AI’s inability to grasp business context
We should be clear about what today’s AI can and can’t do. It’s good at recognizing patterns and converting language into action. It can answer generic questions and automate repetitive tasks across industries. But when you introduce it into the unique, detail-driven environment of a real business, with its mix of outdated systems, team-specific definitions, and evolving language, it breaks down.
AI models like GPT have been trained on public data. That data doesn’t include how your finance team calculates revenue differently from sales, or how discounts vary by product region after an acquisition, or why the term “active customer” has five competing definitions internally. These are not things you’ll find on the open web. They live in Jira tickets, SharePoint folders, Slack threads, old PowerPoint decks, and the conversations your team has in weekly meetings.
This is the main reason companies get a surprise when they try to run AI behind the firewall. You throw it against your data warehouse or internal systems, and the model starts guessing. That guesswork increases with complexity: multi-step queries, joins across custom schemas, edge-case transformations. According to Spider 2.0 benchmarks, large language models hit only 59% accuracy on basic SQL queries and drop to 40% when asked to do more real-world transformations. That’s a failure rate most CFOs won’t tolerate in production environments.
If you’re running a company, the takeaway here is simple: AI can’t add value where it doesn’t understand context. And business context is not purely about big data, it’s about small, specific, messy information that makes your enterprise different from every other organization.
Tom Tunguz pointed this out when analyzing Spider 2.0 performance. The benchmark isn’t just a test of query capability, it’s a stand-in for how models manage complexity in actual businesses. And right now, they’re not there.
Architectural limitations over model size
We’ve seen this mistake before, people assume that smarter AI just means a bigger model. Add more parameters, more compute, and suddenly it becomes capable of understanding your business perfectly. That assumption doesn’t hold up.
The limits aren’t in the size of the model but in its architecture. Today’s large language models operate statelessly. That means they don’t remember what you asked them before, what they said last week, or how your metrics were calculated last quarter. Every interaction starts fresh. For businesses, that’s not useful.
If you want an AI that can work inside your company, it needs memory. It needs to hold context about your internal definitions, systems, historical decisions, and even mistakes. That memory should live across three layers: working memory for what just happened, long-term memory for policies and knowledge, and episodic memory for capturing patterns over time. Otherwise, every interaction is just high-effort repetition, and you never get compounding returns.
Governance matters too. AI isn’t going to intuit what your company means by net revenue or which customers are exempt from a policy. You have to tell it, and structure that information so it can retrieve it every time. This is where system design beats scale. You don’t need a larger model; you need a smarter loop, something grounded in your definitions, tested against your policies, and tied to ongoing feedback from experts in your business.
Instead of asking, “Can this model handle 175 billion parameters?” ask, “Does it respect our business processes?” If the system can’t reliably produce the right revenue number or understand how your sales regions are divided, it’s not ready.
To close the trust gap between humans and AI, we don’t need philosophical breakthroughs. We need engineering that helps the model understand what matters inside your company, and remember it. That’s scalable. That’s solvable. And frankly, it’s a smarter direction for enterprise AI.
Enhancing AI with Retrieval-Augmented Generation (RAG)
Here’s the real challenge: most AI models can’t access your internal knowledge. They weren’t trained on it, and out of the box, they can’t reach it. So even if your AI platform is technically impressive, it will still produce vague or inaccurate outputs unless it’s fed the right internal data at the right time.
This is where Retrieval-Augmented Generation, RAG, comes into play. It’s not a new model. It’s a new method. With RAG, the system retrieves domain-specific data, such as data definitions, schema files, visual diagrams, DBT models, row samples, and lineage metadata, then integrates it into the AI’s response process. It’s not guessing anymore. It’s answering based on controlled input that reflects your company’s actual environment.
This dramatically reduces the chance that the AI invents a join, selects the wrong table, or applies an outdated rule. It also lowers the volume of false positives you get with generic AI. And importantly, it transforms the model from a general-purpose tool into something that starts to resemble a trustworthy function inside your business operations.
But the source of the data matters. You don’t want the AI pulling information from a random collection of PDFs, outdated slide decks, or vector-bundled text with no provenance. Retrieval should be based on governed, curated sources, data catalogs, semantic models, lineage graphs, metric stores. Every answer from the model should have a traceable origin.
These structural inputs give the model awareness of your schemas and definitions, things public data can’t provide and traditional training can’t solve for. This doesn’t just improve accuracy, it raises confidence across your teams. And when operations, finance, and product leaders can trust these answers, they’ll actually use the system, which is the real signal of adoption.
The need for layered memory in AI systems
Without memory, the AI forgets what you just told it. And that’s a problem. In enterprise settings, context is not optional. It’s a requirement.
Your business operates through processes that span days, quarters, and years. Your people remember why decisions were made. Your systems reflect that history. That knowledge sits across tools, documents, databases, and people. AI tools, if they lack memory, start from zero every time, which destroys continuity, creates rework, and increases error rates.
