How Mik Kersten turns AI productivity into measurable outcomes
Mik Kersten is Author & Advisor at Lymyt. Lymyt helps enterprises adopt AI-native operating models that turn AI-driven productivity into measurable business outcomes, and advises leaders on redesigning how organizations structure, govern, and measure work for the age of AI. This conversation explains practical choices for converting AI-amplified output into actual business value.
Convert AI-amplified output into measurable business results by redesigning operating models so outcomes are defined, owned, measured and reviewed. Use AI to automate administrative and financial work, but keep nuanced storytelling and persuasive presentations human-driven.
What’s the quick origin story of your business, and what makes what you do genuinely different from the other options your customers are looking at?
I founded Lymyt after spending decades building software, developer tools, and technology companies, and seeing firsthand that AI was removing the constraint on producing knowledge work, but not on creating business value. I wrote Output to Outcome to help leaders make this transition, and founded Lymyt to implement the concepts in the book and turn AI-driven productivity into measurable business results.
Where does most of your new business come from today, and where do you wish it came from?
New business comes from inbound interest from executive leaders needing support with their transformations. Most of the business is with large enterprises, and my hope is that more mid-sized companies and government agencies become part of our customer base, as both are areas where I want Lymyt to have an impact.
What’s one thing you’ve actually handed over to AI in the last year, and one thing you tried it on and went back to doing the human way?
Having started a company before, I’ve been amazed by how much of the administrative and financial work of running the business I can now hand over to AI. This allows me to focus much more of my time on value creation.
One thing that has not worked for me is having the latest models generate presentations based on my work. The nuance of the storytelling gets lost, so I’ve gone back to creating low-fidelity presentations by hand. The outcomes are better and more persuasive.
Have you noticed a change in how customers find you or what they already know before they reach out? Is anyone arriving through AI assistants like ChatGPT yet?
I’ve found there to be a clear split. Some customers use AI to arrive much further into the conversation than they would have before, allowing us to hit the ground running. Others use AI to gain only a surface-level understanding, then bring that into the interaction as if it were deeper expertise. That can actually slow down the engagement and the customer journey rather than accelerate it.
If you were advising someone in your industry on all of this for the next 12 months, what would you tell them to actually do, and what would you tell them to ignore?
With AI amplifying output, my advice is to focus relentlessly on outcomes. That is harder than it sounds because most management systems, organizational charts, and incentive structures are still designed to optimize outputs. Those outputs might include products shipped, projects completed, features delivered, or work produced.
Over the next 12 months, I would focus on redesigning how outcomes are defined, owned, measured, and reviewed, as well as how teams are empowered to deliver them. Then, I would focus on identifying the organizational constraints and processes that prevent 10x output amplification from translating into business value.
I would also prioritize adaptability in the operating model itself. AI is changing business models, roles, and sources of competitive advantage so quickly that rigid structures will become a liability. As business models evolve, organizations need to be able to reconfigure teams, ownership, decision rights, and workflows as constraints shift.
Thank you to Mik Kersten and the team at Lymyt for sharing what actually worked with Leaders Perception readers.
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