The context advantage: Why your company's collective ethos is the new AI frontier

As more organizations access the ability to generate software and automate tasks with AI, traditional technology advantages are becoming less distinctive. In this new era, competitive edge will increasingly reside in how technology works within a company's unique context. This collective “hustle” of teams often remains intangible, however; embedded in group knowledge, implicit processes and daily work interactions. But what if an organization could encode its essential corporate DNA in its AI systems?

By weaving on-the-ground operational intelligence into the fabric of AI, businesses can unlock significant productivity gains, codify their distinctive culture, and dynamically optimize human-machine collaboration for a powerful source of differentiation.

Looking ahead, it's clear that AI will increasingly power every company. While this will certainly lead to significant gains in efficiency, it will also erode the power of technology as a differentiator.

In this future of commoditized technology, an enterprise's unique way of operating will be what truly sets it apart. The way winning teams collaborate and innovate, the nuances of communication, the shared values and collective “hustle” form a living blueprint of competitive advantage. Making AI contextual to real-world workflows enables leaders to evolve towards an AI-driven operating model that safeguards – and elevates – what sets their organization apart.

The challenge is how to make this intangible heterogeneity actionable, capture the context of work, and apply it across a hybrid human and digital workforce.

Consider the idea of a "work graph" – an anonymized, aggregated view of how teams execute processes: the sequence of applications used, the data accessed, the communication patterns, the variations in workflow, the points of friction, and the successful shortcuts.

Using work graphs, companies can capture the group knowledge that defines how a specific team actually gets work done and use it to train AI systems to be more effective.

Take the example of an AI agent designed to assist with drafting a complex document, like a contract. A generic model can generate boilerplate language. But if that agent has access to the work graph of the legal team – understanding the specific clauses they commonly use, the systems they retrieve data from, the typical sequence of review steps, and the past interactions related to this specific type of contract – it can generate a draft that is far more precise, relevant and immediately useful.

Training AI systems on specific enterprise context turns technology into a highly effective collaborator that understands and complements the existing workflow and knowledge base.

The concept of the "jagged frontier" of AI capabilities highlights that the boundary between tasks best suited for humans and those for machines is not a predictable line. As AI capabilities expand, this frontier constantly shifts.

Optimizing collaboration along this jagged edge requires AI that understands when and how to engage with a human colleague. A contextualized AI agent, trained on the work graph, knows the team's typical workflow, pain points and areas where human intervention is usually required. It can anticipate needs, provide relevant information precisely when needed, handle routine variations autonomously, and flag exceptions or complex scenarios for human attention, often suggesting potential next steps based on learned patterns from the team's past behaviour.

This intelligent hand-off is the essence of optimizing productivity on the jagged frontier. It allows humans to focus on the uniquely human tasks – like navigating ambiguity, persuasion and collaboration – while the AI handles the contextualized automation of supporting activities. The result is not just individual efficiency, but a significant boost in team productivity, as the collective output becomes greater than the sum of its parts.

By capturing the group knowledge and interaction patterns of high-performing teams, organizations are able to in effect digitize and codify aspects of their culture – how things really get done, the informal processes that drive success.

An enterprise that begins with a “blank slate” generic AI model and aims to redesign every workflow risks stripping away winning cultural DNA. Contextualizing AI enables leaders to start from an already high rung on the ladder and optimize from there, while preserving enterprise distinctiveness.

Just as culture evolves, so too do work patterns and the context of work. Businesses should therefore treat this context-capture and optimization process as a continuous loop. This iterative approach of machines learning from humans and optimizing in alignment with the unique culture of the business will ensure that AI’s practical applications remain relevant, effective and competitively differentiating.

To begin exploring this strategic edge today, enterprise leaders should consider several key aspects:

The future is multi-agent and context-driven. Leaders who embrace their organization’s strategic heterogeneity and deploy AI agents that are intelligent in the context of the work being done will amplify team productivity and build enterprises uniquely differentiated by their culture.​​​​​​​​​​​​​​​​