What’s needed is layered memory, working memory for immediate tasks, long-term memory for persistent rules and definitions, and episodic memory to track patterns, exceptions, and shifts over time. You can’t rely on the model alone to do this. It needs access to structured memory built from your company’s systems of record. The database becomes the anchor of this memory. And not just any database, but one that can store contextual metadata, embeddings, event logs, and allow targeted retrieval during request time.
Layered memory enables your AI to evolve. It remembers what’s been corrected. It records what inputs led to effective outputs. It learns what your teams do repeatedly and adjusts its assumptions. Without this, you get surface-level intelligence that doesn’t improve with use. With it, you get a system that compounds in value each time your people interact with it.
For business leaders, this is the infrastructure shift that matters. Layered memory moves AI from toy to trusted capability. It’s not just about reducing errors. It’s about moving faster because you’re not retraining the system every day with the same inputs and decisions. You’re building institutional knowledge into the machine itself. And that’s when the system starts to really pay off.
Structured interfaces reduce ambiguity
Most enterprise systems depend on precision. If you give your AI model too much freedom, and let it generate answers using open-ended natural language, it will eventually deviate from what’s correct. This isn’t due to lack of intelligence; it’s because natural language leaves room for interpretation. And in business logic, ambiguity causes mistakes.
You can address this by structuring how the AI communicates. Instead of letting it generate unbounded SQL or procedural prose, constrain the output through structured interfaces, like abstract syntax trees or restricted query formats. Limit the model’s available options. Define the logic it’s allowed to call. Force every action through validation layers that snap the output to known dimensions, metrics, and entities from your existing semantic model.
This does two things: it increases accuracy and guarantees compliance with your internal data contracts. AI isn’t guessing metrics anymore, it’s invoking functions like get_metric(‘active_users’, date_range=‘Q2’), where both the metric and the syntax are defined and validated.
These tools also allow you to integrate the AI into production environments with far more stability. The execution layer checks what’s being requested before anything runs. That protects both system performance and data quality.
For business leaders, this means AI-generated actions are auditable and aligned with operational rules. They no longer depend on someone interpreting what the model might have meant. You reduce the back-and-forth. You improve trust. And you create a framework where the system participates in the business without disrupting it.
Human feedback is key to refining AI outputs
No AI system is perfect on its own. It will make errors. Some will be minor, like choosing the wrong column. Others will be more serious, such as applying the wrong logic to customer segmentation. What matters is whether the system learns from those mistakes. That learning depends on human-in-the-loop feedback.
Your people shouldn’t spend their time manually correcting syntax or rewriting entire outputs. Instead, focus their effort where it counts, on the ambiguous or high-risk cases. Set up approval flows that highlight potential issues, such as incorrect joins, unexpected filters, or results that break a known pattern. Make it simple to give feedback in structured ways. For example, allow someone to tag that status_code in (3, 5) should be excluded from active customers, or that a generated query is missing a security constraint.
That feedback can and should feed back into the retrieval and memory systems. Over time, the AI starts making fewer of the same mistakes. This shifts the role of your experts from constant correction to performance enhancement. Every approval becomes a training signal the system uses to calibrate how it works.
Executives should think of this as tuning the intelligence layer of their business. You’re not just reviewing AI output, you’re shaping how the system learns. You don’t need to retrain a foundational model on your data. You just need to give it consistent, high-signal corrections that update how it retrieves, processes, and applies your business knowledge.
This makes the system more stable, more accurate, and ultimately more aligned with how your company operates. That’s what turns artificial intelligence into enterprise intelligence.
Domain-Specific KPIs trump generic benchmarks
It’s easy to be impressed by an AI model that passes a benchmark. But benchmarks like Spider 2.0 aren’t how your business measures success. They show what a model can do in a test environment, not in your environment. That difference matters.
What actually counts is whether the AI does the job your team needs done, accurately, consistently, and securely. Can it produce the three core revenue queries your finance team needs to close the quarter? Can it respect data access controls 100% of the time? Can it generate correct sales reports based on the filters and definitions used by your territory managers? These are the questions that drive real operational impact.
To assess that, you need your own internal benchmarks. Run nightly tests, not to see if the model got a score, but to check if it completed the actual tasks that matter. Identify the KPIs that determine success in each department, revenue closure accuracy, compliance with privacy rules, data lineage alignment, and test AI systems against those.
For leadership, this reframes how you evaluate AI systems inside your company. Don’t rely on metrics that matter to researchers. Focus on metrics that drive your business. The closer your evaluations are to real workflows, the more useful they become. The goal isn’t to brag about a benchmark score. The goal is to improve execution on real tasks that affect your company’s financial and operational performance.
