Building an AI Business Strategy: A Beginner's Guide
Competing in today’s market requires adapting to emerging technologies like artificial intelligence (AI) to transform business strategies, streamline operations, and gain a decisive edge.
Many traditional business models no longer suffice and now require incorporating AI. But what does an AI-driven business strategy involve?
What Is an AI Business Strategy?
An AI business strategy integrates AI into your company’s operations, decision-making, and growth plans. Unlike traditional strategies based on manual processes and historical data, those centered on AI leverage machine learning, data analytics, and automation to drive innovation.
By quickly processing vast amounts of data, AI can enhance everything from customer experiences to supply chain logistics. However, that doesn’t mean strategic planning isn’t important.
Adopting AI isn’t just about deploying technology but reshaping your organization’s business model and aligning its culture, goals, and resources.
No matter what you hope to optimize, recognize that implementing AI is a long-term strategy—not a quick fix. Here are six steps to building a strong AI business strategy.
6 Steps to Building an AI Business Strategy
1. Understand Business Objectives and Needs
The first step to building an AI strategy is understanding how it helps achieve business goals and objectives. Marco Iansiti and Karim Lakhani recommend using an AI-first scorecard—an assessment of your organization's readiness to adopt and integrate AI technologies—to gauge your capabilities and align stakeholders.
The AI scorecard evaluates your organization’s:
- AI adoption: How well it integrates AI, data platforms, software, and analytics across departments
- AI architecture: Whether its digital infrastructure is strong enough to ensure seamless, standardized data between systems for optimal performance
- AI capability: Development teams’ strength, how agile processes are, and whether it’s structured to promote innovation
By applying the AI-first scorecard, you can gauge your company's AI adoption, identify gaps, and prioritize actions to meet long-term goals.
2. Conduct a Data Audit
Before implementing an AI business strategy, assess your organization’s data infrastructure and AI maturity—or how prepared it is to leverage AI.
Conducting a data audit—which evaluates data assets’ quality, accessibility, and governance—is crucial to understanding data infrastructure because it:
- Identifies your organization’s data sources, including customer databases, sales records, supply chain data, and financial reports
- Reviews data’s accuracy, consistency, and completeness so AI systems use reliable data outputs
- Ensures relevant teams across your organization can access the data they need without barriers
- Evaluates how you manage data, who can access it, and how secure it is from breaches or compliance violations
In the assessment process, you may uncover challenges like data silos—when different departments separately store and manage data. For example, if your marketing and sales teams manage data separately, it can be challenging for AI to generate valuable insights.
With a data audit, you can connect information across departments to eliminate silos and create an integrated infrastructure capable of supporting AI at scale.
3. Develop an Ethical Framework
As organizations rush to implement AI, many fail to address its ethical considerations, such as data privacy, bias, and transparency. Those must be part of your strategy from the beginning to avoid serious consequences.
Not examining those issues puts your organization at risk of legal violations, loss of customer trust, and long-term reputational damage. Preventing such pitfalls requires defining clear ethical standards that cover:
- Data privacy
- Fairness
- Algorithmic transparency
Since AI heavily relies on data, it's vital to have a strong data governance policy detailing how you collect, store, and use data to manage privacy, security, and integrity.
4. Choose the Right AI Technologies and Tools
The next step in your AI business strategy is selecting AI technologies and tools that align with your business goals.
Start by assessing the problems you’re trying to solve with AI, such as automating processes, improving customer experiences, or enhancing decision-making.
AI technologies that can benefit your organization include:
- Machine-learning platforms, which enable systems to learn from data and improve without explicit programming
- Natural language processing tools, which allow machines to understand and respond to human language
- Robotic process automation, which automates repetitive, rule-based tasks and frees up time for more strategic work
When evaluating technologies, consider testing tools on a small scale before full implementation.
5. Prioritize AI Skills Development
AI technologies are complex. Without a skilled team, even the most promising can fail.
To execute your AI business strategy, identify skills gaps in areas like:
- Machine learning
- Data science
- Data engineering
If gaps exist, determine whether to upskill your employees through AI training or recruit new talent. While your information technology and data management teams may be strong, AI often requires specific expertise.
Collaboration is equally important. AI initiatives shouldn’t operate in isolation; they should be alignment on business goals across departments.
Without a team equipped to handle AI’s complexities, even the best strategies can struggle to succeed. By investing in AI training and acquiring the right expertise, your organization can be better positioned to compete.
6. Get Employee Buy-In
As you implement your organization’s AI business strategy, make it a point to gain employee buy-in. AI-driven changes affect not only your systems and processes but employees’ roles, skills, and collaboration.
To garner employee buy-in, clearly communicate the vision for adopting AI across your organization. Employees must understand AI’s benefits and how it can positively impact their roles.
Lead in the Age of AI
As AI redefines industries, learning how to harness its potential has never been more critical. Beyond technical know-how, staying ahead involves developing a comprehensive business strategy that integrates AI into every aspect of your organization.