Most companies don’t understand that today’s AI tools are capable of fundamentally transforming how daily knowledge work is done.
This is because they’re using AI in an unsophisticated way and aiming it at the wrong place.
But this level of transformation is already happening, as millions of knowledge workers have figured out, and as enlightened companies are starting to recognize.
Technology Officer & Futurist, Citrix.
To delve deeper, you first need to understand that most knowledge work is invisible. The essence of knowledge work—thinking, processing, judging, ruminating, planning, mulling—happens in workers’ heads, unseen.
Unfortunately, workplace AI is currently deployed into the knowledge systems that are visible, the outputs —emails, documents, chats, meetings, etc. It doesn’t matter how good the AI is, because when it operates at this level, it’s too late to really transform how the work is done.
To give a practical example, when you need to create a project deliverable, 80% of your effort is likely spent creating the first draft, with the remaining 20% polishing into a final deliverable. AI workplace assistants do a great job with that final polish (which we like, thank you). But to truly transform how work is done, you need AI to help with the underlying, unseen 80% effort used to create the first draft.
The real opportunity: A practical model for AI-driven work
The good news is AI is fully capable to help transform that 80%. This does not require waiting for “better” models or AGI. All you need to do is change how you’re using AI today, by integrating existing LLM-based tools into that invisible thinking portion of your work, rather than just keeping it at the surface-level work outputs.
While the AI vendors haven’t exactly made this intuitive (yet), using AI in this way has exploded in popularity since the beginning of 2026. In practice, the basic approach is to use AI the way software developers do, not as a one-off tool but as something that builds context over time.
Move beyond web-based interfaces where every conversation restarts from scratch
Create a centralized repository, put your critical files into a folder on the business computer which you give AI access to. Start with the classic things (deliverables, meeting notes, project plans, etc.)
Before doing any work, ask AI to interview you about your work style, what’s important to you and your personal preferences.
Review and refine AI’s understanding, ask it to scan through all your files to synthesize your latest thinking, ideas, story arcs, writing style and any other “intelligence” it can determine from your work. Review its findings and go back-and-forth until you feel it has a good understanding of you, your work and your style.
Build upon each session. A crucial step is having the AI tool understand this is not a one-time or manual exercise. Instead, a continued process to create, maintain, organize and update the files, based on what it learns about you over time, each subsequent AI session builds on all the work you’ve done together and what it has learned about you.
In essence, you are asking your AI to create a personal Wikipedia-style repository which gives your AI system an ever-growing continuous context library perfectly built and tuned just for you and your work.
Using AI like this doesn’t require a new product or company, but a new way to leverage current tools. This is often called a “second brain”, “AI context vault”, “LLM-powered personal Wiki”, or something similar, and you can do this with any LLM vendor or product.
Most AI vendors now allow users to connect their LLM platforms into other business systems (like email, chat, document stores or productivity suites), which lets workers connect their personal knowledge systems into corporate apps and data.
Workers who use AI in this new way report fundamental shifts in the way they work within the first few hours. After a few days, many workers declare they will never go back to the “old way” of working again.
The tradeoffs to consider
Using AI to transform work in this way is not without its downsides, especially from the corporate perspective.
First, all the “classic” security complexities still apply: How do you know the AI did what it said it was going to do? How do you know it didn’t hallucinate? How do you trust it won’t spin out of control and email all your contacts with nonsense?
Addressing this involves many of the things you probably know but haven’t taken time to investigate yet, including configuring alternate accounts with restricted permissions for AI or setting clear guidelines for when and how AI-generated outputs will be reviewed.
This new process also requires asking workers to slow down and verify what their AI generates, which is pretty much the opposite of why they started using AI in the first place.
Another challenge is visibility. Much of the “back-and-forth” work – which previously happened in the open – now happens within the AI tool and the worker’s personal context vault, where it’s less visible to coworkers and management scrutiny. Individual workers view that as a positive, but to organizations, it can be a liability.
Lastly, when workers build personal AI context vaults using their personal AI subscriptions, the company can’t prevent the worker from taking all that context with them when they leave the company. Companies need to buy proper enterprise AI subscriptions which they can link to corporate SSO and DLP systems. The downside is that enterprise AI pricing is completely different from consumer pricing, and workers using AI like this via enterprise systems can easily consume thousands of dollars of tokens per month.
The bottom line is that today’s AI can fundamentally transform work, but only if there is a mindset reset around how it is being used.
This new approach introduces added complexity. Organizations will need to spend more time understanding, managing, and securing AI differently, but it’s clear that AI operating in this way is inevitable, so the time to start thinking about AI as a “second brain” is now.
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