The Context Problem: Why AI Still Doesn't "Get" Your Business

Welcome to Jam-packed Business Made Easy with AI. My name is Robyn Anderson, and I'm the CEO of Jam AI. At Jam AI, we have a community where we are training thousands of small business owners every week on how to use applicable and everyday AI. In this podcast, we're focusing on the human side of AI. We're interviewing members of our community and understanding how they're using AI to save more time and make more money. We also practice what we preach. Every Monday, we're generating a podcast from our AI agent. You're going to love it. We have way too much content and not enough time to record, so it's helping us get this information to you. Well, buckle up, take some notes, and listen to what we're about to tell you. And if you want to know more, you can join us free for 7 days in Club Jam by going to jamout.ai for more information.

Imagine spending just millions of dollars rolling out the most advanced, you know, hyped-up artificial intelligence platform on the planet to your entire team, right?

A massive undertaking.

Yeah, Exactly. You're fully expecting it to, like, revolutionize your workflows, cut your costs in half, and essentially just do the heavy lifting of your daily operations.

That's always the promise, isn't it?

Always. But then a few months later, you look at the data and you realize it has a 95% failure rate.

I mean, it sounds like an absolute worst-case scenario for a tech executive, but honestly, that is the statistical reality right now.

Yeah. And that specific number, that 95% failure rate for moving AI from a pilot program to, you know, actual operational impact, it comes straight from a massive July 2025 report by MIT.

The MIT NANDA report. Yeah, it's eye-opening.

Totally eye-opening. And it completely upends the narrative we've been fed. Like, you get your hands on this incredibly powerful AI tool, you plug in a prompt that you think is just brilliant, and the answer you get back sounds like, I don't know, a robot reading a generic Wikipedia page.

Exactly. Yeah. It has absolutely nothing to do with your actual reality.

Right. So we are taking a deep dive today into what that actually means, because our mission today is to uncover cover why simply, you know, plugging in AI is failing so many businesses.

And how a concept called context is quietly becoming the ultimate hidden competitive advantage.

Yes, context. That's the key word. And we've synthesized a massive stack of research for you today to get to the bottom of this. We're pulling insights from some really deep in the weeds technical reports from the New Stack and AskMe360.

Both from January 2026, I should add.

Yep, January 2026. And we're combining those with big picture strategic analysis from Harvard Business Business Review from February 2026, plus some incredibly practical frameworks from places like the US Chamber of Commerce and IBM. Oh, and some insights from Open Data Science, or ODSC, as well.

It's a lot of ground to cover, but viewing all of those distinct sources through a single lens, it reveals a very clear, urgent thesis.

Which is?

Well, the fix for bad AI outputs isn't buying more AI, and it certainly isn't waiting for the next generation of smarter models to save the day. The fix is providing better context to the models you already have.

Okay, let's unpack unpack that 95% failure rate a bit more because if the technology itself is functional, I mean, if the AI can pass the bar exam and write complex code, which it can very efficiently, right? So why does it fail so spectacularly when we drop it into like a mid-sized logistics company or a marketing agency?

It really comes down to the fundamental architecture of large language models. We face what the industry calls the blank slate problem.

The blank slate problem.

Yeah. Because of how these models are built, they are inherently stateless. When you open a session in your enterprise AI system, it starts from absolute zero.

So it literally knows nothing about you.

Exactly. Every single time you type a new query, it is a blank slate. It possesses no memory of the clients you served yesterday. It is completely blind to your internal approval processes. And, you know, it has no concept of your brand-specific tone of voice.

Okay, let me try to visualize this. It's essentially like hiring a genius-level executive assistant who unfortunately suffers from total profound short-term memory loss every single morning.

That's— yeah, that's a very accurate way to look at it.

Like this person has a flawless resume. They speak 20 languages. They can instantly synthesize data. But every morning at 9:00 AM, you have to sit them down and re-explain what your company actually makes.

Right. And who your target demographic is.

Yeah. And what happened in the meeting last week and why we don't use certain buzzwords. I mean, you would spend half your day just catching them up to speed.

That analogy hits the nail on the head. Now scale that amnesiac assistant across an enterprise with 10,000 employees.

Oh, wow. That sounds incredibly expensive.

It is. The financial and temporal hemorrhage is staggering. There is a fascinating data point from that January 2026 AskMen 360 report we mentioned earlier that quantifies this perfectly.

What'd they find?

They tracked user behavior across large organizations and found that these enterprises are hemorrhaging over 35,000 productive hours annually.

35,000 hours. Wait, how does that even happen? What are people actually doing at their keyboards to burn that much time?

They are desperately trying to cure the amnesia.

