What to consider before adopting AI: 7 critical aspects for a future-proof AI strategy

The growing interest for integrating artificial intelligence into the broader business operations marks a new chapter in modern corporate narratives. In a matter of months, AI efforts evolved from being exploratory initiatives primarily pursued within tech-savvy and forward-thinking companies, to become a core component of corporate strategy across a wide range of businesses. Nevertheless, developing an AI strategy can present many challenges due to the technology’s fast evolution, high level of uncertainty, and extremely broad application. This article dives into critical considerations that can shape the foundation of a robust AI strategy, ensuring that your company not only keeps pace but leads in the age of smart technology.

What is an AI strategy, and why do you need one?

An AI strategy is essentially about evaluating a number of high-level alternatives for integrating artificial intelligence into a company, aligning it with the broader business objectives. It serves as a guide, detailing how AI can enhance data insights, improve operational efficiency, optimize supply chains, and improve the experiences of employees and customers. It ensures the right technological framework is in place for effective AI deployment, while remaining adaptable to industry developments and technological breakthroughs.

Ever since OpenAI launched ChatGPT, many corporate executives are under pressure to adopt AI to boost efficiency and stay relevant. However, diving into AI projects before having a coherent strategy in place often falls short of expectations. Creating solutions to non-existent problems, building extensive but unnecessary data infrastructure, or mismanaging AI projects through conventional approaches are just a few pitfalls we commonly observe. Given the unprecedented speed at which AI reshapes entire industries, formulating a strategic plan is essential for unleashing potential, securing competitive advantages, and driving long-term success in the new digital landscape.

7 critical aspects to consider when formulating an AI strategy

Define clear, impactful objectives

The bedrock of a successful AI strategy lies in the clarity and relevance of its objectives. AI should not be adopted just for the sake of novelty; it should address specific business needs, improve efficiencies, or unlock new opportunities. Begin by identifying problems that AI can solve effectively and align these solutions with overarching business goals. Whether it’s enhancing customer experience, optimizing operations, or driving new revenue streams, clear objectives will provide direction and a measure of success for your AI initiatives.

Data readiness and governance

AI systems thrive on data. The quality, availability, and integrity of your data directly influence the performance of AI applications. Start by assessing your existing data resources, ensuring they are clean, comprehensive, and well-organized. This evaluation will clarify whether the necessary data is at hand or needs to be sourced externally. It’s also important to look over robust data governance policies to maintain data quality and address privacy, security, and ethical concerns. Establishing a strong data foundation not only fuels AI effectiveness but also builds trust and reliability in AI outcomes.

Building the right team and culture

The success of an AI initiative is largely dependent on the people behind it, so carefully consider what competencies you’ll need, and how to acquire them. Generally, a multidisciplinary team that includes AI and machine learning experts, data scientists, business analysts, and ethical advisors is required. However, technical expertise alone is not enough; fostering a culture that embraces change, encourages experimentation, and allows for failure is equally important. Empower your team with continuous learning opportunities and encourage collaboration across departments to break down silos and integrate AI thinking into the organizational fabric.

Technology and infrastructure

Choosing the right technology and infrastructure is pivotal in realizing your AI strategy. Evaluate whether to build in-house solutions, purchase from vendors, or adopt a hybrid approach based on your objectives and resources. It’s the typical strategic dilemma: to buy, build, or partner. Consider scalability, compatibility with existing systems, and long-term support in your decisions. Investing in a flexible and scalable AI infrastructure that can keep up with the rapid development will support evolving AI needs and enable your organization to adapt quickly to technological advancements.

Ethical AI and compliance

AI ethics and compliance are not just about adhering to regulatory requirements but are fundamental to building trust and defining the applications of the technology. The AI strategy should include an ethics framework that addresses fairness, transparency, accountability, and privacy. Consider the level of risk your business is prepared to tolerate – should your AI systems be free from biases, respect user privacy, and provide clear explanations for their decisions, or are you open to exploring cutting-edge AI with higher potential reward, but also higher potential risk or less predictable outcomes?

Centralized vs decentralized AI

The tradeoff between a centralized approach, establishing a central hub for AI excellence, and a decentralized strategy, where units tailor AI to their needs, is another key strategic decision that will shape AI adoption. In a centralized approach, the central hub sets standards and leads AI initiatives, ensuring uniformity and shared knowledge across the organization. Conversely, a decentralized strategy empowers individual departments to develop AI solutions tailored to their unique needs, fostering innovation and quick adaptation. A hybrid approach combines these, providing central guidance and support with flexibility for local teams to execute on AI projects. Consider what path is most effective based on your company’s structure, goals, and scale of AI ambitions.

Monitoring, measurement, and scaling

An AI strategy is not set in stone; it requires ongoing evaluation and adaptation. Implement mechanisms to monitor AI performance against predefined metrics and objectives. Use insights gained from operational AI systems to refine and enhance their accuracy and efficiency. Recognize when to pivot, scale successful projects, and phase out underperforming initiatives. A dynamic approach to monitoring and measurement ensures that your AI strategy remains aligned with business goals and market conditions.

Laying the foundations for a sustainable AI ecosystem

The rapid advancements in artificial intelligence are reshaping the landscape of both business and society. Corporations now face a major challenge in responsibly configuring and managing these powerful systems. A well-thought-out AI strategy will be a game-changer for any organization, providing a competitive edge and faster adoption. By focusing on clear objectives, data governance, the right team, technological infrastructure, ethical considerations, and organizational structure, your company can harness the full potential of AI. Keep in mind that strategy is ever evolving, so continuous improvement is equally important.

For a more detailed guide to what specific questions to evaluate, download our report: