Trappe Digital LLC may earn commission from product clicks and purchases. Rest assured, opinions are mine or of the article’s author.
Some new tools can eat up more time than is helpful. Certainly, that can be the case when it comes to testing and testing and testing new AI tools and processes. But at some point, the AI-powered process will either make things more efficient or help drive more results. So how to scale AI is the question I tackle with Jeff Coyle of Market Muse, on Episode 681 of “The Business Storytelling Show.”
Join us.
I’ve always been a fan of improving workflows and with the emergence of AI, the possibilities are certainly endless. But there are also many issues that need to be avoided, including:
- Does this tool actually help us do whatever we need to do better than we could without it?
- If it saves time and if it does, ensure it’s not just adding time elsewhere.
- Are the right human quality checks in place? For example, even the awesome AI-powered Grammarly at times gives recommendations that a human editor would or should not approve.
So when it comes to how to scale AI, there are plenty of things to consider to keep in mind, but if nothing else, we need to make sure it’s actually helping the process and most importantly leads to results. And it should definitely not hinder it.
6 steps to get right how to scale AI right
Jeff mentioned several important steps. Let’s look at them.
Using business data and expertise
It’s important to use your own business information in AI. Jeff warns that if you use the same AI process for more than a year, you might fall behind.
Human-in-the-loop process
There should be a “human-in-the-loop” process for AI content creation. This means people can guide the AI to avoid things they don’t like. You can add your knowledge to shape what the AI creates. This helps make sure the AI’s work sounds like your brand.
Scaling best practices
Instead of making more content, use AI for tasks that improve quality. This can include jobs teams know are good but rarely do because they take too much time. For example, before Opus Clips, I wouldn’t do video soundbites from podcasts.
Improving content “batting average”
Not all content will perform well. There’s definitely “batting average” to talk about. How many of your content pieces do well? Jeff suggests using AI to increase this number.
“Before, only one piece might succeed, but now 40 or 50% can be successful,” he said.
Continuous tool evaluation
Coyle advises checking your AI tools often. He says this should never stop because AI changes quickly. If you use the same process for more than a year, you might miss out on new, helpful AI tools. He suggests looking for new tech that could make your work easier or do new things.
Avoiding generic “Main Street” solutions
Jeff warns about AI tools that give the same advice to everyone. He calls this “Main Street” advice. He explains that if a tool gives the same tips to every company, it probably won’t work well for you. He stresses the importance of AI solutions that tell your unique story and show what makes your business special.