Oh, wow.

Yeah. Those hours are lost because employees are manually trying to reconnect reports. They're explaining nuance to dashboards, and they're just rewriting prompts over and over again.

Right. Because the AI doesn't remember what they did yesterday.

Exactly. Let's say a regional manager A sales manager asks the AI for a Q3 sales forecast. The AI gives a generic, totally useless template because it doesn't know that Q3 is historically weak due to a specific, you know, seasonal supply chain issue.

So the AI just assumes a normal quarter.

Yes. So the manager then spends 45 minutes arguing with the chatbot, tweaking the prompt, trying to coax the system into understanding the nuances of their specific department. The overarching business intent gets totally lost in the mechanics of prompt engineering.

The employee isn't the problem here. They aren't inherently bad at utilizing new technology.

Not at all. The AskMen 360 report makes that abundantly clear. It is fundamentally a context problem. Every time a user asks a new question without a massive overarching framework attached to it, the system essentially resets.

And decision momentum just grinds to a halt.

Exactly.

Hey, hold on. My brain immediately goes to the most logical corporate solution for this.

Which is?

Well, we spend millions of dollars and countless hours writing and updating our standard operating procedures, right? Our SOPs.

Oh yes, the SOPs.

We have documentation for everything. So if the AI doesn't know the rules of the company and the human is tired of explaining them every morning, why can't we just hand the AI the massive 200-page company rulebook? Shouldn't our stated company protocols serve as that missing context?

Ah, you've just walked right into the trap that thousands of companies are currently stuck in. It's called the documentation trap.

The documentation trap.

Executives assume that their written rulebook accurately reflects the reality of their operations, but a January 2026 analysis from the New Stack completely dismantles this idea.

Really? How so?

They explain that documentation fundamentally fails to solve the context problem because documentation is, by its very nature, always incomplete.

Incomplete because it's outdated the second we print it. Yeah, yeah, yeah. Or incomplete because, you know, people just ignore the rules.

It's a mix of both, honestly, but the core issue is much deeper. An SOP manual only records what someone explicitly thought to write down at a very specific point in time.

Right. It's idealized.

Exactly. It entirely misses the dozens, sometimes hundreds of hidden unwritten micro-decisions that actually govern how a system runs on a random Tuesday afternoon.

Micro-decisions. I like that phrase.

Yeah. And you have to remember how an AI works mechanically. A large language model predicts the next most most likely word or action based strictly on the data it has ingested.

Okay.

If a micro-decision or an unwritten exception isn't captured in that data, the model literally cannot predict it. It is invisible to the machine.

That is a massive blind spot. Yeah. Give me a concrete example of what one of these unwritten micro-decisions looks like in practice. Yeah. Like, how does it break the AI?

Sure. Let's consider a procurement department. The written SOP, the document you fed into the AI, states very clearly, All vendor invoices exceeding $10,000 must be routed to the Director of Finance for explicit approval before payment.

Makes sense. Sounds like a standard solid rule.

On paper, yes. So the AI processes a $12,000 invoice from your oldest, most reliable paper supplier and routes it to the Director of Finance, putting a hold on the payment.

Let me guess. Suddenly your supply chain freezes up.

Exactly. Why? Because the actual execution in that office is different. The finance director is overworked, so there's an unspoken verbal agreement with the floor manager that invoices from vendors you've worked with for over 5 years just get pushed through automatically.

Yeah, just to avoid bottlenecks.

Right. The director only actually wants to see invoices from new vendors.

And the AI has absolutely no way of knowing that unspoken agreement exists. It only knows the rigid written rule from the manual.

The AI operates on the fantasy of how the business works, not the reality. The gap between what experienced employees carry around in their heads you know, their situational awareness, their relationships, their knowledge of workarounds and what the AI can parse from a static document is the true bottleneck in deployment today. We really have to separate stated process from actual execution.

Stated process versus actual execution. That makes so much sense.

Right.

So stated process is like the idealized flowchart we show the board of directors.

Yes, precisely.

And actual execution is the messy human reality of how the work actually crosses the finish line.

Exactly. And tying this back to the broader strategic picture, this is exactly the dynamic Harvard Business Review zeroes in on in their February 2026 analysis.

Oh, right. The HBR piece.

Yeah. We have reached a point where every company from a lean startup in a garage to a Fortune 500 giant has access to the exact same foundational AI models.

The playing field for raw intelligence is completely leveled because, I mean, no one is building a multibillion-dollar language model in their basement. We are all essentially renting the exact same supercomputer brains from the same 3 or 4 tech giants.

Exactly. Therefore, HBR points out that since the baseline models are totally commoditized, your unique organizational context has become the ultimate differentiator.

