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How to Develop Your Artificial Intelligence (AI) Strategy

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AI strategy defines a roadmap for integrating AI into business to enhance operational efficiency. Artificial intelligence can be used to make efficient business products and services. It can optimize business processes by automating repetitive tasks. But to actualize the AI potential, an organization needs a strategic plan to determine its AI maturity, list the challenges and track its progress.

AI profoundly impacts the business landscape and drives innovation. AI market size was about $330 billion in 2021, and it would be approximately $1400 billion in 2029, growing at a CAGR of 20.1 %. Moreover, a Gartner study found that

  • 80% of business executives believe that AI automation can be used for any business decision.
  • With 72% of executives reporting that they have or can source the AI talent they need.
  • 54% of AI applications successfully transition from pilot to production.

In this blog, we will explore what an AI strategy is, its planning and execution phase, and its benefits.

What is an AI Strategy?

Starting an AI venture without an AI strategy will lead to complications, vague expectations, unwanted delays, and, ultimately, project abandonment. An organization needs to define its AI needs, resources required, and timeline to build an actionable AI strategy to guide business growth.

Phase 1- Business Plan and AI

Business Strategy and AI Strategy

The first step for an organization in making its AI strategy is identifying its goals and objectives. The organization should revisit its business strategy and streamline it to align with the AI strategy. In this step, the organization should answer the following questions:

  • What are our business goals, and how can AI help us achieve them?
  • Why and where are we using AI?
  • What kind of and how many resources will it take to execute the AI strategy?

Identify use cases

Identifying use cases is a natural transition from the questions asked above. In this step, the organization should identify its pain points. To that end, the organization should list 3-5 relevant use cases, rank them according to their importance and select the ones that can help achieve significant business goals or minimize the major business problem. For example, computer vision can be used in healthcare for medical image (e.g., CT scan) analysis.


Phase 2- Execution (a step-by-step process for a viable AI Strategy)

Data Strategy

There is no AI without data. Data is an asset for an organization. Data strategy refers to a comprehensive plan for an organization to manage its data. A company should identify its data sources, store them, update them, and use them for business goals and AI/ML pipelines. While formulating the AI strategy, the company should align its data strategy with the AI strategy.

Auditing and Risk Assessment

An AI application needs to be agnostic when variables such as color, gender, or race are changed. Biased AI applications can be harmful. A thorough risk assessment is necessary for legal, ethical, and social considerations.

To this end, auditors use AI frameworks, data regulations, and AI ethics for auditing the AI/ML pipelines. By conducting risk assessments of ML pipelines, an organization builds trust in its AI system.

Technology Infrastructure

Technology infrastructure refers to the hardware and software required for your AI strategy. In this step, the organization determines computational power, programming libraries, frameworks, cloud computing services, data processing and analysis tools, and deployment tools necessary for building the AI system.

Skilled Manpower

Organization needs to identify the team it needs for building the AI system. Data engineers, data analysts, data scientists, machine learning engineers, software engineers, and AI architects are required to make the AI application. The organization should communicate the talent requirements to the HR team to understand and bridge over the knowledge gaps. Talent recruitment differs based on the type of AI product an organization needs. For language models, employees with expertise in NLP (Natural Language Processing) are required for object detection, and localization employees with experience in CV (Computer Vision) are required.

For help with hiring visit our best AI Recruiting Companies guide.


Once everything is in place, it is time to execute the plan. The implementation consists of the following steps:

  • Data Gathering
  • Data Preprocessing
  • Data Analysis
  • Modeling and Evaluation
  • Deployment

The AI architect understands the organization’s AI objectives and leads the team. Data analyst receives data from data engineers and preprocesses it. After preprocessing and analyzing, the data analyst shares key insights with the team and stakeholders. Machine learning engineer makes proper validation strategy for modeling. Once the model with the best result is selected, a secure platform is chosen by the software engineering team to deploy the model. After deployment, the model is continuously monitored and updated to achieve desired results.

Benefits of having an AI Strategy

Enhanced Efficiency: AI is efficient in decision-making and can automate repetitive tasks. By automating mundane processes, employees can then focus on high-value tasks.

Clarity: Clearly defined AI strategy creates a roadmap that is easy to follow and is likely to succeed. In AI strategy, the roles and responsibilities of everyone in the team are communicated. Moreover, it raises the stakeholders' trust in investing in the venture.

Competitive Advantage: Having an AI strategy gives a disproportionate advantage. For example, an auditing firm using AI applications will work faster and, in turn, do more business.

AI Strategy – Way Forward

AI strategy is an organization's comprehensive plan to integrate artificial intelligence into its business strategy in tandem with data strategy. The AI ecosystem will continue to expand exponentially with Cutting-edge research methods, massive data, and tremendous computational resources catalyzing the growth. An organization needs to keep up with the pace and revise its AI strategy to get the most out of the AI boom.

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