Why Context is the New Gold

The AI revolution is at an impasse, not due to a lack of computational power, but because organizations are often trying to solve the wrong problem. In 2025, global spending on generative AI is forecasted to reach a staggering $644 billion, showing that companies are pouring money into AI like never before. Yet many of these big-budget projects are falling flat. Experts warn that over 40% of AI initiatives involving autonomous agents (“agentic AI”) will be canceled by 2027. In other words, despite the investment, a lot of AI projects aren’t delivering value. The reason isn’t the hardware or algorithms; it’s the lack of context and understanding. Simply put, if an AI system doesn’t truly comprehend the meaning of the data and the business problems at hand, all the computing power in the world won’t save it.

While companies have been obsessing over model sizes and processing speed, a fundamental shift is underway: the next phase of enterprise AI is about deeper comprehension. Context is becoming more important than raw compute. Recent industry moves underscore this change. For example, data cloud company Snowflake spent $250 million to acquire Crunchy Data (a PostgreSQL provider) to infuse semantic AI capabilities into its platform. Another major player, cybersecurity firm Rubrik, acquired the AI startup Predibase with the goal of enabling customers to “securely deploy agentic AI” by focusing on contextual accuracy as much as computational power. These moves show that smart money is moving toward context-first AI. The industry leaders are betting that the real competitive advantage lies in AI that understands nuance and business context, not just in AI that runs faster or on more data.

Smart Money

For organizations developing AI-powered products, the message is clear: context-aware AI is where the biggest rewards lie. According to S&P Global Market Intelligence, many companies in 2025 have refined their AI initiatives, with 42% rethinking projects that lacked clear business understanding. This isn’t a sign that AI development is risky: it’s proof that success depends on building AI agents with real-world context baked in.

Technical power alone isn’t enough. An AI agent that processes data but doesn’t understand what “customer lifetime value” means across marketing and finance will miss the mark. That’s why leading companies are shifting focus to context-first AI development, embedding semantic architecture and business logic directly into their systems. Industry leaders like Snowflake are already moving in this direction. By enhancing semantic AI capabilities, they aim to give developers the ability to build trustworthy, context-aware AI agents that scale securely and deliver real business impact. The takeaway is simple: startups and product teams that prioritise context in their AI development strategy will unlock greater value, faster, turning AI from a basic tool into a true competitive advantage.

Where Context Meets Scale

Real-world success stories are beginning to prove the value of a context-first approach to AI. A great example is the recent collaboration between Palantir and Qualcomm, which demonstrates the transformative power of combining context with scale. Palantir, known for its data analytics platforms, introduced an “Ontology” approach in its AI architecture. In simple terms, an ontology is like a shared vocabulary and set of relationships for a business: it maps out concepts (like products, customers, or workflows) and the rules and relationships between them in a format that machines can understand. By building this semantic map of the business, Palantir’s AI can do more than just pattern recognition; it performs plain-speak business reasoning.

How does this work in practice? In their partnership with Qualcomm, Palantir embedded this ontology-driven, context-rich AI into resource-constrained environments (even running on devices with limited connectivity or processing power). The AI doesn’t just crunch data; it understands the meaning behind the data. For instance, in Palantir’s work with nuclear energy companies, the AI system goes beyond predicting when a piece of equipment might fail. It actually comprehends the cascading business impacts of that failure – from supply chain disruptions to regulatory compliance issues that could arise as a result. In a manufacturing setting, the same AI can grasp how quality control, inventory levels, and customer delivery commitments are all interrelated. This holistic understanding means the AI can predict problems and suggest fixes before small issues snowball into big ones, because it sees the bigger picture.

This context-driven capability is a game-changer. It enables AI to operate effectively even when it’s offline or working with limited resources, because the AI “knows” what’s important in the business. As one Palantir executive explained, the ontology-centric approach allows teams to combine different sources of business logic and rules into unified workflows. In other words, it’s now possible to introduce AI securely into very complex decision-making processes, because the AI respects and understands the varied business rules at play. Palantir’s success shows that when AI systems are built with a deep understanding of context, they can scale across different scenarios and still make sound decisions — they effectively become context-aware collaborators rather than just number-crunchers.

The Context-First Infrastructure Revolution

The shift from efficiency-first to meaning-first architectures represents a fundamental rethinking of enterprise AI infrastructure. For years, companies focused on making AI run faster and more efficiently (which is still important), but now they are realizing that making AI smarter about the business is the real key to success. This industry-wide transformation toward context-first infrastructure hinges on three critical factors (as highlighted in Gartner’s 2025 Data & Analytics Summit):

Semantic Data Architecture

Every piece of data should carry business meaning, not just a raw value. In practice, this means adding a semantic layer on top of your data. A semantic data architecture ties data points to real-world business concepts and definitions. Research from Enterprise Knowledge (a consulting firm) shows that semantic layers act as a bridge between raw data and the applications that use that data. By implementing this layer, organizations can create unified and contextualized views of information, so that both humans and AI systems can interact with data in an intuitive, business-friendly way. Instead of data being just numbers in a database, it becomes meaningful information (for example, a data point isn’t just an ID number – it’s recognized as a Customer ID with all the associated attributes and relationships that entails).

