Intelligence is Solved - Why isn't AI Everywhere?

Every day brings news of exciting AI advancements—whether it’s a breakthrough in energy-efficient AI chips, a breakthrough visual classification system that works with minimal data, or a new language model (LLM) outperforming its predecessors.

Narrow AI, or AI that excels at specific tasks, seems to be everywhere. We could even go so far as to declare that intelligence is ‘solved’.

But if that’s the case, why don’t we see AI’s widespread application across businesses? Where are the promised automation, efficiency gains, and operational improvements?

While narrow AI has made incredible strides, the broader challenge is scaling its application to the dynamic, ever-changing realities of businesses. Automating specific tasks is one thing, but transforming entire workflows, operations, and decision-making systems across diverse industries is a much more complex problem. For example, while we can train AI to recognize images or even converse naturally, turning those capabilities into tangible business impact requires a deeper integration with business goals and processes.

Barriers to Mass Adoption

Several key barriers stand in the way of mass AI adoption across enterprise workflows, regardless of organization size:

The New Human/AI Interface is still up in the air.

The way we interact with AI is still evolving. While LLMs like GPT are commonly deployed in chat interfaces today, is that where it ends? AI’s capabilities go far beyond conversation, from generating dynamic charts to analyzing complex data sets. In this world, should AI be an add-on to existing dashboards, or should it replace them entirely?

In the screen grab below you can see our default AI Assistant interface (available in our Services Catalog) returning a plot on a map. You could easily click on a data point and jump into a new, location-specific AI Agent for further discussion. OpenAI’s real-time API, which enables voice interaction will play an increasingly important role, though you obviously can’t ask for an easy definition of a map in voice or translate any complex visual concept for that matter.

We Won’t Get There with Legacy SaaS ‘Bolting on’ AI: 

Every large-scale enterprise SaaS and ERP platform is desperate to be at the center of the AI explosion within an organization by integrating AI as a bolt-on solution. But it just won’t work.

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These legacy systems are weighed down by outdated structures, data silos, and especially high costs. For instance, Salesforce charges $2 per invocation of their AI agents, making such models unsustainable for widespread use. Instead of relying on patchwork AI integrations, companies must Own Their AI™. 

This means adopting an AI roadmap that supports a dynamic, programmable AI infrastructure – integrating both internal and external data sources, from simple automation to fully autonomous agents.  Companies that Own Their AI have the ability to orchestrate AI solutions across the entire enterprise, whether a solution is enabling sales, operations, finance or service.

Let’s take a sales organization that manages its lead generation and customer service workflows through siloed, expensive SaaS tools. By “owning their AI,” they could deploy an internal AI system that autonomously analyzes customer data, predicts lead potential, and drives automated follow-ups, all while being integrated seamlessly with their CRM, marketing and financial systems. Instead of paying per task, the company gains a scalable AI infrastructure that grows with its needs, freeing it from reliance on external vendors.

We won’t get there with our current development methods.

As AI continues to evolve, businesses will need to leverage hundreds of AI-driven services and autonomous agents for tasks ranging from routine operations to highly complex decision-making. However, there aren’t enough software developers to build and manage this ever-expanding ecosystem, even with AI-enabled tools like code co-pilots. Every department, from marketing to logistics, will have the opportunity to improve efficiency with AI, but this creates a monumental demand that existing technical teams simply cannot meet.  The demand on technical teams will quickly overwhelm their capacity, creating an insurmountable backlog.

The ONLY answer is that AI will create AI.

The only solution to this scalability challenge is that AI will need to create AI. In the near term, business analysts and technical program managers can use tools like Contextual’s SolutionAI to define the AI solutions they need. Our AI orchestration technology then takes over, constructing these solutions from the ground up. Whether it’s analyzing a data set or building a fully autonomous system, AI will design the platforms that replace traditional, costly SaaS and ERP systems.

For instance, imagine a marketing team that traditionally relies on expensive, third-party software to track customer sentiment and generate targeted campaigns. With AI creating AI, the team could input their needs into SolutionAI, and the AI would autonomously build an entire sentiment analysis system and create customized outreach programs—all without requiring software developers to manually build or maintain the system.

Ultimately, AI will create the tailored software solutions businesses need—automatically and efficiently replacing every generic piece of software that exists.

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AI isn’t everywhere … not yet!  However, platforms like Contextual will make it possible, paving the way for widespread AI adoption.

Ready to explore how AI can transform your organization?

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