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Developing an AI Strategy for Organizations: A Perspective

Developing an AI Strategy for Organizations: A Perspective

September 01, 2021

Benjamin Franklin once said that if a person fails to plan, they are simply planning to fail. This statement applies in organizations too. Considering the amount of complexity a business operates in the day-to-day, the importance of strategy becomes fourfold. Strategy drives an organizational design and keeps on redefining ways to achieve the ultimate business objectives. Therefore, all businesses proactively involve themselves in defining focused strategies that serve as a guideline for the company. A thoroughly studied and well-planned strategy leads to the sustainability and success of the organization.

Every organization makes its strategy after analyzing the following areas:

1. Competitions 2. Target Market 3. Business Plan

In addition to acting as a blueprint for the organizational processes, the strategies are also used to measure the organization's current performance against predictions. Thus an efficient business strategy allows businesses to predict the changes in advance and meet them efficiently.

Strategy for an AI-driven organization

When businesses are focused on innovations, the need to be flexible enough to adapt to changes becomes indispensable. An organization that is planning to invest in Artificial Intelligence must consider devising its AI strategy thoroughly.

At first, the organization needs to reflect on the existing use-cases of Artificial Intelligence to prioritize how they want to incorporate AI in the workflow. Some of the ways to do so are the following:

  • Development of advanced products using AI
  • Development of advanced services using AI
  • Enhancement of existing business processes
  • Automation of repetitive tasks within the organization
  • Automation of manufacturing processes

If planning to invest in multiple AI projects, then rank them in terms of their priority.

Secondly, invest resources in the identification of cross-cutting issues of AI applications. Here AI strategy is divided into skills and technology. The goal is to observe activities, challenges, goals, and themes that will be identical across all the use-cases of AI.

Next comes the strategy related to data. AI education highlights the importance of data and why it is crucial for data engineers to work on clean and up-to-date data. This step is highly influenced by the priorities you set for AI. Thus it becomes mandatory for decision-makers to look closely at their AI priorities and then determine data strategy accordingly.

After finalizing the data strategy, the team must look into the legal and ethical issues pertaining to Artificial Intelligence. Every use-case of AI has some of the other issues associated with it. Hence, the team needs to take this opportunity to identify and work upon them. Consider data privacy as an example. Everyone knows how sensitive data can be; thus you need to ensure that the collected data is highly secured.

Next comes identifying and designing infrastructure. Here the team must ponder upon the technical requirements as well as the challenges that will be in AI use-cases. In this step, try considering the following four data layers and pinpoint the technology required for that layer:

  • Data Collection
  • Data Storage
  • Data Analysis
  • Gathering Insights from data

One of the most vital parts of artificial intelligence strategy is, deciding the capacity and skills. The major roadblock organizations face while incorporating AI in their business processes is the lack of a skilled labor force. Even though AI education is growing, AI skills are limited to few experts. There are a handful of AI professionals who are capable of applying AI research to AI applications. Furthermore, according to the ‘Global AI Talent Whitepaper,’ only 300,000 AI researchers and practitioners are available. Hence it becomes mandatory for organizations to hire the best talent who will help them use AI in an ideal way. You can also choose to partner with an AI provider instead of doing all the work internally. However, this choice needs to be made with proper caution.

The next and final step is the implementation of AI. In this step of the AI strategy, you must identify all the requirements and challenges that may occur during the project's implementation phase. The goal here is to be prepared to deal with the roadblocks, thus ensuring the delivery of exact AI objectives.

Must have skills to build a sustainable AI strategy

In addition to the in-depth knowledge of AI and its related subjects, you need to be a keen observer when building a sustainable AI strategy. As AI is known for making disruptions, if handled poorly, it can cause huge losses. Following are the must-have skills:

  • Quantitative skills
  • Sharp observation skills
  • Advanced Analytical Knowledge
  • Detailed Oriented
  • Problem Solving
  • Flexibility
  • Interpersonal skills
  • Communication skills
  • Dealing with Uncertainty
  • Creativity


AI is known for bringing revolutions. When an organization decides to incorporate AI in its business processes, it must go through change management. AI strategy revolves around identifying, determining, and finalizing all the changes required in different steps of AI implementation. Whether the AI project automates tasks or manufacturing processes, it is bound to impact human resources. Hence all thoughts must be put into seeing all the changes that will happen and how to deal with them. Ensure that the AI strategy covers a 360-degree overview of the impact of AI projects on the organization and its people. Always deal with changes strategically, especially when the change is happening due to AI.