Business Logic Integration

To deliver real value, modern AI can’t operate in a vacuum – it must be tightly integrated with an organization’s existing business logic and processes. This means embedding the company’s rules, policies, and domain expertise directly into AI systems from day one. Microsoft’s Copilot Studio is a strong example of this approach. It allows organisations to design and deploy AI agents that are deeply connected to their internal applications, data sources, and workflows. Through seamless integration with Microsoft 365, Dynamics 365, and custom APIs, businesses can ensure their AI agents understand the rules and constraints unique to their operations. Combined with frameworks like Model Context Protocols (MCP), this approach ensures context-rich data interpretation across departments and systems. The result is an AI development process where agents not only automate tasks but make decisions that align with the company’s strategic goals, compliance requirements, and real-world context. By preserving enterprise-specific logic within AI workflows, organizations create intelligent systems that are trustworthy, autonomous, and truly business-aware.

Contextual Decision Engines

It’s not enough for AI to complete a task; in enterprise settings, the AI must understand the business implications of every task and decision. A contextual decision engine means the AI evaluates actions through the lens of business impact. McKinsey’s 2025 workplace AI report emphasizes that successful AI systems are the ones deeply tuned into the organization’s goals and context. Yet, only about 1% of companies feel they’ve achieved true AI maturity. This statistic highlights a huge gap: many companies have capable technical AI, but very few have AI that truly understands their business. To bridge this gap, AI systems need built-in context awareness so they won’t, say, prioritize a minor cost savings if it means violating a key compliance rule or damaging customer trust. Contextual decision engines weigh decisions like a seasoned business executive would, considering not just efficiency but also strategy, ethics, and long-term implications.

These three factors together signal a revolution in AI infrastructure. Instead of treating context as an afterthought, leading organizations are architecting their AI systems around context from the ground up. This “context-first” approach means that when an AI agent is deployed, it’s not just powerful – it’s knowledgeable. It understands the language, rules, and priorities of the business on day one.

The Competitive Implications

Why does all this emphasis on context matter in competitive terms? Because organizations that successfully build context-rich AI systems will gain self-reinforcing advantages that competitors will struggle to copy. When an AI truly understands your business, every interaction becomes a learning opportunity that deepens the AI’s nuanced understanding of your specific needs and challenges. Over time, this creates a virtuous cycle: the more the AI is used, the more context it accumulates, and the more effective it becomes. The result is an AI that gets smarter and more attuned to your business with each passing day, which is a competitive moat that a rival can’t easily replicate just by spending more on servers or hiring a few data scientists.

There’s evidence that focusing on context delivers better outcomes. Deloitte’s latest “State of Generative AI” report found that while a majority (about 60%) of organizations are dabbling in up to 20 different AI experiments, the ones that saw dramatically better results were those zeroing in on industry- and business-specific challenges. In other words, AI initiatives that were grounded in specific context (a particular industry problem or a particular business process) outperformed those that were more generic. It turns out that contextual relevance beats breadth of experimentation. An AI model fine-tuned to understand, say, insurance claims processing in depth will likely generate more value for an insurance company than ten different pilot projects that aren’t integrated with how the business actually works.

There are also talent implications to this context-first shift. Yes, AI engineers and machine learning experts are in high demand, but an equally scarce skill set now is the AI domain expert – someone who understands both advanced AI techniques and the business domain or industry in question. It’s one thing to code an AI algorithm, but quite another to teach an AI about the nuances of retail supply chains or healthcare regulations. PwC’s 2025 predictions note that

“AI success will be as much about vision as adoption.”

Companies need teams who have the vision to align AI with business strategy and the domain knowledge to implement it correctly. If the people building and training your AI agents don’t deeply understand your business, those AI agents won’t understand the business either. This means companies investing in AI need to either upskill their domain experts in AI, or vice versa, to create these hybrid roles. Organizations that recognize and fill this talent gap will be better positioned to create AI solutions that truly resonate with their business needs, leaving competitors who focus only on technical talent behind.

In summary, a context-rich AI strategy creates a flywheel effect: better understanding leads to better performance, which attracts more usage and more data, which further improves understanding. Competitors who stick to brute-force AI (just bigger models and more data without context) will find themselves outpaced by those who infuse context and meaning into every layer of their AI systems.

The Strategic Imperative

All these trends point to a clear strategic question for companies: What changes should we make to build AI that truly understands our business? In other words, how can an organization architect its AI and data systems today to win in this context-driven future?

First, companies need to start treating semantic information as a first-class citizen in their data strategy. Gartner’s Data & Analytics Summit has highlighted the importance of shifting from technical metadata to semantic metadata. Technical metadata is basically data about data – things like table schemas, data types, and processes. Semantic metadata, on the other hand, enriches data with business definitions, ontologies, and relationships. This is a fancy way of saying that data is tagged or structured with context about what it actually means in human/business terms. For example, instead of just recording a field as “Category ID: 7”, semantic metadata would link that ID to “Product Category: Electronics”. This clarity ensures that whether a person or an AI model looks at the data, they immediately grasp its meaning. Adopting a semantic-first design means when you build new databases, data lakes, or AI models, you embed this kind of meaning and context from the start. It’s a shift in mindset: from “data as numbers and strings” to “data as knowledge.”