Wow. So context isn't just a nice-to-have. It might be your only differentiator.

Exactly. But true context isn't your pristine SOP document. True context is demonstrated execution.

Demonstrated execution, meaning the actual signals that teams respond to in the real world.

Yes. The order in which roles actually get involved in a project, the real-world exceptions that trigger a different workflow, the repeating human judgment calls.

The stuff that isn't written down.

Right. Those patterns are only visible when you look at how work is executed, not how it is documented.

Okay, so what does this all mean for you listening to this right now? We've established the problem. You know, the AI is an amnesiac.

A very expensive amnesiac.

Yeah. And we've established that just handing it the company rulebook doesn't cure the amnesia because the rulebook is full of half-truths and entirely ignores our unwritten micro decisions.

Right.

So, how do we practically bridge this gap? Like, how do we actually inject our messy real-world context into the machine?

Well, we need to look at two distinct approaches. There's the enterprise-level technical solution, and then there's the everyday practical fix.

Let's tackle the technical side first because I know that is rapidly becoming the gold standard. IBM recently published an excellent explainer on a framework called RAG.

Yes. I stands for Retrieval Augmented Generation.

Retrieval Augmented Generation.

Yeah.

Okay. I'm going to need you to translate that out of engineer speak.

Fair enough.

From my understanding, RG is basically a framework that lets your AI model access your internal proprietary data to fill in its knowledge gaps.

Right.

But why use this complex RJ thing? If the model doesn't know our data, why don't we just retrain the entire model on all of our internal servers?

Two reasons: cost and latency. Retraining a massive AI model from scratch using your company's specific data requires astronomical computing power and highly specialized talent.

I'm guessing that takes a while.

It can take months. And the fatal flaw is that by the time the retraining process is finally finished, your data is already out of date.

Oh, right, because the business kept moving while the AI was training.

Exactly. New micro decisions have been made and the AI is obsolete on day one. RAAG bypasses the need for retraining entirely.

Let me try to visualize the mechanics of how this actually works. A lot of people call RAAG an open-book test for the AI, but that feels a bit too passive to me.

Passive? How so?

It seems more like having a hyper-efficient invisible librarian standing right over the AI's shoulder.

A hyper-efficient librarian. I like that. Expand on that.

So the AI is still the amnesiac assistant. It doesn't have your internal emails, your Slack messages, or your actual execution data memorized. Right. But right before that assistant tries to answer your prompt, this invisible librarian sprints into your company's digital archives.

Searching for the exact context.

Exactly. The librarian searches for the specific keywords or concepts in your prompt, grabs the 3 or 4 most relevant pages of your messy real-world data, sprints back, and silently pastes those pages onto the desk in front of the assistant.

And then the AI reads those specific pages.

Yes. It augments its baseline knowledge.

Yeah.

And then generates the answer for you. And all of this happens in like milliseconds behind the scenes.

That is a remarkably accurate description of the mechanism. The technical term is vector search, where documents are converted into mathematical representations to find perfect contextual matches.

Vector search. Got it.

But your librarian metaphor captures the causality perfectly. The AI doesn't have to start from zero because it is continuously retrieving your real-time, real-world organizational context a split second before it speaks.

And because it's searching your live systems, like your actual communications, your recent project management tickets, it's picking up on those unwritten micro-decisions in real time.

Precisely. It sees that the floor manager approved the invoice yesterday, even if the SOP manual says otherwise.

That's incredible.

And this represents a massive philosophical shift in how we approach technology. This was heavily emphasized by Open Data Science, or ODSC, in their January 2026 Outlook.

What was their take?

Their core argument is that for modern operators, learning the context, meaning the intricacies of the business, the psychology of the end user, the real-world constraints of a workflow is fundamentally more valuable than learning the mechanical syntax of every new AI tool that drops.

Because the tools are going to change anyway.

Exactly. The tools themselves will change every week. The interfaces will evolve, but your context is what actually generates value.

Okay. RAG makes total sense as the enterprise-level technical pipeline to inject context, but, uh, What about the person who doesn't have a massive IT department at their disposal?

The everyday user.

Yeah. What about the solo entrepreneur, the small agency team leader, or just someone prepping for a massive corporate presentation tomorrow morning? They can't set up a vector database and an automated retrieval system by 9 AM. We need a practical, immediate fix for them.

For that, we turn to a surprisingly excellent guide from the US Chamber of Commerce. It is arguably the most actionable piece of source material in our stack today.

Oh, I loved this part. It's so simple.

