CNBC’s “Oracle of Remote Work” Breaks Down Why AI Integration Will Only Be as Effective as an Organization’s Documentation and Remote-Work Practices
Good documentation makes AI work better: AI can only be as smart as the information it’s given. If a company’s notes, processes, and records are messy or outdated, AI won’t help much. Clear, organized documentation is the key to getting real value from AI.
Remote-ready teams are already ahead: Companies that learned to work well remotely, by writing things down, staying transparent, and using tools to manage work are in a stronger position to use AI effectively. The habits that make remote work succeed also make AI easier to use.
Treat company knowledge like a valuable product: Keeping company information up to date isn’t a one-time project. It needs regular care, just like maintaining a good product. Someone should be responsible for making sure it stays accurate and easy to find.
Darren Murph is a pioneer of remote work and a strategic voice in the future of work. Named an “oracle of remote work” by CNBC and recognized on Forbes’ Future of Work 50 list, Darren helped lead GitLab to become the first officeless company to IPO. Today, he advises top organizations on how to navigate an increasingly AI-first world.
In this conversation, Darren shares why AI is only as effective as the documentation it relies on, how leadership and operating models must evolve, and why remote-first principles are even more essential in a world with AI.
How Darren Murph went from remote-work pioneer to AI strategist
I'm Darren. I was named an oracle of remote work by CNBC and featured in The Forbes Future of Work 50. And I run a consultancy supporting organizations adopting remote work and AI.
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Over the past 20 years, I've supported transformations at Zillow, Vistaprint, Dropbox, Abbvie, Sonos, Mozilla, and more. I was the first Head of Remote at GitLab, which became the first officeless company to IPO. While there, I co-developed organizational design case studies with Harvard Business School and INSEAD. Our team's highly unique operating model notched top-decile performance against McKinsey's rigorous OHI (Organizational Health Index) benchmarks.
Prior to shaping transformations, I earned a Guinness World Record in publishing as the world's most prolific professional blogger. I also authored GitLab’s Remote Playbook and “Living the Remote Dream: A Guide to Seeing the World, Setting Records, and Advancing Your Career.”
Why remote-ready teams have the advantage in AI adoption
I advise my clients that to be AI-ready, you must first be remote-ready.
Remote-first principles are all prerequisites to being AI-ready — like transparency, rigor around documentation, and intentional use of project management software to enable asynchronous work. The AI era is an extension of the remote work era.
In the past, insisting on a documentation culture was a choice. Organizations that took a laissez-faire approach to codifying workplace operations, culture, and ways of working could leverage the physical office to smooth over glaring operational gaps.
In the age of AI, every instance of operational laxness is made obvious. AI can only assist an organization to the degree to which its underlying company documentation is sound. If AI is pulling from outdated or missing information silos, companies will be underwhelmed by the technology's ability to make it more efficient.
Thus, organizations that have already invested in remote-first principles are closer to being AI-ready than those who sat on the sidelines during COVID and tried to keep working with archaic practices.
Why AI isn’t a silver bullet for workplace transformation
The biggest fallacy I see today is that AI is a silver bullet. AI does not create value out of refuse. AI's impact is directly correlated to the quality of an organization's raw materials. Garbage in, garbage out.
I'm addressing this with my clients by implementing a knowledge management audit that forces a hard look in the mirror. We begin by evaluating the company's existing tool stack. Most do not have a dedicated knowledge base. We select the right tool for the size and scale of the company, then stand up a basic taxonomy that includes frequently accessed questions and information. Then, we select the person or people to scale and maintain it.
It's akin to a product launch. Knowledge must be seen as a product within the company, not merely a project.
Most organizations are woefully under-documented and under-resourced. My role is to help leaders realize that managing knowledge is as important as managing security.
How building knowledge infrastructure lays the foundation for AI success
One organization I worked with was struggling to justify the return on investment for great internal documentation. They implemented Glean — a company-wide search platform that uses AI to distill information from every data source the company uses. When seeing how lackluster the search results were after merely layering Glean on top of existing sources, they had an epiphany.
They needed to invest in a new role dedicated to shaping organizational information. This led to new workflows, including training on document validation, recurring initiatives to deprecate outdated information, and operators across the business becoming responsible for building out collections of priority information.
