From Problem to Action: A Strategic Framework for Adopting AI in Business (or Any Organization)
As generative AI continues to boom, organizations are quickly adopting tools to supplement their needs. While many C-Suite executives see the value in using generative AI to streamline tasks, a study from Adecco found that only 10% were confident that their workforce is ready for the advances AI is bringing.
In Andrew Wu’s opinion, the adoption of AI tools can lead to many great discoveries, but they can also translate into overwhelming tasks that don’t yield the expected results. Wu is an associate professor of technology and operations at the University of Michigan’s Ross School of Business, and he’s been helping companies realize the benefits of AI integration. In his open online course, Generative AI in Business, he introduces his strategic framework for AI adoption, designed to help organizations make informed decisions and achieve tangible results.
Overcoming AI Adoption Challenges
Working off his extensive knowledge of organizational structures and technology, Wu was excited to help organizations launch into AI adoption. During consultations with teams, Wu noticed a pattern that accompanied the excitement of tool adoption: executives would come up with a laundry list of problems AI could solve, but struggled to translate them into practical solutions.
“What you want to do is to make sure to use a structured, rigorous approach to evaluate and uncover additional areas of value in these ideas,” said Wu.
For him, while idea generation was useful, the best value for businesses meant executing on a few feasible tasks. Prepared for the continued interest in AI adoption across industries, Wu knew he could use this moment to help people take that structured approach to AI adoption. It quickly became clear that there wasn’t a playbook that helped leaders focus their ideas and generate solutions leveraging this new technology.
The PAD Framework: A Roadmap to AI Success
In Generative AI in Business, Wu highlighted what he sees as essential steps leaders can take to understand the importance of a strategic approach to generative AI integration. Here, he presents the Problem, Abilities, Data (PAD) framework: a step-by-step approach to supporting an organization’s AI strategy.
Problem: The first step is pinpointing the areas where AI integration will deliver the most significant value to your business. During this step, Wu guides learners to:
Consider employee, stakeholder, and legal considerations related to AI adoption
Conduct a brief analysis depending on your business type to identify pain points and provide specificity to your argument
Translate the problem into a compelling argument that stakeholders can easily identify the qualities they’re gaining from the adoption
Abilities: This step focuses on determining your tool's specific capabilities to address the problem and yield results. Wu encourages you to:
Understand the range of capabilities generative AI tools have
Determine from the spectrum of abilities which align with your problem
Identify the current solutions that generative AI offers
Align the tool abilities and solutions with the spectrum of identified needs
Data: The final step is collecting the right data to power your generative AI solution.
Create a catalogue of data relevant to your problem
Develop a process to ensure that your data’s quality will yield effective results
Inject your data into your AI solution through structured prompts, retrieval systems, and continued fine-tuning
Actionable Learning for Real-World Impact
Throughout the course series, learners can access assignments and exercises that lead them through the framework and help them think more deeply about their AI adoption. The framework functions for a range of business types, from large organizations to small businesses across industries, allowing them to apply strategies to real-world problems. Together, Wu believes these steps will bring leaders and individuals to the heart of what they want: action.
“My optimistic prediction for the future of business in the near term, in the next five years or so, is that we are going to have a golden age of business,” says Wu. “The connection between business decision making and data is going to be much, much closer thanks to the accessibility that AI tools [can] bring.” By following the framework, Wu hopes learners can help bring good problems to the forefront, allowing them to offer up even better solutions with the help of generative AI.
Generative AI in Business is available to all learners on Michigan Online.