How To Create A Winning AI Strategy

April 4, 2023

Gaining insights from big data is crucial for businesses to remain competitive and survive in today’s digital era. With users generating almost 1.15 trillion megabytes of data daily, plenty of valuable insights await companies willing to manage and analyze such data effectively.

Artificial Intelligence (AI) is the primary tool for organizations to achieve data dominance. In fact, around 84% of C-level executives believe that AI will boost company growth. However, using AI to accomplish any business goal requires a sound AI strategy. 

In this post, we’ll discuss the critical components of an AI strategy and its challenges and suggest a checklist to help you develop a foolproof plan for your next big AI project.

Components of an AI Strategy

AI strategy can involve different elements depending on an organization’s nature, size, and the industry in which it operates. But certain factors are common to all organization types. Here, we mention the five most significant components that every AI strategy must consider - the business problem, market size and need, AI’s role, data infrastructure, and team expertise. Need a transition sentence.

Business Problem

The first component concerns the business problem. What is it that you’re trying to solve? Businesses often tend to use AI without understanding what it will solve. Of course, you’ll have several business problems, and you must filter out those you think are the most suitable for AI to solve. 

The nature and complexity of the problem will help you decide whether your organization has the right resources to use AI to address the issues in question. It will also help you manage expectations more effectively and ensure the top leadership buy-in. 

Also, once you know the problem, you can craft possible solutions. The solution must be specific and clearly state how it will solve a particular issue. For example, a solution may be introducing a new feature in your product to address a common customer pain point.

Market Size and Need

If your solution is to launch a new product or service, you must consider the market size and whether a market gap exists. It should involve examining the competition and analyzing whether your solution will reliably help customers in a way your competitors cannot.

The market size and need will also help you plan your investments more efficiently. A larger market size indicates more opportunities and can suggest a faster return on investment (ROI). 

Also, studying market needs will let you know their intensity. For example, addressing a critical need means customers will adopt your product or service quickly, allowing you to break even in minimal time.

AI’s role

The most crucial element of an AI strategy is to analyze whether the problems and their solutions, market size, and need warrant investments in AI.

AI is unlike a typical IT project where you can use plug-and-play software to improve operations.

AI is a long-term strategic technology that takes time to mature and allow the company to reap its rewards. As such, businesses must avoid using AI only because of its popularity. The question should be whether AI can help solve problem X profitably rather than “how can we use AI?”

The issue with the latter approach is the lack of a clear objective. It shows that you only want to use AI to keep up with the Joneses. Instead, you must identify viable solutions to a problem and examine whether AI can help.

A common way to see whether AI has a role is to familiarize yourself with common AI use cases and study how AI works. It will help you understand its applications and decide whether to use AI more efficiently.

Data Infrastructure

There’s no AI without data. You must have a robust data infrastructure that ensures you get high-quality, relevant, and enough data to fuel AI algorithms. More importantly, you must see whether the problem you want to solve requires and has data.

Sometimes, you don’t require a lot of data to solve simple problems. Suppose you’re a healthcare provider wanting to diagnose a particular flu type. You can achieve this by consulting or hiring a specialist instead of developing a complex data system to understand the disease and identify the cure.

On occasion you can’t obtain the relevant data to apply an AI solution to a particular problem. For example, diagnosing a highly complex and novel brain disease can require analyzing brain scans to develop a possible cure. But AI cannot provide a cure as the disease is new, and little is available to suggest a reliable remedy.

So ensure you have enough high-quality data to train your AI algorithms so they can work reliably. You must also see whether your organization can build the required infrastructure to curate such data.

Team Expertise

Your organization must have the relevant staff with the right skills and expertise to create, deploy, and manage AI projects. You mainly require data scientists and subject-matter experts to help drive your AI efforts.

Of course, the need to assess whether you have the desired staff rests upon the fact that you build AI solutions internally. Otherwise, you can hire an external AI consulting firm to do the job for you, though you’ll require in-house data scientists to help you navigate the many challenges AI projects pose.

However, AI applications are interdisciplinary, and AI projects require high interoperability between teams. So building AI in-house means organizations will likely develop a robust collaborative culture which will be a critical success factor for a company’s AI strategy in the long term.

Challenges of AI Strategy

As stated, AI is more than a typical IT project where you can quickly integrate a tool into your existing infrastructure and observe improvements in the next few months. 

Instead, AI is a complex process that involves experimentation, data management, learning from mistakes, and building a solid governance framework to ensure a collaborative and compliant culture. 

But doing so is challenging. We highlight five significant challenges companies must address while working on AI initiatives. They include leadership buy-in, deployment issues, unrealistic expectations, cross-team collaboration, and scalability.

Leadership buy-in

The biggest challenge is convincing the leadership to undertake major AI projects. The reason is that it’s unclear how AI aligns with an organization’s overall mission, vision, and long-term goals.

And that’s why it’s always ideal to ask whether a specific mission-critical problem needs AI. If yes, convincing the executive team to allocate sufficient funds for AI efforts would be easier.


