the record shows that culture can make or break strategy. If your employees don’t understand why AI matters and how it can positively impact their roles, any strategic plan is destined to fail.

You can’t expect your employees to simply adjust to AI-driven changes without being fully on board. So it’s critical that you clearly communicate the benefits of AI—show them how it will make their jobs more efficient, improve decision-making, and help them adapt to a constantly evolving business landscape. This isn’t a “one-time” conversation. AI is a perpetual transformation. To ensure adoption, build a culture of continuous learning and adaptability—one that can quickly pivot, upskill, and embrace new technology.

Many companies start developing an AI strategy with brainstorming use cases, whereas the first thing they need to do is identify their technical and organizational constraints, including data quality, infrastructure maturity, budget, team readiness, and compliance. That is to say, they put the cart before the horse. So, our number-one piece of advice is to assess what can hold you back. The following questions will help you understand your constraints:

• Is your data clean, usable, and easily accessible?

• Can your current infrastructure support the computational demands of AI?

• Do you have the right talent in-house or need to outsource AI development?

• Can your budget support a long-term project?

• Do legal requirements limit how you gather, store, and use data?

And don’t let isolated use cases distract you from the big picture. The point is that leaders can easily get caught up in multiple technical AI possibilities and overlook the main objective—real business value. Sure enough, a few one-off AI projects may feel practical and promising in the short term. However, several disconnected AI initiatives can’t move the needle unless they are linked to a broader, company-wide strategy.

Outsourcing AI planning to tech teams that focus solely on technology and not business outcomes leads to siloed solutions that fail to add up to a company-wide change. The most effective AI strategies don’t start with algorithms—they start with defining the company’s overarching objectives, growth targets, and key performance metrics. In this scenario, the overall business strategy serves as the engine, whereas an AI strategy functions as fuel to it. This is where cross-functional collaboration becomes essential.

A standout example of scaling AI effectively comes from Amazon. Instead of isolating AI with a single department, the company challenged their business leaders to figure out how AI and ML could drive business value in their domain. That move embedded AI into every corner of their business landscape, laying the foundation for Amazon’s leadership in the field. The lesson learned? Finding opportunities and aligning them with broader goals must be a top priority—AI integration into business strategy is what comes next.

So make sure that your entire company moves in sync, aligning every AI effort with the core business strategy.

Too often, AI is treated simply as a tool for automation, optimization, or data crunching behind the scenes. Yet, artificial intelligence is bigger than that. It represents a new way to interact with people, systems, and data. Also, it’s not just about doing things faster—it is about doing things differently. Consider this:

• Employees aren’t just looking at better dashboards—they’re working together with AI to make faster, more informed decisions.

• Customers aren’t just browsing your website—they’re interacting with AI agents that understand what they mean, not just what they type.

• Leaders aren’t just reviewing reports—they’re using AI copilots to explore scenarios, test assumptions, and guide long-term decisions.

One of the most common traps when developing an AI strategy is chasing the “perfect” model. Precision, recall, and F1 scores really matter, but they don’t guarantee success. In practice, it is not the model that plays a key role—it is the feedback loop.

What drives real results is your ability to learn quickly and adapt. It’s essential how swiftly your team can close the loop—collect performance data, retrain the model, and redeploy. That very cycle is what differentiates a high-performing AI solution that adapts weekly based on real usage from a fancy one that stalls in production.

So, our next recommendation is as follows: do not fall into the trap of over-engineering a model. Your AI strategy should prioritize iteration over perfection, even if you have to sacrifice complexity at the outset. It’s not the smartest model that wins—it’s the one that learns, iterates, and scales.

AI still has a trust problem. Users, stakeholders, or regulators need to know why the model has made a specific decision. Since if they don’t understand the intent, they won’t trust the outcomes, which hinders adoption. That is why explainability should be baked into the strategy from day one.

Whether it’s a customer app, a decision support system, or internal automation, people need visibility into how the system works. That means selecting interpretable models where needed and UX that makes outputs understandable. You will need to strike the right balance between performance and clarity. In some cases, it’s better to opt for a less complex model to gain transparency. In others, it’s about designing clear interfaces that explain the “why” behind the output.

So make it a rule from the start: if you can’t explain something to a non-tech user, simplify the model.

Focusing on the staff adoption inside the client’s company was our first step. ITRex collaborated closely with the client teams to make sure that everyone involved recognized how important AI was to changing how patients and healthcare professionals interacted. We made sure that everyone in the organization—from developers to clinicians—understood and welcomed AI’s role in their day-to-day operations by promoting continuous education and communication. This cultural adjustment was a crucial first step in ensuring the AI platform’s long-term viability.

What we did next was to assess the existing infrastructure and organizational constraints before diving into potential AI use cases. We examined the client’s data quality, infrastructure maturity, budget, and regulatory limitations to help the client gain a clear understanding of what was realistically achievable.

ITRex encouraged the client to establish a more comprehensive, corporate-wide AI strategy that would support their business objectives rather than pursuing isolated AI initiatives. By making sure the AI project aligned with the client’s long-term goals, our team created the groundwork for scalable, significant solutions that went beyond discrete technical implementations.

By envisioning AI as a game-changer for user experience, rather than simply a backend optimization tool, ITRex helped the client develop an AI solution that significantly improved patient care and clinical decision-making. The comprehensive platform consists of three integrated components—MyInsights, MyCommunity, and MyJournal—designed to offer personalized insights, facilitate patient support, and capture ongoing patient data.

Our next step was to prioritize a continuous feedback loop throughout the AI development process. Instead of aiming for the perfect model right from the start, we focused on rapid iteration and continuous learning. This approach allowed the AI platform to evolve with real-world conditions, becoming a dynamic tool that could improve over time and better serve both patients and healthcare providers.