Second, organIsations must implement effective contextual AI governance. As AI capabilities advance, there’s a risk of confusion (or even hype) in the market. Some AI solutions might be marketed as “agentic AI” or context-aware when they’re basically just doing automated tasks without real understanding. Good governance means having frameworks and checkpoints to evaluate whether an AI system is truly learning and using context appropriately, and ensuring it’s aligned with ethical and operational standards. This involves everything from data governance (making sure the AI is trained on high-quality, relevant data) to model validation (verifying that an AI agent’s actions make sense in the business context and don’t create new risks). Governance is also about transparency and trust. Business leaders and AI teams need to be on the same page about how decisions are made by AI and what business rules or context are being applied. By tightening governance around context, companies can differentiate between true agentic AI – systems that genuinely understand and act with agency in a business – versus basic automation tools that might be dressed up with buzzwords. This way, enterprises invest in the right technologies and avoid chasing fads that won’t deliver lasting value.

“To get real value from agentic AI, organizations must focus on enterprise productivity, rather than just individual task augmentation,”

Anushree Verma, Senior Director Analyst, Gartner.
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Ultimately, the companies that succeed in the coming era of AI will be those that strategically configure their AI agents with deep business context. That means from the boardroom to the data science team, everyone champions the idea that context is not a “nice-to-have” – it’s mission-critical. When AI systems are built on semantic foundations and governed properly, they can be trusted to act autonomously and effectively in complex scenarios. They’ll make decisions that aren’t just technically correct, but contextually wise.

The Agentic AI Opportunity

The push for context and semantic understanding isn’t happening in a vacuum – it aligns with the rise of agentic AI in the enterprise. Gartner predicts that by 2028, 33% of enterprise software will include agentic AI capabilities, a huge jump from less than 1% in 2024. This trajectory shows how quickly autonomous AI agents are expected to become commonplace in business software. These AI agents will handle multi-step tasks, make recommendations, and even take actions on behalf of humans. But to do all that effectively, they must be built on a foundation of context. If not, we’ll see a lot of those projects hit the same wall we discussed earlier.

To truly seize the agentic AI opportunity, organizations need to ensure their AI systems have a few essential capabilities from the start:

As companies pour billions into developing agentic AI, those that neglect these semantic foundations will likely see escalating failure rates. We’re essentially heading toward a split in the road: on one side, organizations whose AI investments thrive because they’ve built an ecosystem of understanding around their AI; on the other, those whose AI projects repeatedly stumble or cause mishaps because the systems lack context and coherence. For any startup founder, product manager, or tech leader eyeing the agentic AI wave, the message is clear: if you want your autonomous AI projects to succeed, invest in context and semantics just as much as you invest in algorithms and infrastructure.

The Context Imperative

As agentic AI systems become more prevalent, the gap between organizations with context-grounded AI and those without will widen dramatically. We are entering an age where having abundant computing power or the latest model isn’t a sustainable advantage on its own. The differentiator will be contextual intelligence. Businesses that have laid down semantic infrastructures and prioritized contextual understanding in their AI will watch their AI agents turn into insightful advisors and efficient colleagues. Those that haven’t will wonder why their AI, despite being state-of-the-art on paper, can’t keep up.

For enterprises – from innovative startups to large corporations – investing in AI with context is no longer optional; it’s imperative. If you’re planning an AI-driven product or feature, you need to teach your AI about the world it operates in: your world. This means now is the time to build those semantic foundations, map out your business ontologies, and ensure your AI initiatives are tied into real business objectives and knowledge. The payoff isn’t just avoiding failure; it’s gaining a powerful competitive edge. Context-aware competitors will turn their savvier AI investments into advantages that compound over time, creating a gap that late adopters will struggle to close.

In the age of abundant compute power and endless AI tools, context is the new gold. Organizations that can teach their AI systems to truly comprehend the business they serve will effectively have the Midas touch – turning data into insights and insights into impactful action. The strategic lesson for every startup founder, product manager, and business leader is this: by making context the cornerstone of your AI strategy, you’re not just adopting AI – you’re unlocking AI’s true potential to transform your business. In this new era, those who strike gold will be the ones who understood that meaning matters just as much as math.

At Digital Bricks, we help businesses move beyond generic AI tools to build agentic AI solutions that truly understand their domain. Our approach combines semantic architecture, business logic integration, and AI agent development to ensure your systems aren’t just powerful but context-aware. Whether you’re a startup founder designing an AI-driven product or a product manager looking to scale intelligent features, we work with you to create AI that can reason, act autonomously, and deliver real business impact. If you want to embed context into your AI development and stay ahead of competitors, Digital Bricks can help you lay the right foundations from day one.