Most businesses today struggle with mountains of data that never quite translate into actionable insights. They invest heavily in analytics and AI solutions, only to find their teams still drowning in manual research and fragmented information systems. Kyle Johnstone, general manager of Business Intelligence Solutions, has spent over two decades helping companies bridge this gap between data potential and business reality.
Fixing the Hidden Data Problems
Johnstone runs Business Intelligence Solutions and has worked on more than 500 transformation projects. He keeps seeing the same problems everywhere he goes. “What I’ve noticed is that most companies have issues with data clarity, data quality, as well as data unification,” he says. Companies want the sexy AI stuff, but they skip the boring foundation work. Here’s what actually needs to happen first. “The idea basically is we need to get the data together, get it organized, and get it into a usable format,” Johnstone explains. Not glamorous, but it’s the difference between AI that works and AI that burns through your budget. His projects have consistently delivered 10x ROI, but only because he makes companies do the groundwork first.
Applying Three Rules for AI Success
Johnstone doesn’t mess around when it comes to building solutions. There are three things that make or break every project:
Value First: Every solution needs to solve a real business problem and deliver measurable returns. If you can’t explain why it’s worth the investment, don’t build it.
Safety Matters: The solutions need to work reliably without causing bigger problems. “We want to make sure the solutions we build are safe and they don’t go off the rails,” Johnstone emphasizes.
Security Always: Your data and systems need protection from day one. This isn’t something you bolt on later when you remember to think about it.
That safety part isn’t just corporate speak. Too many companies have watched their AI systems make expensive mistakes because nobody thought about what could go wrong.
Solving Real Problems Before AI Adoption
The biggest problem with AI projects? Companies fall in love with cool technology instead of solving real problems. “We want to anchor AI to a business problem. Not just an idea, but a business problem,” he says. He makes clients go through design thinking exercises to figure out what they’re actually trying to fix. Johnstone has zero patience for AI hype. “The thing you have to realize is that AI is not a magical thing. It’s not something that’s going to take over the world, and you do have to make sure that it’s programmed and tuned to the problem you’re trying to solve.” Most companies skip this step and wonder why their AI project fails.
When Johnstone isn’t fixing data problems, he teaches race car drivers how to go faster around tracks. Turns out there are lessons here for business too. “There’s something that’s been said that speed isn’t everything. It’s technique, it’s polish, it’s knowing exactly where to be in a corner,” he explains. The racing analogy works better than you might think. “Speed is necessary, but agility is what wins the game,” he notes. His company uses six accelerators and pre-built models to get clients moving fast, but the real magic happens when they customize everything for specific business needs.
Deploying AI That Actually Gets Used
Here’s something most consultants won’t tell you upfront. “Most AI projects fail not in design, but in deployment,” Johnstone admits. Companies build beautiful AI models that never actually get used by real people doing real work. His team recently fixed this problem for a client using Salesforce. They built a system that scrapes LinkedIn and Google data, runs it through AI, and drops summaries right into the sales platform. “Imagine if you open up your Salesforce and you can hover over one of your accounts and it pops up all the relevant information that has been released in the last few weeks of that account,” he describes. The sales team stopped spending hours researching and started selling more. “We were able to drive more than 40% more sales,” Johnstone reports.
Empowering People Over Replacing Them
The last piece that most companies get wrong is thinking AI is about replacing people. “We want to equip the people not just the platform,” he says. When people don’t understand or trust the system, it doesn’t matter how smart your AI is. The goal isn’t to eliminate jobs, it’s to eliminate the boring stuff that wastes everyone’s time. “We want to empower your people to be better at what they do by eliminating some of these mundane tasks such as research, looking through knowledge bases,” he explains. Give people better tools and they’ll do better work.
Johnstone’s advice for anyone thinking about AI comes down to this: “Let’s not build an AI solution because it’s a cool idea. Let’s build an AI solution that actually delivers value and empowers your people to be better than what they are.” The companies that get this right don’t just save money. They actually get ahead of their competition.
Connect with Kyle Johnstone on LinkedIn to explore proven ways to make AI deliver real results.
