When Is An Organization Really Ready for AI?

There's a misconception that companies need to have perfect data before implementing AI. It’s simply not true. In reality, employees are going to use AI regardless. And doing nothing will cost companies. So how can organizations use AI when data isn’t perfectly clean? How can AI be used to clean data? What about ERP data and AI? Bill Aimone and Peter Purcell discuss data challenges and approaches to using AI even when data is messy (hint: AI might actually make handling “dirty data” even easier).

AI can do a lot of the work now… so what makes people stand out? In this episode, we chat about the future of work: AI may take over entry-level tasks, but companies still need people to grow into future leaders. One thing is clear: people who use AI will have an advantage over those who don't. We also dig into why the trades may boom as AI maxes out what white-collar work needs and demand increases for trades. We cover how AI is reshaping education, why universities will be slow to adapt, and what people can do to stay ahead.

Millions in, and most companies are still running exactly the way they did before AI showed up. Why aren’t they seeing return? AI's efficiency gains are being swallowed up by three big problems: endless meetings, too many management layers, and lengthy approvals. AI isn't delivering ROI at big companies because the organizations themselves haven't changed to take advantage of it. In this episode, Bill Aimone and Katy Wyrick talk about how leaders can redesign how work gets done to really capture value from AI and see returns.

Feeling the pressure to "do something with AI" before the competition gets ahead? A lot of business leaders are in this boat. Getting started doesn't require a massive AI transformation project, a big budget, or months of approvals. In this episode, we break down how starting small with the tools you already have access to is often the smartest move, and practical ways you can begin adding real value with AI right now, in whatever project or task you're working on.

In this episode, Todd Boutte and Landon Guyton share how we help organizations evaluate if and when they’re ready for AI at an organizational or enterprise level. This includes technology readiness, foundational resources, people/culture, and budgeting resources for AI. They also discuss common pitfalls around AI, handling skepticism, and why strategy must come first.

Consulting firms have three typical paths to help organizations develop an AI strategy. Each has strengths and shortcomings. In this episode, we’re exploring these approaches and sharing our own recommendations for developing an AI strategy that prioritizes long-term capability over short-term flash. The companies that win will be the ones that treat AI like engineering, not magic.

Worried about losing your job to AI? In this episode, we’re talking about several things you can do to make sure that doesn’t happen. OK, we’re kidding. But jokes aside, we’ve noticed some common pitfalls when it comes to implementing AI. Whether it’s neglecting an AI strategy or trying to do too much at once, certain oversights can prevent AI initiatives from ever gaining traction. Bill Aimone and Todd Boutte chat through common mistakes that derail AI implementations and how to stay on track.

Many companies are hesitant to adopt AI because of the potential for incorrect outputs. In this episode, Bill Aimone and Peter Purcell share strategies on how to prevent AI hallucinations, which occur when AI provides incorrect or misleading answers. AI hallucinations happen all the time in large language models, but they’re preventable with the right AI data strategy, proper training and guardrails, and human governance. Bill and Peter discuss how to adopt AI effectively and securely without putting the business at risk and share practical advice for organizations serious about implementing AI.

In this episode, Todd Boutte and Emma Demmerle discuss how process documentation impacts operations. Many companies have processes that exist in someone’s head, but they aren’t documented, and no one really knows what goes into employees’ day-to-day activities. Todd and Emma share practical advice for documenting processes and the broader value of documentation to improve visibility and decision making. They also share their experience working with companies who have uncovered inefficiency and risk and have seen significant, tangible improvements from process documentation.

Many companies want to implement AI but don’t know where to start. They've taken a wild buckshot approach, trying different things to see what works. But the business is at risk, it’s costing time and money, and nothing is as expected.  In this episode, Bill Aimone and Todd Boutte discuss a strategic approach to AI using the ACE Method that helps companies determine the right uses for AI. The end result is meaningful and actionable use cases the entire organization can understand and get behind. AI projects can solve real business problems and add measurable value without putting data or finances at risk.