Our mission: to make advanced AI useful in everyday business so people can do better work.
Founder & CEO, Morning Person
A commercial background.
A technical point of view.
Before founding Morning Person, Kenneth spent approximately eight years at Google and three years each at TikTok and OpenX, working in technology sales, partnerships, and technical advertising integrations.
He holds a master's degree in artificial intelligence from The University of Texas at Austin.
He founded Morning Person to bring that commercial and technical perspective to the practical decisions businesses face as they adopt AI—understanding what matters, choosing technology thoughtfully, and building a company customers can rely on.
Kenneth on LinkedIn Previously at
Studied at
What we believe.
Morning Person exists because good technology isn't the whole job. Businesses have people, knowledge, responsibilities and ways of working that deserve to be understood before anyone introduces another AI tool.
We believe technology should respect the knowledge and judgment already inside a business. It should make useful information easier to understand, help people make better decisions, and keep responsibility clear.
We can be ambitious about AI without making every business problem an experiment. That's the spirit behind Morning Person—and the principles that guide our work.
Frequently asked questions.
Are you a SaaS or AI software company?
Not in the usual sense. Morning Person helps businesses choose, implement, integrate and manage AI. Sometimes the right solution already exists. Sometimes something needs to be built. We recommend what fits your business rather than forcing every problem into one product.
Are you replacing our IT provider?
No. Your IT team or managed service provider continues to look after the infrastructure and services it manages. We work alongside them on AI-enabled workflows, integrations, permissions, adoption and ongoing management, coordinating wherever responsibilities meet.
Do we need to replace our ERP or buy expensive software?
Not necessarily. We start with the systems you already have. If an existing tool can do the job reliably, we'd rather use it than introduce unnecessary software. New tools or custom development should have a clear business reason.
How do we know whether our data is ready for AI?
That depends on the work you're trying to improve. We look at the relevant information, how trustworthy it is, where it lives, who may access it and what happens if something is missing or wrong. One useful workflow doesn't necessarily require a company-wide data overhaul.
What can AI do on its own, and who approves it?
That depends on the workflow and the risks. We help define what AI can read, prepare, recommend or change; what requires human approval; what is recorded; and who owns the result. Greater capability does not automatically mean greater permission.
How will we know whether AI is worth the investment?
We start with a business outcome: less re-entry, fewer corrections, faster responses, shorter processing cycles or more reliable follow-through. We agree on relevant KPIs before implementation and examine whether the work actually improves—not just whether the AI looks impressive in a demo.
What's with all the Monstera plants?
We think technology could use a little more humanity. More sunlight and living things, fewer futuristic tech clichés. We believe the best technology makes life easier, respects how people work, and doesn't make anyone feel out of their depth. AI is complicated enough. Working with us shouldn't be.
Our founding principles.
Start with the work, not the AI.
Understand what people are trying to accomplish and what is getting in the way. Choose the intervention that helps: AI, a rule, an integration, a process change—or leaving a working system alone.
In practice: Name the work and the improvement sought before naming the model or platform.
People and agents should work from the same reality.
Reliable data and shared business context let people and agents understand what is known, what changed, what remains unresolved, and what matters next.
In practice: Connect the relevant data, keep its meaning and freshness clear, and make sources traceable. Give each participant the access appropriate to their role.
We don't make AI harder than it has to be.
AI can make technology more natural to use, not just more powerful. We start with how people actually work, speak their language, and change routines only when there's a good reason. We explain what customers are buying, how it works, and who's responsible. We can be technically exact without being pompous. We don't cosplay the future.
In practice: Use familiar tools and workflows where they make sense. Explain choices, costs, limits, and responsibilities clearly. Welcome questions, give straight answers, and make it easy to reach a person.
Your business knowledge should outlive your AI vendor.
The knowledge, processes, decisions, and working history built around a business should remain useful and independent as its technology changes. We design for continuity and against lock-in.
In practice: Keep business context in formats the customer can retain and use. Document exports, dependencies, and what changing providers would take.
Measure whether the work got better.
Activity isn't ROI. Faster execution matters when it helps the business or the people doing the work. Agent runs, dashboards, and usage statistics do not establish value on their own.
In practice: Establish a baseline for costs, time, quality, and customer outcomes. Assess training and adoption alongside economics, and separate measured results from estimates.
Capability is not permission.
A more capable model does not get more authority by default. We define what it may see, infer, change, send, or approve—and where people make the call. Human agency and privacy are part of the design.
In practice: Set permissions, approval rules, and stopping points before enabling actions.
Fast and experimental where reversible. Careful and durable where consequential.
Move quickly when mistakes are contained and recoverable. Take more care when actions affect customers, money, sensitive information, or commitments. Novelty is neither a reason to rush nor a reason to freeze.
In practice: Give experiments room to move; give consequential changes testing, verification, and recovery plans.
Someone owns what happens after launch.
Real use brings changing data, people, processes, vendors, and exceptions. A demo settles none of it. Responsibility must be as clear after launch as it was before.
In practice: Managed systems get continuing review and maintenance. Project work ends with documented ownership and a usable handoff.
We'd like to be the company that handles your AI.
Tell us what's taking too much time, getting lost between systems, or any cool ideas you have that would make your business run better.
Let's talk about your AI