Intial Post from Linkedin:
https://www.linkedin.com/posts/lukenorris_generative-ai-genai-has-captured-widespread-activity-7256635850820595712-7t6-?utm_source=share&utm_medium=member_desktop
Generative AI (GenAI) has captured widespread attention, primarily through its accessible consumer applications. Services like Anthropic’s Computer use and similar offerings are popular for enabling users to engage with AI without requiring complex infrastructure. However, while these solutions suit general accessibility, their utility in enterprise environments falls short when it comes to executing high-value, large-scale tasks that demand continuous operational presence and dedicated resources.
These are the use cases and tasks we tackle KamiwazaAI
Anthropic and similar providers use shared-resource models that offer companies scalable AI without the need to build infrastructure. This is ideal for prototyping or smaller tasks but lacks the customization, security, and robust operational control that enterprise-grade applications require. In contrast, GenAI workloads that run within a company's dedicated environment enable the deployment of AI agents that work 24/7, tailored specifically to business needs and integrated into operational workflows.
True agentic workloads—AI programs that operate autonomously—offer transformational potential in the enterprise. Unlike general-purpose AI tools, these agents proactively execute tasks, make decisions, and drive efficiencies on behalf of business functions. They operate continuously, using proprietary data, enhancing themselves over time, and generating value in a way that is nearly impossible with subscription-based AI models.
For instance, a marketing department might deploy an AI agent to manage campaigns in real time, optimizing bids, and generating insights based on evolving market conditions. Such an agent, embedded in the company's infrastructure, continually adapts without requiring user oversight, thus creating a feedback loop of value that increases over time.
With ownership over the infrastructure, enterprises gain the flexibility to optimize AI specifically for their use cases and ensure secure, real-time processing. For example, a manufacturing company could use AI agents to monitor equipment and predict maintenance needs autonomously.
Subscription costs also become unsustainable for businesses that require always-on AI performance. By controlling their AI infrastructure, companies avoid these constraints, gain predictable costs, and retain direct control over data flows, compliance, and operational priorities.
As we enter the Fifth Industrial Revolution, the gap between consumer and enterprise AI will widen further. Enterprise-integrated AI—where agents operate autonomously and at scale within the organization—will drive real-time decision-making and efficiency. Companies that embed GenAI as a core component of their systems will achieve competitive advantages through scalable, continuously improving workflows, fundamentally transforming their industries and business operations.