Most businesses are chasing AI agents, but they're not realizing that the most valuable thing is something that they already own. Let that sink in for a second. I speak with executives every single week saying, I need a new agent for this department or I want the latest agent to do this, but they're not realizing that the most important piece of their technology stack is already at their disposal. Almost none of them are asking the right question. Can my business support the agents that need to run on top of them? And a startling fact is that ninety five percent of AI projects fail to deliver results or meet expectations of executives, and that's a study done by MIT. And the number one reason for that is not the agents are are smart enough, but actually that the data and the process and the technology behind it is not strong enough to support those agents. So in this video, I'm gonna walk you through how the technology stack has evolved into four new layers. First, the systems of record, then the context layer, then the agentic layer, and the most important layer I'll save for the end. If you're making technology decisions for your company in twenty twenty six, you're gonna wanna watch till the last layer. So let's get into it. For the last thirty to forty years, the technology stack has been pretty simple. You have systems of record like ERP and CRM systems, so enterprise resource planning systems and customer relationship management systems and productivity tools that supported those. And then you had humans that interacted in these systems and performed processes in specific departments and was organized relatively easily. That model is now completely broken. It's not like those systems went away. In fact, they didn't it at all. Instead, now those systems and people matter more than ever before. What has changed is now there's a new layer of intelligence that's been introduced into the mix, And this is rewriting how everything gets done within the business. Here's the shift in kind of plain terms that we're seeing every day. Before, people would log in to Outlook, read their emails, determine what they needed to then go do within their system of record or their ERP. They pull up the system, go through and actually perform that action, go back over in their email, and send out the details on what they just did within the system. That entire process has been redefined. Now the software has evolved to meet the work where it needs to actually get done. So instead of your team going and reviewing their emails manually determining how they should go operate within the system of record, the intelligence sits inside of the email inbox and determines then what actions it needs to take within the systems of record. And then you have humans which approve those actions as opposed to being the ones who are performing the task. This changes the way that system architecture needs to work completely. Microsoft alone has seen a hundred and sixty percent growth year over year in their use of AI across their products, and ninety percent of the Fortune five hundred are leveraging Microsoft AI today. This is not something that you can wait another year or two to start figuring out for your business, or you're already gonna be behind. The ones that understand the new technology stack in twenty twenty six are gonna be operating at a whole other level as compared to those who still operate with the mindset of twenty fifteen. So before you make another technology decision, I want you to stop and actually map out your business into these different layers that we're going through so you can understand how to make the best decision possible for your technology architecture in twenty twenty six. So layer one, the system of record. This is the base of everything. The foundation for all of the intelligence and all of the agents that are gonna be operating on your behalf. This stuff has to be right, and you have to have high integrity of the data that's available within these systems of record. Your source of truth has to actually be truth. So this is where all your transactions live. It's inside the ERP, the CRM, the product life cycle management system, the the HRM, all of these core systems that have logic and business policies put in place to support your organization today. And here's what most people are getting wrong at this moment in time. Because people are thinking that AI is gonna replace this layer, but rather you have to have a high integrity system that your agents can rely on so you know that what they're gonna be performing on your behalf is gonna be accurate, have integrity of the data, and adhere to the policies that you've set in place with your business. If you're asking your agents to operate on something on your behalf, you wanna ensure that it has trustworthy data that it can stand on to make good decisions for you. Agents are really good at turning bad data into way more bad data. So you want good data in to support so you can have good data out. If your ERP is fifteen or twenty years old and you know your data is stale, you think that throwing an agent on top of this is gonna improve the issue? I say this all the time, but agents are only as good as the data that they're built on top of. A lot of businesses that I talk to are trying to make the decision on if they should upgrade from their legacy systems into a more modern solution. They know they need to modernize, and they've been putting it off for years on actually getting a strong foundation in place. Now that window is closing very fast where the new layers are evolving even quicker than ever before, and your competition is getting ahead by taking advantage of these new layers because they're able to on a modern stack. Reports show that eighty percent of data in twenty twenty seven is gonna be unstructured data. And you wanna make sure that that twenty percent that is structured data is high integrity, and you can use it to build agents off of. These systems of record are the most important pieces of your infrastructure. They're like the heart to your organization, and you have to get it right so everything else can fall in line. So take an honest look at your systems of record. Are they accessible by AI? Are they, you know, locked away in an on premise solution that nothing can get access to and the data is unable to be exported out or even called in by an API or an MCP? Is your CRM half adopted and none of the processes are being followed? Or is your data in your ERP old, stale, messy, and nobody trusts it? If so, you need to focus on fixing the foundation first. Introducing agents into the mix is not gonna solve these problems for you. You have to have a strong heart in order to focus