8 steps to create an effective AI strategy
With artificial intelligence (AI) transforming industries at an unprecedented pace, it’s clear that organizations must learn how to integrate these technologies or risk falling behind.
Your AI strategy is your roadmap for success, guiding the adoption of AI into core business operations to help you become your most efficient, profitable, and competitive with this rapidly evolving technology. This strategic framework will help you make important decisions about data, talent, and technology, while outlining the steps to turn AI initiatives into successful solutions.
This article explores the eight steps to creating an effective AI strategy for your business.
What is an AI strategy for business?
An AI strategy is a comprehensive plan for how to integrate AI into your organization’s operations, decision-making, and growth goals.
By quickly processing vast amounts of data, AI can automate tasks that once required human intelligence, enabling you to streamline operations, improve customer experience, and create new opportunities for innovation and growth.
However, AI is as complex and transformative as it is promising. Adopting AI effectively requires more than simply deploying new technologies, but also preparing for it to redefine every aspect of your core operations going forward.
An effective AI strategy is based on the core principles of business strategy, helping you implement AI in targeted areas while aligning with key KPIs to create a decisive competitive advantage. If done well, it will enable you to drive immediate business value from AI initiates, while also setting yourself up for long term success in the AI revolution.
Here are the key steps to create an effective AI strategy.
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8 steps to build an AI strategy
1. Define business objectives and opportunities
The first step in creating an AI strategy is to understand how AI can help you achieve your business objectives and address persistent challenges. Aligning your strategy with existing problems and well-defined goals will ensure that every decision you make around AI supports the overarching vision and purpose of your business, helping to avoid wasted time and resources.
AI can enhance everything from customer experience to supply chain management, so you can hone in on your best opportunities by asking questions like:
What are you trying to solve with AI?
What challenges do you face in your industry?
Where can AI provide the most benefit?
In 2010, Jeff Bezos told every leader across Amazon to plan for how they would use AI to help the company win the ecommere space. This mandate drove unmatched innovation and cemented Amazon’s position as industry leader. This example illustrates how the best AI strategies come from clear objectives to identify gaps and opportunities in key areas, working backward from problems to apply AI as a solution.
2. Prioritize the right use cases
Next, identifying the right AI use cases to pursue is vital to an effective AI strategy. AI can point to innovation and opportunities across the organization, but an ideal use case will be highly valuable, actionable, feasible, and scalable with your business goals.
To identify AI use cases, start by connecting your business objectives with specific AI domains, or areas where to focus investment. A domain might be a department, a core product, or even an end-to-end process—like the customer experience or a workflow in a contact center—which will include several use cases.
Why focus on domains first and then their corresponding use cases? Simply, an isolated AI implementation or two is far less likely to move the needle than multiple use cases operating in unison. By identifying multiple use cases that complement each other, you can maximize the value of AI implementation.
Once you’ve chosen a domain like customer experience or AI customer service to focus on, you’ll generate a list of use cases in that domain, such as conversational chatbots or automated ticket routing. To whittle down to your best use cases, you can rank them by actionability, feasibility, and business value to determine which provide a near-term win for AI efforts as well as long-term strategic business value.
3. Assess your current capabilities
Next, it’s important to assess your organization’s readiness to leverage AI. This involves auditing your current capabilities in terms of infrastructure, expertise, and data, looking to identify the gaps in skills, tools, or resources required to effectively implement AI.
AI relies heavily on data to function, so conducting a data audit is key. A data audit evaluates the quality, accessibility, and governance of your data assets. It will be crucial to helping you understand data infrastructure, eliminate siloes, and connect information across departments to create integrated infrastructure capable of supporting AI at scale.
You can track your AI readiness and capabilities on an AI scorecard. Beyond data, this is a centralized assessment of your organization’s readiness to adopt and integrate AI technologies—can help you gauge your capabilities and align stakeholders.
This scorecard helps you to evaluate:
AI adoption: How ready are you to integrate AI, data platforms, software, and analytics across departments?
AI architecture: Is your digital infrastructure robust enough to ensure optimal performance by enabling the seamless, standardized data flow between systems
AI capability: How agile are your processes? How AI-skilled is your development team? Are you structured in a way that supports innovation?
With an AI scorecard, your organization can identify gaps and prioritize actions for both near term goals and ROI, but also long term goals.
4. Create an AI roadmap
Once you have a clear sense of your goals and capabilities, you’re ready to create a roadmap that outlines the necessary steps and resources to bring your AI strategy to life. In other words, start to conceptualize the steps it will take to smoothly transform your AI projects and ideas into impactful solutions.
This involves setting milestones and deadlines for each stage in the timeline and establishing the KPIs that will enable you to evaluate the success of each AI initiative in the roadmap. It also involves determining the tools and talent needed to support each stage and initiative and ensure effective implementation, specifically:
Data: The lifeblood of AI systems are vast sets of high-quality data, so craft a data strategy that determines if existing data are clean and ready, or new datasets will be needed to get accurate results from AI. Consider establishing processes around data collection, data quality, storage, access, compliance, and privacy to ensure safe use.
