How to build a successful AI strategy

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, including manufacturing, hospitality, healthcare and academia. According to recent research, 92% of C-suite executives expect to digitize workflows and use AI-powered automation by 2026.¹ Without an AI strategy, organizations risk missing out on the benefits AI can offer.

An AI strategy helps organizations address the complex challenges associated with AI implementation and define its objectives. AI can be used for deeper data analysis, automation of repetitive tasks, process optimization or enhancing customer experiences with AI assistants and agents.

A well-defined purpose and plan helps ensure that the adoption of AI aligns with broader business goals. This alignment is essential for extracting meaningful value from AI and maximizing its impact. A successful AI strategy also provides a roadmap for building necessary capabilities and ensuring a strategic and responsible application of AI within the organization.

Organizations that make efforts to understand AI now and harness its power will thrive in the future. A robust AI strategy will enable these organizations to manage the complexities of integrating AI, adapt quickly to technological advancements and optimize their processes, operational efficiency and overall growth.

An artificial intelligence strategy is a plan for integrating AI into an organization so that it aligns with and supports the broader goals of the business. A successful AI strategy should act as a roadmap for this plan. Depending on the organization’s goals, an AI strategy can outline how AI helps extract deeper insights from data, enhance efficiency, strengthen the supply chain or ecosystem, or improve talent and customer experiences.

A well-formulated AI strategy should also help guide the tech infrastructure to ensure that the business is equipped with the hardware, software and other resources needed for effective AI implementation. And because technology evolves so rapidly, the strategy should allow the organization to adapt to new technologies and shifts in the industry. Ethical considerations such as bias, transparency and regulatory concerns should also be addressed to support responsible AI deployment.

As artificial intelligence continues to impact almost every industry, a well-crafted AI strategy is imperative. It can help organizations unlock their potential, gain a competitive advantage and achieve sustainable success in the ever-changing digital era.

Building an AI strategy offers many benefits to businesses integrating artificial intelligence, whether they are start-ups or global organizations. An AI strategy allows organizations to purposefully harness AI capabilities and align AI initiatives with overall business objectives. The AI strategy becomes the compass for meaningful contributions to the organization’s success. It empowers stakeholders to choose projects that will offer the biggest improvement in important processes such as productivity, decision-making and the bottom line.

An AI strategy outlines steps that help AI projects transform ideas into solutions. To achieve these goals, the organization must make important decisions regarding data, talent and technology. A well-crafted strategy provides a plan for managing, analyzing and using data for AI initiatives. It determines the talent needed to develop, attract or retain skills in data science, machine learning (ML) and AI development. It guides the procurement of hardware, software and cloud computing resources for effective AI implementation.

In essence, a successful AI strategy is indispensable because it supports business objectives, facilitates prioritization, optimizes talent and technology choices and ensures an organized integration of AI that will drive organizational success.

The following steps are commonly used to help craft an effective artificial intelligence strategy:

Gain an understanding of various AI technologies, including generative AI and agentic AI (and how they differ), machine learning (ML), natural language processing (NLP) and computer vision. Research AI use cases to know where and how these technologies are being applied in relevant industries. List issues AI can address and the benefits to be gained. Note the departments that use it, their methods and any roadblocks.

Understand the organization, its priorities and capabilities. Review the size and strength of the IT department, which will implement and manage the AI systems. Interview department heads to identify potential issues AI could help solve.

Identify the problems that the organization needs to solve. What metrics need to be improved? Don’t assume that AI is always the answer—choose business objectives that are important for the business and that AI has a track record of addressing successfully.

Find companies in the AI space that have worked within your industry. Create a list of potential tools, vendors and partnerships, evaluating their experience, reputation or pricing. Prioritize procurement based on the phases and timeline of the AI integration project.

Create a roadmap that prioritizes early successes that will bring value to the business. Choose projects based on identified practical needs. Determine the tools and support needed and organize them based on what’s most crucial for the project, specifically:

• Data: Make a data strategy by determining whether new or existing data or datasets will be required to effectively fuel the AI solution. Establish a data governance framework to manage data effectively.

• Algorithms: Algorithms are the rules or instructions that enable machines to learn, analyze data and make decisions. A model represents what was learned by a machine learning algorithm. Determine who will deploy algorithms and design, develop and validate models, as expertise is needed to effectively manage these tasks. 

• Infrastructure: Determine where your AI systems will be hosted and how they will be scaled. Consider whether to deploy on your own infrastructure or on third-party platforms.

• Talent and outsourcing: Assess the readiness of and skills gaps within the organization to implement AI initiatives. Determine whether a talent pipeline exists to fill roles such as data scientists and developers or whether skills can be developed internally through training. Also assess whether certain tasks, such as deployment and operations, should be outsourced.

Present the AI strategy to stakeholders, ensuring it aligns with business objectives. Attain buy-in for the proposed roadmap. Clearly communicate the benefits, costs and expected results. Secure the necessary budget to implement the strategy.

Start upskilling AI teams or hiring individuals with the right AI expertise. Encourage teams to stay updated on cutting-edge AI advancements and to explore innovative problem-solving methods.

Understand the ethical implications of the organization’s responsible use of AI. Commit to ethical AI initiatives, inclusive governance models and actionable guidelines. Regularly monitor AI models for potential biases and implement fairness and transparency practices to address ethical concerns.

Keep up with the fast-paced developments of new products and AI technologies. Adapt the organization’s AI strategy based on new insights and emerging opportunities.

Following these steps will enable the creation of a powerful guide for integrating AI into the organization. This integration will allow the business to take better advantage of opportunities in the dynamic world of artificial intelligence.

Several issues can get in the way of building and implementing a successful AI strategy. Their potential to impede the process should be assessed early—and issues dealt with accordingly—to effectively move forward.

How and where is your data, really? AI models rely heavily on robust datasets, so insufficient access to relevant and high-quality data can undermine the strategy and the effectiveness of AI applications.

A lack of awareness about AI’s capabilities and potential applications might lead to skepticism, resistance or misinformed decision-making. This lack of understanding will drain any value from the strategy and block the successful integration of AI into the organization’s processes.

If the AI initiatives are not closely tied to the organization’s goals, priorities and vision, it might result in wasted efforts, lack of leadership support and an inability to demonstrate meaningful value.

Professionals are needed to effectively develop, implement and manage AI initiatives. A shortage of AI talent, such as data scientists or ML experts, or resistance from current employees to upskill, could impact the viability of the strategy.

Recent developments within artificial intelligence highlight its scale and power on business and society. However, businesses must determine how to structure and govern these systems responsibly to avoid bias and errors as the scalability of AI technology can have costly effects to both business and society. As your organization uses different datasets to apply machine learning and automation to workflows, it’s important to ensure data quality, compliance and transparency within your AI systems with proper guardrails in place.

IBM can help you put AI into action now by focusing on the areas of your business where AI can deliver real benefits quickly and ethically. Our rich portfolio of business-grade AI products and analytics solutions are designed to reduce the hurdles of AI adoption, establish the right data foundation and optimize for outcomes and responsible use.

Global enterprises rely on IBM Consulting™ as a partner for their AI transformation journeys. As a leading AI consulting firm, we enhance the impact of AI development and cloud technologies in business transformation. By working across our own IBM watsonx® technology and an open ecosystem of partners, we deliver any AI model, on any cloud, guided by ethics and trust.