on the rest of the body. So now moving up the stack to the next layer that surrounds the systems of record is the context layer. And this layer has been around for a long time with Teams messages, emails, phone calls, transcripts, really all of the data that's getting generated from business actually operating. This is really where the knowledge of your business has lived forever. It's never just been captured in a way that's accessible for something to reason over until now with agents. This unstructured data that flows like the blood throughout your organization is absolutely critical to harness to have intelligent agents operate on your behalf. This stuff has never been structured or captured in a real meaningful way until now. Now tools like Microsoft Copilot, Claude, and ChatGPT can reach down into your context to understand the business operations, reason over it, and then make decisions and perform actions on your behalf. So every Teams message, email, shared transcript, phone call, all of that is getting stored within a central context layer that the agent can understand and perform actions on. This is something that standalone ChatGPT or Claude is not gonna be able to do without structured context or to reach down and understand your business. It's important to get this layer right, and you have to be mindful of how you're managing this context. You can't give it decades of data that is not relevant anymore and assume that it's gonna make right decisions. You have to be a context gardener and constantly be pruning what's relevant and what needs to be accessed by the agent. Context engineering is more important than prompt engineering. This is all the connective tissue that exists really between the systems and the people and the world at large that really showcase what is is working within the organization. What you should think about is if I was onboarding a new hire today to learn your business, how would you go through and educate them on what's going on? You would teach them the org chart. You'd help them understand who they need to go to and why they need to go to them. You'd help them understand the processes that they need to perform. You'd give them standard operating procedures of exactly how to perform those given tasks and then access to the tools and the systems to be able to perform those things. That context layer now needs to get actually added in and managed in the same way that you're managing your systems of record. So with that same care and diligence that you have of managing your systems of record, you also have to have around your context. So action for you. You should audit your context layer. Is all the communication that you're have that you're that's happening within your teams happening in teams? Are you having meetings recorded? You know, is these meetings being on listed on SharePoint? Do you have those transcripts available? Do you have your wiki stored within, like, a OneNote or a Notion? Are those tools that you're using to capture all the context within your organization accessible to AI? You know, this doesn't mean, like, Sally writing down her notes on a piece of paper that that is gonna be able to be ingested by AI and reasoned over. You have to make sure that context is being captured in a way that's gonna be accessible for AI and able for these agents to reason over if you want them to make good decisions and perform good actions on your behalf. So remember, the cleaner that your context layer is, the better performing your agents are gonna be. Layer three is the agentic layer. This is where the fun stuff happens. Right? This is where all the hype is right now. This is where your AI agents live, Whether that's built in MyGPT and ChatGPT or Claude agents or Copilot Studio agents or Azure Foundry agents, wherever you have your agents that are models that are contextualized and have tools that they can leverage, wherever they may exist. This is an important layer to have right. These are not chatbots, but actually things that are performing actions on your behalf based on triggers and autonomous flows that have. Think of this as like the new operator layer. For the last thirty years, if you wanted to get something done within a system, you had to bring in a human to be able to actually go through the process. And if you wanted to scale that, you had to add more humans. Now in order to do that, you have to add the context and then be able to add the agent that sits on top of it and then scale the agent out. So when an email comes in, if you get a hundred more emails, then that doesn't mean you have to have another human do that. You can have an agent that triages the email inbox and then go and perform those tasks. It'll pull the relevant context from your structured context layer, perform that task, and then bring it back for approval if necessary. And this is already happening today across enterprises at scale. And I really wanna emphasize scale. I'm not talking about little flashy agents that perform one specific operation, you know, asking ChatGPT to build you a report or something. I'm talking about deploying this across your entire enterprise. And what you'll realize is that it's easy to get single wins, but it's very hard to roll this out across an entire organization. Because the foundation is built on a shaky bedrock and there's lots of cracks built into it, You're gonna be able to find ten or fifteen percent agentic process transformation capability. But if you have a really rock solid foundation and a structured context layer, a hundred percent of your organization is gonna be able to be implemented by agents. And this is already happening today in production at scale. And I really wanna emphasize the scale. This is something that is not just a flashy demo that you're getting a single agent to respond to an email for. Yes. You could do that across a number of different areas, and what you're gonna find is that it works well for maybe ten to fifteen percent of your business. But when you get into the more complex scenarios, you're gonna see that these agents break down because they don't have the context to perform the process, and they don't have a system of record that they can access. If you really want to expand these agents and their purview and what they can do, you have to make sure that you get that system of record layer and that context layer right. And then a vast majority of your business is gonna open up for agentic transformation. And Microsoft and the big tech giants are investing all in on this transformation. Microsoft has committed thirty billion dollars in compute infrastructure to support this agentic transformation. Anthropix Cloud models are now integrated across