Algorithms: AI algorithms are the rules that enable machines to learn, analyze data, and make decisions. An AI model is a machine that’s learned to operate based on a machine learning algorithm. Expertise is needed to effectively manage both, so determine who will deploy your algorithms, as well as design, develop and validate AI models.
Infrastructure: Determine where your AI systems will be hosted and how they will be scaled. You can deploy AI on your own infrastructure or on third-party cloud platforms.
Talent: Assess the readiness of and skills gaps within the organization to implement AI initiatives. Determine if a talent pipeline exists to fill roles such as data scientists and developers or if skills can be developed internally through training. Also assess if certain tasks, such as deployment and operations, should be outsourced.
This step is where you dive deep into considering how AI technology will be implemented in your organization, considering the details about how it will be integrated with operations, implemented, orchestrated, and scaled in alignment with your business goals.
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5. Select the right AI tech stack
The next step in your AI strategy is to choose the various AI technologies that will advance your business goals.
Based on the problems you’re trying to solve with AI—automated customer service, enhanced customer experience, improved decision making—you’ll choose the tech that provides the capabilities for your use cases.
Technologies in the AI tech stack can include:
Machine learning (ML): Enables AI systems to learn from data and adapt without explicit programming.
Natural language processing (NLP): Allows machines to understand and respond to inputs in human language.
Generative AI: Creates new content like text, images, code, and more by recognizing patterns in data.
AI agents (Agentic AI): Autonomous entities that reason, make decisions, use tools and real-time data, even learn and adapt over time.
Robotic process automation (RPA): Automates repetitive, rule-based tasks to free up AI agents and humans for more complex work.
Once you’ve created a list of the potential AI tools and systems required in your AI strategy, create a list of vendors and partnerships based on experience, reputation, pricing, etc. You can prioritize procurement based on the phases and timeline of the AI integration project.
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6. Invest in AI expertise
AI technologies are complex, so even the most well-planned AI strategy can fail without a skilled team to implement and manage them. To execute your AI strategy, identify skill gaps in areas like:
Data science: For developing AI models and analyzing data
Machine learning: For implementing and scaling AI models
Data engineering: For building data pipelines, orchestrating components, and optimizing model performance
If you find skill gaps, you can choose to recruit new talent, outsource, or upskill your existing employees with AI training. AI is evolving rapidly, so encourage teams to stay updated on breakthroughs and explore innovative ways to solve problems with these technologies.
Collaboration is equally essential to a successful AI strategy. Rather than designating a core of AI experts in your organization that are shared between departments, AI initiatives and the expertise behind them should be aligned with business goals across departments.
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7. Establish an ethical framework
Using AI responsibly involves understanding its ethical implications, such as data privacy, bias, and transparency. Any organization that rushes to implement AI without an ethical framework is at risk of serious consequences, such as legal violations, loss of customer trust, and lasting damage to its reputation.
To avoid these pitfalls, organizations can define clear ethical standards that address:
Data privacy: AI can handle sensitive customer data
Fairness and bias: Model training data must be free of bias that can impact outputs
Algorithmic transparency: Ensure models are interpretable and can explain their decisions
AI must be monitored regularly for potential biases and compliant outputs, while transparency practices like auditing the decision-making of models allow for scrutiny and accountability. Importantly, implement a robust data governance policy that details how you collect, store, and use data to guide management of data privacy, security, and integrity to mitigate risk.
8. Start small, iterate, then scale
The final step in your AI strategy is to launch a small pilot project, testing its feasibility and value before you scale it up. For example, an ecommerce retailer could start by using AI to optimize warehouse operations and route delivery in one region before expanding globally.
AI evaluation involves monitoring metrics around return on investment (ROI), user satisfaction, as well as model performance—using feedback loops to refine models and improve outcomes over time. Our example ecommerce retailer would, for instance, track the precision and efficiency of its AI model decision-making in tandem with real-work outcomes like efficiency gains or faster delivery times. With performance data in hand, it could identify areas of improvement before scaling AI across existing workflows and systems for adoption across the organization.
Example framework of AI strategy for business
Create an AI strategy for long-term success
AI is a transformative opportunity that must be mapped to concrete, well-considered goals to deliver best results. An AI strategy must address the people, processes, and organizational factors at play to pave the way for effective implementation.
Additionally, adopting AI across your organization is a perpetual state of transformation: technology changes, ecosystem changes, market changes. This involves learning and adapting quickly, and an AI strategy framework is the guide, allowing you to be nimble and prepared for change without compromising your core competencies, mission, and competitive advantage.
If you’re looking for help with creating your AI strategy, Sendbird can help. Our team of AI experts includes machine learning engineers, data scientists, and more are skilled and ready to help enterprises craft and deploy an effective AI strategy at scale. Our robust AI agent platform makes it easy to build AI agents on a foundation of enterprise-grade infrastructure that ensures optimal performance with unmatched adaptability, security, compliance, and scalability. Contact our team of friendly AI experts to get started.
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