From: aidotengineer
Steven Moon, founder of Hai, advocates for a paradigm shift in enterprise AI deployment, urging organizations to leverage decades of existing enterprise infrastructure instead of building parallel systems [00:00:15]. For builders of AI agents and solutions, the core challenge lies not just in powerful models but in deploying these digital workers in a way that respects established enterprise security, compliance, and workflows [00:00:30].
The New Computing Paradigm
AI engineers are at a unique moment where software applications can understand users directly and interact using the same interfaces as people [00:00:45]. Large Language Models (LLMs) represent a new computing paradigm, enabling AI agents to reason, understand context, and interact naturally through existing channels [00:00:56].
Avoiding Old Patterns in AI Deployment
Despite this new paradigm, there’s a tendency to fall back into old patterns when deploying AI agents in enterprises [00:01:09]. Each new AI agent often becomes another external system, portal, requiring new credentials and security reviews [00:01:11]. This approach builds more barriers between users and new capabilities, rather than embracing agents that can use human interfaces and understand directly [00:01:25].
Microsoft CEO Satya Nadella observed a fundamental shift in business software, suggesting that traditional SaaS interfaces are becoming obsolete as AI agents become the primary way to interact with business systems [00:01:34]. Yet, many are still building new AI portals and dashboards, recreating patterns that are becoming outdated [00:01:53].
Leveraging Existing Enterprise Infrastructure
Enterprise AI agents should function like any other employee: adhering to security policies, utilizing approved systems, respecting data boundaries, accessing only necessary information, and being monitored and audited [00:02:00]. The good news is that enterprises already possess the necessary components [00:02:13]:
- Secure compute environments [00:02:16]
- Identity management [00:02:19]
- Data governance [00:02:21]
- Compliance frameworks [00:02:21]
- Audit capabilities [00:02:23]
These systems have been refined over decades, and many companies have their own private clouds where AI agents can operate within their security boundaries [00:02:26]. Modern AI infrastructure allows agents to run in private clouds, keep data within tenants, use existing security controls, leverage current workflows, and maintain complete oversight [00:02:38]. The technology exists to deploy AI with the same privacy controls applied to human employees [00:02:49].
AI Agents as Digital Employees
Rather than creating new interfaces, the focus should be on delivering AI capabilities through systems users already know and trust [00:03:09]. Existing enterprise infrastructure like Microsoft 365 and ERP systems are battle-tested platforms integrated into security and compliance frameworks [00:03:22]. Building on these platforms allows AI agents to inherit established trust and infrastructure [00:03:40].
Nvidia CEO Jensen Huang stated that “the IT department of every company is going to be the HR department of AI agents in the future” [00:03:55]. This means IT teams can provision AI agents exactly like human employees [00:03:50]:
- Create agent accounts using existing Active Directory tools [00:04:13]
- Apply standard security policies [00:04:16]
- Set permissions through familiar interfaces [00:04:17]
- Use existing audit and monitoring tools [00:04:21]
This approach eliminates the need for new systems or special handling, treating AI agents as another employee managed through familiar tools [00:04:24].
Agent-to-Agent Communication and Oversight
Systems like email open up powerful patterns for agent-to-agent communications, similar to how humans collaborate [00:04:44]. AI agents can email each other to share data and coordinate work, with every interaction logged and auditable [00:04:50]. Permissions are automatically enforced through existing systems, and data flows remain transparent and controllable [00:05:00]. This creates a framework for building observable, controllable AI systems at enterprise scale [00:05:06]. These patterns are not limited to one ecosystem and can work with platforms like Google Workspace or other enterprise platforms [00:05:13].
A Fundamental Shift in Enterprise AI
The key insight for AI engineers is to leverage existing enterprise infrastructure rather than building parallel systems [00:05:20]. These platforms provide built-in identity management, established security controls, proven compliance frameworks, and enterprise-grade APIs [00:05:30]. This allows engineers to focus energy on building new capabilities and solving problems instead of reinventing infrastructure [00:05:38].
The future of enterprise AI isn’t about building new interfaces; it’s about enhancing the systems perfected over decades [00:05:47]. Every enterprise has existing systems—document management, internal messaging, workflow tools—that, with the ability of software agents to understand human intent directly, become potential gateways for AI capabilities [00:06:03].
This represents a fundamental shift in how organizations approach AI in business operations and application development [00:06:23]. Instead of asking what new tools are needed, the question should be: “Which of our existing systems can we enhance with AI agents?” [00:06:31]. The most powerful solution may be the quiet intelligence added to tools customers already trust and use daily [00:06:45]. The era of mandatory translation layers between humans and machines is ending, giving way to direct understanding and seamless AI collaboration [00:06:51].