all of Microsoft Copilot as well as OpenAI models, so you can mix and match, pick whatever the best model is for you, and deploy it into your business. What you'll find is that these models are gonna become infinitely intelligent. But in order for them to be deployed for your business, you have to have a rock solid foundation and a highly structured context layer for them to reason on top of. And the trap is very enticing. It's very easy to implement agents when you have all these layers figured out. You can build these agents in fifteen or thirty minutes that can perform tasks that will amaze you. But what you'll find is that if you build this on top of a rocky foundation, those agents are gonna be performing bad actions on your behalf. So the easy part is the agents, and that's the trap. The hardest part is getting that foundation in the systems of record and the context layer right. We see it over and over again, People trying to deploy agents where they don't have that solid foundation. So before you move forward and decide on deploying an AI agent, audit your foundation and your context layer. Do you have bad data that exists across your different departments that's unstructured and that's unorganized? Get that stuff right before you try to set an agent on top. Now the fourth layer is the most important layer. This is the human layer. This is really the orchestration layer of all the agents and all the systems underneath it. Understanding how to leverage these new tools is gonna be the most important skill set of the future. And the people that are able to organize these agents into skills and into sequences or or commands like in quad, these people are gonna be the winners of the future. And this layer is where all of the people sit now in the future, not as users of systems of record, but orchestrators of the entire stack, all of the agents, the context, and the systems of record. The role of a business leader is absolutely changing. It's not who can click through fastest through an ERP or a CRM tool, but rather it's who can leverage agents in the most capacity to be able to do what they need to be done. This is all about directing agents, setting the outcomes and business objectives that you want, and then aligning the agents to meet that goal. Then, of course, reviewing what the agent did and course correcting and training it to get the desired response the next time. Making these judgment calls is something that machines will never do. We're never gonna be able to replace human judgment and domain expertise around your business. And what we're seeing today in the market is that the best people are not being replaced. They're actually being amplified. A controller who used to spend half their time just going through and reconciling financials now can actually spend the time forecasting out cash flow and helping their business make better decisions financially. A sales manager who spends half their day in CRM now can have conversations with customers and drive more business. But this only happens if all the three layers underneath are working properly. Here's the map that nobody is really seeing and or talking about today is that McKinsey's estimating four point four trillion dollars is gonna get added to the economy through the use of generative AI agents. And this value is not gonna be capitalized on by people that just run the model companies. Rather, it's gonna be the companies that use the models in the right way to capture their industries. And this orchestration layer on the top is the moat that you can take advantage of now. If your people don't know how to use an agent, govern an agent, or manage the outputs of an agent, then you don't have AI advantage. This is actually just an AI expense. So start investing in your people as orchestrators of these systems. Treat this as a skill across your entire organization. Run AI literacy programs. Identify those on your team who you think are gonna be power users and champions of these tools and enable them for success. This human layer here is where all of the return on investment comes into play. So here's the bottom line. The technology stack has now evolved into these four layers, the system of record, the context layer, the agentic layer, and the human orchestration layer. Every single layer matters starting from the foundation moving outwards, and every single layer depends on the one beneath it. And the companies that see this clearly now and act on it are gonna be the ones that pull so far away from the ones that don't see it. So three main takeaways. Your systems of record are not going away. Rather, they become the most important piece of foundation of your entire business. So invest in them as such. Two, your context layer is real data, and you should manage it that way. Treat it like data, govern it like data, and prune it and be a context gardener. And third, the agentic layer is very exciting, but the most important layer is the human orchestration layer. Invest in your people's ability to manage and orchestrate agents, and I promise that's gonna be where you see the return. If you're making technology decisions this year and only one of these four layers is on your road map, then you need to take a wider lens. Look at all four layers and make sure that you're making the right investments. Thanks for watching the video. If you like this, then follow and subscribe for more actionable advice that you can take and adopt into your business. And if you wanna watch another video, got one linked here, and then also more information down in the description below. Thanks for watching.
This video explains why most AI projects fail and introduces a new four-layer technology stack that businesses need to understand for 2026. The speaker argues that while companies are focused on chasing AI agents, they’re missing the foundation required to support them. The four layers are: systems of record (ERP, CRM systems that serve as the foundation), the context layer (unstructured data like emails and messages that provide business knowledge), the agentic layer (where AI agents operate), and the human orchestration layer (where people direct and manage agents).
The video emphasizes that 95% of AI projects fail because companies don’t have strong enough data, processes, and technology foundations. Success requires investing in all four layers, starting with a solid foundation of high-integrity systems and well-managed context, before deploying agents that can be effectively orchestrated by humans. The speaker provides actionable advice for auditing each layer and warns that companies who understand this new stack will significantly outperform those still operating with outdated mindsets.
