By Calvin Scharffs
Generative AI is not just making waves in the news—it's transforming our work processes. By examining patterns in existing data, this technology can rapidly generate text, images, audio, and video. Naturally, business leaders across various industries are posing critical questions: How do we embark on our AI journey? Which tasks are most appropriate for AI? How can we transition from merely experimenting with AI tools to strategically implementing them?
To answer these questions, I spoke with Christina Nolan, VP of Delivery Solutions and an emerging voice in practical AI implementations. Christina has become a sought-after speaker at industry conferences because she excels at explaining how organizations can harness AI’s potential while avoiding common pitfalls.
CS: You’ve been giving a popular presentation on the stages of AI adoption. Can you walk us through them?
CN: Absolutely. I see four distinct stages: crawl, walk, run, and fly.
Most organizations are at the crawl or ad hoc stage. Employees experiment with AI tools to boost productivity, but formal strategies or guidelines are needed. This stage can introduce risks, so we encourage companies to develop a plan for managing AI adoption early.
The walk happens when organizations identify areas where AI can make a difference. They implement structured solutions to automate repetitive tasks, allowing their teams to focus on creative and strategic work.
Run is the stage where companies take a systematic, strategic approach. AI is implemented with clear ROI goals, use cases span multiple departments, and senior leaders oversee adoption through a comprehensive AI strategy.
Fly is the most advanced stage, where AI is fully integrated across operations. Only some organizations outside of companies like Microsoft or Google are at this level. At this level, AI drives documented ROI, and it’s treated as a core business strategy.
CS: Many organizations want to start using AI, but the options can feel overwhelming. What’s the best way to begin?
CN: First, it’s essential to recognize that your AI journey has probably already started. Employees are bringing generative AI tools into the workplace, just like they introduced PDAs and instant messaging years ago.
When getting started, look for tasks with clear patterns and known variables. Generative AI is particularly good at tasks with complicated data sets but predictable outcomes, such as analyzing customer reviews or creating project plans. But remember that while AI excels at handling large amounts of structured data, it could be more effective in complex, unpredictable situations where human judgment is required.
CS: Can you give me an example of a complicated data set with predictable outcomes?
CN: Sure, SEO is a great example, illustrating the importance of AI adoption. On a fundamental level, SEO is a complex mathematical equation, and because SEO follows mathematical formulas, AI can optimize content far more efficiently than manual methods.
This creates an interesting dynamic: organizations that don’t use AI for tasks like SEO will find themselves competing against technically superior content. The stakes are getting higher—AI won't just save time; it's raising the quality baseline for everyone.
The key is recognizing where AI can handle these technical calculations, freeing humans to focus on creative and strategic work - like understanding the "why" behind the data rather than just gathering the "what."
CS: Security concerns are at the top of many organizations' minds. How can they approach AI implementation safely?
CN: Start with non-sensitive tasks like generating meeting templates or presentation frameworks. Use enterprise-grade AI tools for sensitive information that offer strong privacy protections and clear data-handling policies.
It’s also critical to set clear guidelines about what can and cannot be shared with AI tools. For example, organizations should explicitly prohibit the input of client names, financial data, proprietary information, or strategy documents.
In early 2023, Samsung engineers accidentally leaked internal source code by inputting it into ChatGPT. All organizations should learn from this mistake and prevent it by creating clear protocols for using AI tools.
CS: As you said, employees already use AI tools at work. How should organizations manage this grassroots adoption?
CN: Grassroots adoption is natural and often beneficial. Early adopters can become internal champions who help the organization scale AI usage effectively.
The key is formalizing this enthusiasm by creating guidelines and identifying opportunities for structured AI implementation. Don’t try to build an AI department overnight—let adoption grow organically while managing risks.
CS: We hear so much about the importance of creating good prompts … the garbage in / garbage out issue. How can users ensure they get the best outputs from generative AI?
CN: Think of prompts like recipes—they need specific ingredients. A good prompt includes:
- Your role (e.g., "I'm an investor relations analyst")
- Your objective (what you're trying to achieve)
- Examples of what good looks like
- Examples of what to avoid
- Desired output format (e.g.,”HTML” “a PowerPoint deck,” or “a bulleted list”)
The more specific your instructions, the better your results.
CS: Any final advice for organizations just starting their AI journey?
CN: Everyone is figuring this out together, so don’t feel pressured to have all the answers. Starting with ad hoc experimentation is entirely normal.
The key is to move forward thoughtfully. Focus on clear use cases, establish guidelines, and remember that AI enhances human capabilities—not replace them. The organizations that succeed will find the right balance between innovation and responsibility.
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About the DDH AI Council
The DDH AI Council was founded to address a growing concern: the widening divide between organizations that embrace generative AI and those that are hesitant to adopt it. Generative AI is rapidly reshaping the way we work, raising the overall caliber while enabling teams to innovate faster. We understand that for many business leaders, generative AI is still an unknown technology and one that comes with a lot of risks. Our goal is to demystify generative AI, and to provide the education and insights business leaders need to build a roadmap for its adoption, with full confidence that its use will be safe and transformative. 1
1 Disclaimer: The responses provided by this artificial intelligence system are generated by artificial intelligence based on patterns in data and programming. While efforts are made to ensure accuracy and relevance, the information may not always reflect the latest data and programming news or developments. This artificial intelligence system does not possess human judgment, intuition, or emotions and is intended to assist with general inquiries and tasks. Always conduct your own independent in-depth investigation and analysis of ANY information provided herein, and verify critical information from trusted sources before making decisions.
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