It is. Their non-technical fix is remarkably simple, but if implemented consistently, it entirely transforms the output quality of any generative AI tool. The golden rule is this: you must train your AI tool before you run any transactional prompts.

Training before you prompt. But again, how do we train it without writing code or building a RAGoE librarian?

You do it by creating a one-page business brief. It's often referred to as a system prompt or context window primer.

A cheat sheet, basically.

Exactly. It is a single, densely packed document that you copy and paste into the AI before you ask it to do anything else. And this document must explicitly outline the 3 levels of context.

The 3 levels of context. Walk us through how to build this cheat sheet. What is level 1?

Level 1 is who you are. And to be clear, this is not just pasting in your company's mission statement. You need to define your brand's specific tone of voice, your core differentiators in the market, and your internal culture.

So like setting the personality.

Yes. You need to tell the machine, are you highly academic and formal, relying on peer-reviewed data, or are you witty, conversational, and disruptive?

Right. Because if you skip level 1, the AI will just default to the most generic sanitized corporate speak imaginable.

Exactly. It'll sound like a robot.

Got it. So level 1 cures the personality amnesia. What is level 2?

Level 2 is How you work. This gets directly into your workflow, your constraints, and your actual execution.

Ah, this is where we document those micro-decisions.

You hit the nail on the head. You need to tell the AI what the primary metrics for success are in your specific department. What are the hard financial or temporal constraints you operate under?

Can you give an example of what that looks like in the prompt?

Sure. It's as simple as telling the AI, we operate on a lean budget and prioritize speed over perfection. We never use external consultants for initial drafts.

Oh, I see. So level 2 essentially gives the AI the unwritten rules of the road. It stops the AI from like suggesting a million-dollar marketing campaign when you only have a budget of $5,000.

Precisely. It grounds the AI in your reality.

And level 3?

Level 3 is who you serve. This defines your client, your target audience, or even your physical geographic location if you run a brick-and-mortar business.

So the AI knows who it's actually talking to.

Exactly. You have to tell the AI, who the end consumer of its output will be. Are you writing an email for a deeply technical audience of senior software engineers, or are you creating a brochure for first-time homebuyers who have no idea how a mortgage works?

Huge difference there.

Massive. The AI uses Level 3 to calibrate the complexity and focus of its generation.

Bringing this back to the daily workflow, this one-page business brief is really the ultimate manual override.

It is.

You open your tool of choice, and before you say, draft a project proposal for the new client, you paste in your 3 levels of context brief. You say, read this brief. This is who we are, how we work, and who we serve.

And you ask it to confirm, right?

You say, acknowledge that you understand this context, and only then will I give you your first task.

You cure the amnesia immediately, proactively. And the data from the US Chamber of Commerce shows a direct correlation here.

What's the correlation?

The more specific and honest you are in that initial grounding document about your actual execution, the drastically more relevant the output becomes. You stop hemorrhaging those 35,000 hours of productive time.

Because you aren't fighting the AI to understand your business intent anymore.

Exactly. You've handed it your intent on a silver platter before the work even began.

So if we pull all of these threads together, the core takeaway of our deep dive today is incredibly clear. The solution to bad generic robotic AI outputs isn't waiting for a smarter machine.

No, it's not.

The solution is better context. Whether you are building complex retrieval augmented generation systems to search your internal servers, or you are simply copying and pasting a 1-page, 3-levels-of-context brief into a chat window, the fundamental goal remains exactly the same.

You have to show the machine your demonstrated execution.

Right. You have to bridge the gap between static, idealized rules and the messy human reality of how your work actually gets done.

It forces a realization that organizational context isn't just, you know, background information. It is your competitive moat in a world where everyone has access to the exact same supercomputers.

Absolutely. Now, before we wrap up, I want to leave you with a final provocative thought to mull over based on everything we've explored today.

Always the best part.

We've established that true context is found in our unwritten micro-decisions, right? And that tools like ARAG are incredibly powerful because they act like that hyper-efficient librarian, constantly sweeping our internal communications and real-time data to find the truth of how we operate.

A constant real-time mirror of the organization's execution.

But consider the flip side of that mirror.

Okay.

If the ultimate goal is to perfectly map our unwritten micro-decisions and human judgment calls into an AI's context, Well, do we run the risk of permanently hardcoding our current human biases and bad habits into our organization's future?

Oh, wow. That's a fascinating point.

Like, if the AI learns from all our messy, flawed past decisions, are we essentially making it impossible to evolve beyond our current bad habits?

You might just automate our own flaws.

Exactly. It's definitely something to carefully consider the next time you decide to grant a machine total access to your actual execution. Keep questioning the outputs, keep refining your context, and we'll catch you next time on the Deep Dive.