Evolving roles, human and AI collaboration
As AI integrates across departments, the way people contribute changes. Developers are no longer just writing production code. They’re starting to act as context engineers. They’re defining semantic layers, encoding policy as logic, and setting the guardrails for how AI interacts with the business.
This isn’t about losing control to automation, it’s about steering it. Developers design retrieval pipelines, build structured memory systems, and determine how model outputs are validated and deployed. The more you embed AI into your workflows, the more valuable these roles become. You’re not just automating process, you’re building systems that capture and apply institutional knowledge.
You still need human review, leadership insight, and domain-specific decision-making. Not because AI is weak, but because business context changes constantly. Legal requirements shift. Definitions evolve. Teams reorg. Strategy pivots. Many of these changes don’t live in your data model, they emerge through conversation and judgment.
Executives need to support roles that bridge the gap between how AI functions and how the business operates. The developers, analysts, architects, and operational owners responsible for that integration are the ones ensuring AI works in the way your company actually runs. As the system takes on more tasks, these people ensure those tasks are performed in a way that reflects the real structure and values of the organization.
This isn’t transitional. It’s foundational. The more useful AI becomes, the more critical the collaborative structure behind it becomes. That collaboration is what makes AI viable, safe, and aligned in enterprise environments. You scale the system not just by growing the model, but by growing the intelligence across the people supporting it.
Business context is dynamic and requires continuous oversight
AI systems don’t operate in a vacuum. In the enterprise, the context changes constantly. New products are launched, organizational structures shift, pricing policies change, and definitions evolve with market or regulatory pressure. These are not one-time adjustments, they’re ongoing dynamics that impact how decisions are made and how data is interpreted.
AI isn’t built to anticipate all of these shifts by default. Most models don’t have the autonomy or organizational awareness to adapt without input. That means human oversight isn’t optional, it’s essential. Someone still needs to interpret what a quarterly revenue adjustment should mean, or how a merger affects categorization of SKUs across systems. Without a human maintaining context, the AI will eventually drift or become out of sync with business operations.
This is where leadership plays a direct role. You don’t need to supervise every output yourself, but you do need to invest in systems and teams that ensure alignment between what the AI does and how the company operates. This includes updating memory systems, refining metadata, and adjusting processes to feed continuous context into the model.
Failing to do this leads to automation based on outdated rules, which damages credibility, slows down adoption, and creates unnecessary risk. The better path is to implement intentional oversight, where technical operators and business owners jointly review how AI outcomes reflect the most recent state of the organization.
If your context changes, your system must reflect it quickly. The longer that gap persists, the more disconnected your AI becomes from reality. And that weakens decision-making quality across departments that use its output.
Architectural reimagining as the path forward for enterprise AI
The core problems we’re seeing with AI in the enterprise aren’t going to be solved by switching to a different model. These challenges come from incomplete systems that were never designed for your data environment, your policies, or your workflows. What needs to change is the architecture.
That means going beyond just fine-tuning or prompt engineering. You need to build systems with formal memory architecture, governed data retrieval pipelines, constraint mechanisms, approval workflows, and performance evaluation engines. Each layer has to reinforce the others. Structured feedback needs to flow into memory. Retrieval needs to align with how your organization defines truth. Output generation needs to respect rules and constraints.
This isn’t theoretical. We’ve already seen that foundational models perform reasonably on generic use cases but fail when rules, governance, and shifting context come into play. Tom Tunguz’s Spider 2.0 analysis showed how performance drops from 59% to 40% when real-world transformation complexity is introduced. That’s not a model issue, that’s a system design limitation.
C-suite leaders need to view this differently. You’re not investing in a point-solution AI tool. You’re building the enterprise intelligence layer: infrastructure that enables your company to retain context, execute reliably, and evolve alongside your market.
The payoff is substantial. Reduced manual rework. Faster alignment between departments. Higher trust in system outputs. And operational scalability without the usual cost curve. But none of that happens unless the architecture is designed to handle contextual complexity from the beginning.
Get this right, and AI shifts from being an assistant that sometimes helps, to something that consistently contributes, improves, and scales as your business grows. That’s not just better technology, it’s a smarter enterprise.
The bottom line
AI won’t solve business problems unless it understands the business. Not just the data, but the policies, processes, exceptions, and evolving definitions that make your company what it is. That requires more than a generic model. It demands systems that remember, retrieve, and adapt to your internal logic.
The opportunity isn’t theoretical. It’s operational. Improving decision speed, reducing manual cleanup, automating the right work, all that’s possible. But only if the architecture behind the AI is built for your organization, not just the internet.
For leadership, this isn’t about chasing the next model. It’s about shaping the environment around the model. That means supporting teams that build feedback loops, manage context layers, and translate business decisions into structured sources AI can use.
The payoff isn’t just efficiency. It’s resilience. A system that evolves with your business gives you leverage, not dependency. That’s the difference between deploying AI and making it indispensable. When the architecture respects the business, trust follows. And when trust scales, value does too.
