From: aidotengineer
Steven Moon, founder of Hai, discusses a different perspective on Enterprise AI deployment, one that emphasizes leveraging existing infrastructure rather than building parallel systems [00:00:13]. The core challenge in deploying AI agents and solutions isn’t solely about powerful models or clever problems, but about deploying these “digital workers” in a way that respects existing Enterprise security, compliance, and workflows that organizations have spent years perfecting [00:00:25].
A New Computing Paradigm
AI Engineers are at a unique moment where it’s possible to build software applications that understand humans directly and can use the same interfaces as people [00:00:42]. Large Language Models (LLMs) represent a new computing paradigm where AI agents can reason requests, understand context, and interact naturally through existing channels [00:00:54].
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, set of credentials, and security review [00:01:11]. This approach creates more barriers between users and new capabilities, rather than embracing the direct understanding and interaction that AI agents offer [00:01:21].
Satya Nadella, CEO of Microsoft, observed a fundamental shift in the future of business software, suggesting that traditional SaaS interfaces are becoming obsolete as AI agents become the primary interaction method with business systems [00:01:31]. Building new AI portals and dashboards recreates a pattern that is becoming outdated [00:01:53].
Leveraging Existing Infrastructure
Enterprise AI agents should function like any other employee, adhering to security policies, using approved systems, staying within data boundaries, accessing only necessary information, and being monitored and audited like human employees <a class=“yt=“yt-timestamp” data-t=“00:02:00”>[00:02:00].
The good news is that Enterprises already possess the necessary components:
- 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 [00:02:26]. Many companies have their own private clouds where AI agents can operate within their security boundaries [00:02:31]. Modern AI infrastructure enables running agents in private clouds, keeping data within tenants, using existing security controls, leveraging current workflows, and maintaining complete oversight [00:02:37]. The technology exists today to deploy AI with the same privacy controls applied to human employees [00:02:47].
“Every time we reflexively create a new interface for AI agents, we’re potentially solving yesterday’s problem. We’re building translation layers between humans and machines at exactly the moment when machines can finally understand us directly.” [00:02:54]
Instead of redesigning portals or dashboards, the question should be whether a capability can be delivered through systems users already know and trust [00:03:09]. Existing Enterprise infrastructure, such as Microsoft 365 and ERP systems, are battle-tested platforms integrated into security and compliance frameworks over years [00:03:21]. Building on these platforms allows AI agents to inherit existing trust and infrastructure [00:03:40].
AI Agents as Employees: The IT Department as HR for AI
A powerful aspect for IT departments is the ability to provision AI agents exactly like human employees [00:03:47]. Jensen Huang articulated this shift, stating that “the IT department of every company is going to be the HR department of AI agents in the future” [00:03:55].
IT teams can:
- 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 means no new systems to learn or special handling; an AI agent is simply another employee managed through existing tools [00:04:24].
Agent-to-Agent Communication via Email
Email offers a powerful pattern for agent-to-agent communications [00:04:42]. Just as humans use email for collaboration, AI agents can email each other to share data and coordinate work [00:04:47]. Every interaction is fully logged and auditable [00:04:57], permissions are automatically enforced through existing systems [00:05:00], and data flows are transparent and controllable [00:05:04]. This framework helps in building observable, controllable AI systems at Enterprise scale [00:05:06]. While Hai chose Microsoft’s ecosystem, these patterns apply to Google Workspace or other Enterprise platforms [00:05:13].
The Future of Enterprise AI
The key insight for AI Engineers is to leverage existing Enterprise infrastructure instead of building parallel systems [00:05:20]. These platforms provide built-in identity management, established security controls, proven compliance frameworks, and Enterprise-grade APIs [00:05:27]. This allows focus on building new capabilities and solving new problems, rather than reinventing existing infrastructure [00:05:36].
The future of Enterprise AI is not about building new interfaces for agents, but about enhancing the systems that have been perfected over decades [00:05:47]. Every Enterprise has hardened, secured, and refined systems (e.g., document management, internal messaging, workflow tools) [00:06:01]. Now that software agents can directly understand human intent, each of these systems becomes a potential gateway for AI capabilities [00:06:14].
This represents a fundamental shift in Enterprise AI adoption and application development [00:06:23]. Instead of asking what new tools are needed, the question should be which existing systems can be enhanced with AI agents [00:06:31]. The most powerful solution might be quiet intelligence added to tools customers already trust and use daily [00:06:39]. The era of mandatory translation layers between humans and machines is ending, giving way to direct understanding and seamless AI collaboration [00:06:50].