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The ongoing adoption of Artificial Intelligence by large organizations has been unlike any previous technological revolution — from mobile devices to cloud computing to the Internet itself — in that the pressure to integrate AI (and particularly Generative AI) into products and services is coming straight from the C-Suite. Executives and Board members alike recognize the need to either adapt quickly or risk being supplanted by more agile competitors. After all, even a small company with just a few core team members could leverage AI tools that magnify their efforts and take market share from established players.
At the same time, these same leaders also must contend with pushback from end users who worry that AI will put them out of a job. Fighting off potential competitors while simultaneously staving off employee panic is no easy feat.
The good news is that enterprise AI adoption, like any other technology challenge, can be tackled in a structured and logical manner that addresses all the relevant needs. The key, as ever, is to focus on strategic goals first, and then choose the right technology to support those efforts.
Rather than simply stumble blindly through the world of AI, organizations should begin by thinking about practical solutions that will drive real, measurable business value. There’s no better way to do this than to experience AI for yourself.
- Conduct workshops to identify business drivers. Start by understanding your key business drivers, like revenue growth, user retention, or customer satisfaction. Then, identify where AI tools can make a difference and — equally importantly — where they can’t.
- Create an AI sandbox. Build a secure AI sandbox environment where teams can experience their data and workflows in action. (“Secure” in this case means that not only will the organization’s proprietary data be protected from unauthorized access, but the data also won’t be used to train any third-party AI platforms.) Then you can focus on refining your prompt engineering to get the results you need.
- Focus on a single business outcome. Identify and focus on achieving a single, clear business outcome that will deliver measurable value. Success comes from solving a specific problem with AI — not from broad, unfocused ambitions.
- Launch quickly and iterate quickly. Whether you succeed or fail, move quickly. The goal is to avoid getting stuck in perpetual Proof Of Concept phases. Aim to get a prototype in front of real users within 90 days to start gathering usage data.
- Adjust based on user feedback. Once you have feedback from users, learn from it and incorporate it into the product. You may need to revisit your data, adjust your interactions, or refine workflows and processes.
Once you’ve had your initial success, it’s time to improve the experience, integrate AI into the broader enterprise infrastructure, and quickly scale up.
- Enhance product quality. When used improperly, GenAI tools are famous for delivering “hallucinations” in their responses. Fortunately, by using proven techniques like Retrieval Augmented Generation (RAG), you can ensure that any AI outputs are strictly grounded in your data.
- Leverage strong, structured engineering. Good engineering can make the difference between success and failure. Experienced software architects and developers know how to break down your content into manageable pieces, convert it into machine-friendly embeddings, and develop detailed query strategies to get reliable results from vector databases and search engines.
- Experiment with different AI models. There’s a veritable cottage industry of new AI companies and platforms. In the realm of Generative AI alone, users can access OpenAI’s series of GPT models, Anthropic’s Claude, and Meta’s Llama, just to name a few. Each AI model has its own strengths and weaknesses. Ensure that you don’t tie yourself to any one single solution, so you can experiment and swap them in and out as needed.
Like all digital transformations, AI adoption isn’t just a tech solution — it’s a people solution.
- Involve cross-functional teams. AI isn’t just for technologists; it’s the bridge between real users and real data. Involve experts from Sales, Marketing, Operations, Product, and other teams that understand the customer experience and can help guide the prompt engineering process to achieve meaningful outcomes.
- Harness the expertise of non-technologists. Non-technical colleagues are often great writers and communicators, and they can play a key role in prompt engineering by clearly articulating what the organization needs from the AI tools.
- Align goals across the organization. Ensure everyone is aligned and rowing in the same direction toward your strategic objectives.
- Don’t Go It Alone. Choose the right technology partner who has done this before, understands the different AI platforms, and can help facilitate your journey. Not only will that speed up the entire exploration and implementation process, but it will allow you to focus on your business strategy and goals instead of getting lost down the AI tech rabbit hole.
Top-down pressure from the C-Suite often can feel like a crushing weight for technology projects and teams. But handled well, it can be an opportunity. After all, this pressure represents a sort of pre-approval for promising, new AI initiatives.
By taking a structured approach — workshopping use cases, starting small, iterating quickly, keeping people at the center, and choosing the right guides for your journey —organizations can quickly identify and unlock real value with AI.
Remember, it’s not just about technology. It’s about solving problems, enhancing products, and driving growth.
Erik Schwartz is the Chief AI Officer at Tricon Infotech. Find him on LinkedIn or at [email protected].
