There has been a lot of hype around AI in the past few years. But hype doesn’t bring business value – AI strategy does.
According to the recent McKinsey survey, 78% of organizations use AI in at least one business function, with most survey respondents reporting the use of AI in an average of three business functions. This marks a significant jump from 55% in 2023 but still suggests covering only a fraction of where it could deliver value.
While global AI adoption is accelerating, the majority of businesses still fail to move from the experimental or pilot stages to enterprise-level implementation of AI and thus generate tangible value.
The first thing every business needs to understand before investing in AI is that AI integration isn’t a one-time project,
says Vitali Likhadzed, CEO at ITRex
Rather, it’s a permanent, enterprise-wide transformation that needs strategic planning, solid governance, and a deep mindset change at every level of the organization. It’s not enough for leadership to push AI from the top; they have to build it into roles and workflows. At the same time, employees need to see AI as fundamental to how they do their jobs – not optional, but essential. This is a two-way shift. Rushing headlong into AI without that foundation is a dead end. To realize AI’s full value, companies should stop treating it as a series of isolated, experimental initiatives and start treating it as a core strategy.
In this article, AI consultants from ITRex share hands-on advice for developing an AI strategy – bypassing cliches like “identify use cases” or “choose the right tools” to focus on what actually works in the real world. Here we go.
What is an AI strategy?
At its core, an AI strategy is a roadmap for adopting and integrating AI into the organization’s operations and culture. It has nothing to do with chasing the next big thing or selecting the go-to AI tools. An AI strategy involves identifying the highest value opportunities for the entire enterprise, aligning AI initiatives with key business goals, and defining priorities around talent acquisition, AI governance, data management, and technology infrastructure.
An efficient AI strategy lays the foundation for how AI will be leveraged to maximize its impact and create value. It is not about pushing the limits of what AI can do – it zeroes in on what’s practical, scalable, and built to last, filling the gap between vision and a solution that drives real results. So how to develop an AI strategy that pays off?
Tips for creating an effective AI strategy from ITRex
As an established AI development company, ITRex has helped businesses and enterprises across industries move beyond experimentation to AI at scale. Here are the key insights we’ve gained:
- Prioritize employee adoption
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.
- Don’t start with what’s possible – start with constraints
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?
- Think of your overall business strategy first
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.
- Treat AI as a user experience game-changer, rather than a back-end engine
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.
- Make the feedback loop the priority
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.
- Integrate explainability from the get-go
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.
Developing an AI strategy for cancer patient support system: a real-world example from the ITRex portfolio
A client approached ITRex with a bold vision to transform the way newly diagnosed cancer patients manage their treatment journey. They were looking to create a platform that would offer personalized insights, covering everything from prognosis and treatment options to quality of life and the full cycle of care. While the goal was rather ambitious, the real challenge was to integrate AI as a seamless and impactful solution, rather than simply implement it as a standalone tool. We understood that for AI to be successful, we needed to create a comprehensive AI strategy that would align with both the client’s overarching business goals and patient needs. Here is how ITRex helped the client build a winning AI strategy based on the core principles we described above.
- Prioritizing employee adoption and stakeholder buy-in
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.
- Identifying constraints before exploring possibilities
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.
- Integrating AI with business strategy
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.
- Transforming user experience with AI
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.
- Ensuring continuous feedback and adaptation
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.
As a result, ITRex’s comprehensive AI strategy enabled the client to build a platform that didn’t just integrate AI – it fully embraced AI as a transformative force across business operations. By aligning the technology with the client’s goals and fostering a culture of continuous learning and adaptation, ITRex helped deliver a solution that empowered cancer patients and provided physicians with actionable, real-time insights that greatly improved patient outcomes.
Final thoughts from ITRex
AI is not about technology – it’s all about business and human transformation. Companies that succeed in realizing its full value are not the ones seeking trendy tools or use cases. They are the ones with a well-thought-out AI strategy built on reality: structured around real-world constraints, tied to core business objectives, focused on user experience, fueled by fast feedback, and designed to earn trust through explainability. That is to say, a solid AI strategy doesn’t follow the hype. It follows what works. At ITRex, we don’t just build AI. We build overarching AI strategies that deliver measurable impact – not just technical wins.
Trying to develop an AI strategy to see tangible results? Talk to the ITRex team and turn your AI vision into measurable impact.
Originally published at on May 16, 2025.
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