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6 Steps for CIOs to Launch AI in Their Organization

Artificial intelligence is transforming businesses in a big way. Large language models (LLMs) like ChatGPT, Bard, and LLaMa have brought AI into the spotlight, showcasing impressive new capabilities. As a result, AI now sits at the top of many CEOs’ agendas. CIOs need to get prepared by developing a thoughtful AI adoption strategy – one that harnesses the benefits of AI while managing potential risks around trust and ethics. With AI set to reshape entire industries, CIOs without an AI plan risk falling behind competitors who are aggressively embracing these technologies.

Here are 6 steps CIOs can take to implement AI:

1. Develop an AI Vision, Strategy and Roadmap

When crafting your AI strategy, start by defining a long-term vision and priorities that align to business goals, along with governance policies and ethics guidelines for responsible AI development. Next, establish a multi-year roadmap of use cases, platforms, and capabilities that are needed to achieve the vision. Prioritize foundational elements like data infrastructure, AI workflows and talent acquisition.

2. Appoint an AI Leader

Consider appointing a VP of AI or similar executive role. Look for a candidate with business acumen specific to your industry who also possesses deep expertise across AI, data science and engineering. For example, a retail company could seek out a leader well-versed in retail supply chains and operations, along with AI techniques like forecasting, inventory optimization and personalized recommendations. This combination of business knowledge and technical prowess can drive an AI strategy tailored to the organization's specific needs and opportunities.

3. Define the AI Investment Levels

When adopting AI, define your level of appetite for investment and customization. To pilot capabilities with minimal investment, leverage public APIs or embedded AI like Microsoft's CoPilot. Feeling more ambitious? Open source enables full customization, but requires substantial investment in data, skills, and computing resources. The pinnacle is developing proprietary custom models fully aligned to your unique needs - albeit requiring the highest investment. Clearly defining your desired target level focuses precious time and resources on the AI use cases with greatest business impact.

4. Develop the AI Organization and Operating Model

Structuring your AI organization thoughtfully is crucial for execution. Key roles like AI product managers, developers, and data engineers need to collaborate closely. Consider a centralized team working across business units, or a federated model with embedded AI experts in each unit, or a hybrid approach. Establish processes for development, testing, monitoring and controls of AI systems. It’s also important to define success metrics aligned to business goals (e.g., cost savings, revenue growth, customer satisfaction). For example, a bank may target higher customer satisfaction scores and lower churn from AI-enabled personalization.

5. Identify High-Potential AI Use Cases

Prioritize use cases where AI can drive automation, personalization, or insights. For example, deploy customer service chatbots to boost efficiency, implement AI pricing algorithms to maximize profitability, or use computer vision in manufacturing to automatically detect defects. Focus first on areas with accessible data where AI can generate clear value.

6. Piloting and Implementing AI Projects

After developing a robust strategy and roadmap, focus on executing a few pilot projects to validate capabilities and build organizational proficiency. Take an agile approach - implement, collect feedback, learn, and continuously improve the models or capabilities. For example, an auto insurer could pilot AI for personalized premiums or automated claims processing. Leverage pilot outcomes to demonstrate tangible ROI, address potential risks, and gain insights to inform further AI rollouts.

To fully harness the power of AI, CIOs need to take a strategic approach - identifying high-potential use cases, outlining a long-term roadmap, piloting projects to build skills and momentum, and driving widespread adoption across the organization. With the right vision, governance, and disciplined execution, IT leaders can spearhead AI initiatives that deliver tangible business value in the near-term while building an enduring competitive advantage.

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