When an AI search platform is your front door to work each day, it quickly becomes clear that the underlying infrastructure needs to be in tip-top shape.
Why effective AI integration demands operating-model overhauls
This is not a moment to look for gentle tweaks or silver bullets. Wholesale changes to the operating model will be required to keep pace.
This means shifting more work to asynchronous methods, leveraging project management tools to reduce meeting overload, investing in a company's knowledge base, and building incentive structures that align with the changes you're asking of employees.
Before you look at an individual's performance, look at the company's responsibility. Individuals will only be able to use AI to increase efficiency to the degree that the company itself bolsters the accuracy and depth of its documented operating rhythms.
Don't undermine your investment in AI by blowing off investments in knowledge management, project management, and culture overhauls.
Don’t undermine your investment in AI by blowing off investments in knowledge management, project management, and culture overhauls.
How AI is redefining culture, leadership, and employee autonomy
Culture will increasingly be built outside of the workplace, and leaders will need to lean into this as opposed to rejecting it. As people have more flexibility and agency in where work happens, progressive leaders will recognize this as a unique way to incentivize top talent.
Instead of forcing them into unnecessary meetings, for instance, they'll invest in tooling that enables work to be done at odd hours. This frees people up to spend more time on family and hobbies during what was typically reserved as "working hours." Leaders who relinquish control will attract the most innovative people.
The employer-employee social contract has fundamentally changed. With corporations investing less in loyalty, top talent are hedging by building portfolio careers. In turn, leaders must know that their top performers may only remain with the organization for 12 to 24 months, as loyalty must be reciprocal to be meaningful.
Leaders who provide tooling and cultural frameworks that enable agency and autonomy will outperform the benchmark in terms of keeping staff around for longer, thereby gaining compounding value from their institutional knowledge.
Real-world example of AI workflow automation in action
One of my most rewarding experiences was leading a team to develop a new software solution that improved our company's efficiency by 30%.
The team was using a content management system that required multiple manual steps. There were only a couple of potential outcomes, but dozens of team members were manually taking these steps multiple times per day.
An AI agent was built with the potential pathways and trained with logic on which step should be taken when considering the circumstances in the prior step. The team automated a repetitive step, which saved a material amount of time. In instances where the AI agent chose the wrong path, it learned from human intervention in order to improve its own success rate going forward.
This project taught me the importance of collaboration and innovation in achieving success.
How AI is changing meeting notes, summaries, and follow-ups
AI is also revolutionizing note taking apps and recapping in meetings. I advise my clients to record and transcribe every meeting, leveraging tools like Fathom.
Note-taking during synchronous meetings has always been vital to excellent meeting hygiene, though quite manual in the pre-AI era. Now, there's hardly a reason for a human to take their focus off the meeting itself. Stay engaged, talk, and discuss. AI will handle the notes and post-meeting to-do list.
How to use AI for strategic brainstorming and creative problem-solving
And AI has proven to power mind mapping software and be an exceptional brainstorming partner. If you're willing to allow AI to expand your viewpoint and contribute to a growth mindset, it's great at injecting creative energy into a project.
For example, say your business is facing a paradox of increasing quality while decreasing cost. With a thorough prompt into a large language model like Claude or ChatGPT, you can ask AI to consider all of the factors, confess that it's a paradox, and ask for it to generate ideas on solving the paradox.
Most modern business challenges have been felt and faced before, likely in industries outside of a human's purview, but surely within reach of AI. Half of the battle is getting past your limiting beliefs as a leader, and seeing the pathway to creativity which AI is masterful at assisting with.
Humans are experts at turning creative ideas into plans that can be executed. Let AI help you begin with a richer data set to whittle down.
Why continuous learning is essential in the AI era
In the rapidly evolving world of technology, continuous learning isn’t just important; it’s essential.
The industry demands not only technical skills but also adaptability and problem-solving abilities. One must be proactive in seeking new knowledge and experiences.
Attending workshops, participating in online courses, and engaging with the tech community are excellent ways to keep up-to-date. Moreover, collaborating with peers and mentors can provide valuable insights and foster innovation.
In the rapidly evolving world of technology, continuous learning isn’t just important; it’s essential.
Follow along
You can follow Darren’s work on LinkedIn and visit darrenmurph.com to explore more of his work in remote strategy, AI integration, and organizational transformation.
More expert interviews to come on People Managing People.