Telecom & Technology Consulting
Enterprises continually make investments to maintain competitive advantage today, as well as to set up them for success in the future. And these days, artificial intelligence (AI) is the main focus for those enormous investments. These include an array of AI tools such as intelligent copilots, automation agents, workflow assistants, and others that can deliver company-wide efficiency gains.
No one could tell a CEO that this is the wrong financial or strategic decision for the organization. However, results for AI investments don’t always meet expectations – at least, not immediately. Organizations often realize some initial success, only to witness them reach an insurmountable peak and stall at the top of the hill. In too many cases, the projects simply fall flat and get shelved.
The problem is that AI systems can’t (yet) understand, or detect, the business context in which they’re implemented. They accelerate automation but don’t have the ability to know why they’re solving a specific problem. They’re programmed to work with data but don’t know the history of that data. AI can do wonders, but it doesn’t work in context by itself. And that’s how AI projects can hit a roadblock.
Without context, AI initiatives enter an ever-growing gap between data and interpretation. No one on the team, from the CTO to project managers to engineers, realizes there’s a problem until smart assistants or agentic workflows irritate staff and end users.
That scenario has become the driving force for what is known as organizational context. From a practical standpoint, this type of context provides an ongoing graph of the executives, team members, processes, systems, and policies that undergird the AI project. When AI projects are launched without this context, organizations will only see, at best, incremental improvement of automation rather than the scaled transformations hoped for in the AI strategy plan.
Part of creating organizational context means transforming data to intelligence, and in turn transforming that to insight. As such, you get real-world context for data and then implement that into your AI systems. Done correctly, organizational context is regularly updated. This layer of enterprise activity with multiple connections provides a helpful visual perspective between employees, technical assets, processes, services, applications, and platforms.
On a granular level, you see details like user attributes (employee department, role, etc.); device information (assigned laptops, mobile devices, etc.); and IT infrastructure dependencies (network devices, servers, storage, etc.).
Traditional databases can only attempt to capture a portion of data. But the organizational context layer provides a new level of power: data capture is dynamic, and is connected to entire systems. HR systems, identity providers, SaaS platforms and more sync to a single unified layer that immediately trace the changes between them.
Another benefit of organizational context is that you gain a deeper level of organizational awareness. For example, if an employee reports that one of her apps continually crashes, support personnel can identify that she works in Austin using a Dell laptop, which runs on a specific operating system using a specific version of the app. Support (for both customers and internal teams) becomes increasingly personalized, which then delivers higher satisfaction and decreased costs.
The right tools, technology, and systems can only take you so far. You have a better chance of reaching your AI goals – and then some – by putting your AI systems in a different place. It’s all about context.