Estimating the cost of an AI initiative is challenging due to the uncertainty involved with returns on investment (ROI). And that’s because AI is an experimental process, which means it will take time to get it right and generate the expected returns.

However, organizations with a limited budget cannot afford to try new methods with a high failure risk. There are also concerns about data, as getting high-quality data is challenging. Incorporating these factors into your budget is difficult.

Deployment Issues

Taking an AI project from the initial phases to deployment is long and tedious. You can encounter several issues with data, such as low data quality, lack of suitable platforms to transform raw data into usable formats, accessibility issues, data silos, etc.

Moreover, the development of AI models begins with sample or dummy data. So it’s uncertain how the application will perform on actual real-time data.

Further, there can be issues with compliance due to data laws like General Data Protection Regulation (GDPR) evolving all the time.

Unrealistic Expectations

Businesses often have lofty and unrealistic expectations from AI projects. Although AI is a promising technology, it’s still in its infancy, and we don’t know much about what AI holds for us in the future. 

So setting high expectations without realizing the challenges can lead to failure and leadership exit. But you can avoid it by framing AI projects as long-term investments whose benefits will accrue as organizations learn more about AI.

Change management

Due to the iterative nature of AI projects, you’ll likely encounter complex problems along the way, requiring you to readjust your priorities and processes. 

But changes mid-way means management strategy must be adaptive and guide any necessary transitions to complete a project. 

However, unexpected changes can cause misalignments and miscommunication between teams leading to executive management dropping their support. A resistant culture is also a significant obstacle that you must consider.

Cross-team collaboration

Often, AI projects require input from the leadership team, subject-matter experts, and data scientists. The latter two, however, are extremely important. 

That’s because subject-matter experts have the proper industrial and business domain knowledge to guide data scientists to get relevant data and measure appropriate outcomes.

For instance, developing a financial management app for users would require financial analysts to collaborate with data scientists to help them understand what goes into making sound investment decisions.

Otherwise, getting subject-matter experts to spare time for data scientists takes a lot of work. They may have other critical issues to resolve. Also, organizational incentive structures usually don’t reward out-of-domain efforts.

But it means you can avoid it by tying incentives to efforts that facilitate data scientists and developing a culture of embracing AI.


Using AI to solve short-term problems can lead to projects that are difficult to scale in the longer term. As users increase and data volume grows, your AI application should be flexible enough to allow you to expand its operations conveniently. Otherwise, ROIs can drop mid-way, and the entire AI initiative can become your organization’s most tremendous loss.

Anyway, building such scalable solutions is challenging as it requires extensive foresight regarding future challenges. But that’s where a sound AI strategy helps. 

The strategy must clearly outline the procedures to follow in case of issues and delineate all possible challenges before undertaking an AI initiative. The following section presents an AI strategy checklist to help you develop a roadmap to make your AI projects scalable.

AI Strategy Checklist

1. Long-term Goals and Short-term objectives

The first step is establishing long-term goals you wish to achieve using AI. The goals can span 3-5 years and must align with an organization’s mission and vision. It will clarify where AI fits in your overall business strategy.

You can then tie these goals to short-term objectives to make your goals achievable. For example, a long-term goal might be increasing growth by X% over the next five years. 

A short-term objective to achieve this goal can be to increase sales revenue by Y% per year. You can then analyze how AI can help you achieve this short-term objective. 

2. Identification of AI Opportunities

You can identify all the AI opportunities you think will solve high-priority problems to help your company achieve its long-term goals.

3. Shortlisting

You can then shortlist AI opportunities you believe your company can work on. At this stage, you must assess your company’s skillset, budget constraints, competition, market size, and market need to decide which opportunities to pursue objectively.

Once you know what AI initiatives you want, you should see if you wish to build in-house or hire an external consulting firm. 

Building AI projects with sensitive intellectual property (IP) in-house is advisable. However, you can hire an external agency to create a ready-made solution if the project doesn’t involve sensitive IP.

Otherwise, it’s always better to collaborate with a consulting firm instead of completely outsourcing AI projects. It helps your organization achieve AI maturity by understanding how AI works and allows you to establish a team to address future challenges more flexibly.

4. AI Pilot

Always start small to get an idea of ROIs. You can run your AI solution as a pilot to assess challenges and fine-tune your strategy to deal with issues when you expand AI operations.

5. Expansion

Once you’re confident that your pilot runs well, you can begin with the expansion phase and develop relevant key performance indicators (KPIs) to track progress.

6. Conclusion

Organizations with a robust AI strategy will win the ever-growing digital marketplace. However, building one can be challenging, especially for start-ups and medium-sized businesses with limited budgets. And that’s why they can benefit from experienced AI advisors who understand AI thoroughly. is here to help you guide your AI efforts and ensure scalable solutions with reasonable ROI. We’re a team of experts with extensive machine learning (ML) experience working at Google, Amazon, CERN, and other reputable companies. 

So, contact us now to unleash your AI potential!

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