Core Pillars of Enterprise AI Readiness
There are three primary considerations when evaluating your IT environment for AI readiness, says Public Technology Institute Executive Director Alan Shark.
1. AI for the Individual: Driving Workforce Productivity at Scale
Enterprise AI initiatives often begin at the employee level. Generative AI tools can help workers draft reports, build presentations, write code, summarize meetings and improve communications.
“How do we use AI to improve an employee’s productivity and creativity, their ability to better communicate, write better reports, make better presentations and the like?” asks Shark.
In large enterprises, however, tool sprawl quickly becomes a risk. Hundreds or thousands of employees independently adopting AI platforms can create cost overruns, shadow IT and security exposure.
Instead of issuing unrestricted licenses, organizations may consider establishing centralized AI enablement programs or controlled testing environments. Enterprise IT teams can create governed sandboxes where employees evaluate tools before broader deployment.
Standardized endpoint configurations are also becoming more important. As AI PCs and AI-accelerated devices enter the market, organizations may adopt tiered configurations — from light users to power users — ensuring that compute resources align with workload requirements.
FIND OUT: Windows 11 can help with secure device management.
2. AI at the Enterprise Level: Data Governance and Observability
Beyond individual productivity gains, enterprise AI becomes transformative when integrated across departments — customer service chatbots, supply chain optimization, predictive analytics and automated IT service management.
At this scale, governance becomes paramount.
“If you have very sound data policies that take into consideration privacy and security and access, you wouldn’t even perhaps need an AI policy because your existing data policy would govern it,” Shark says.
For enterprises, this translates into:
- Clear data classification frameworks
- Defined ownership and stewardship models
- Chief data officer or equivalent leadership
- Continuous data lifecycle management
Before training models or integrating AI into core workflows, IT leaders must evaluate existing data sets for quality, compliance and structure. They must also define how future data will be collected, classified and retained.
“Your output is only as good as your data,” Hurt says.
Many vendors have simplified ingestion and model training, reducing some barriers to entry. But enterprises still need deep visibility into their infrastructure before deploying AI at scale.
Hurt underscores the importance of asset management and observability.
“Once they’ve got all of their assets identified, their hardware and their software, they ultimately have a really good view of their entire enterprise,” he says.
For large enterprises, this means:
- Comprehensive hardware and software inventories
- Cloud and SaaS visibility
- Application dependency mapping
- Real-time performance monitoring
With observability in place, IT teams can identify high-value AI use cases that deliver measurable ROI without destabilizing operations.
READ MORE: Enterprise asset management and observability platforms can become a business advantage.
3. Open vs. Closed AI Systems in Hybrid Enterprise Environments
Public generative AI tools — such as ChatGPT, Perplexity, Google Gemini and Microsoft Copilot — are examples of what Shark calls “open systems.”
“This is where you want to be incredibly careful to make sure employees know that there’s no personally identifiable information or anything harmful or outwardly discriminatory or biased,” Shark says.
For enterprises managing intellectual property, financial data and regulated information, governance around open AI tools must be explicit and enforceable. Risks include:
- Data leakage through prompts
- Model hallucinations
- Compliance violations
- Reputational harm
Closed AI systems, by contrast, operate within restricted domains and are accessible only to authorized users. Many enterprises are now deploying private large language models, small language models or domain-specific AI assistants within private cloud or on-premises environments.
This aligns with a broader infrastructure reassessment.
“People are starting to have second thoughts and saying, cloud is great for storage, but some things are better on-premises,” Shark says.
For enterprise IT leaders, the question is no longer cloud versus on-premises. It is about deliberate workload placement:
- Public cloud for scalable, nonsensitive workloads
- Private cloud for controlled environments
- On-premises infrastructure for latency-sensitive or highly regulated use cases
- Edge deployments for real-time analytics
Balancing these environments is central to AI readiness.
DISCOVER: Hybrid has become the default choice for infrastructure and data storage.
Training Is Crucial at Every Stage of AI Adoption
Technology alone does not create transformation.
“AI is a profound change, and we need profound training and education at every level,” Shark says.
For enterprises, this includes:
- Executive-level AI literacy
- Governance and compliance training
- Developer enablement
- End-user productivity education
Some platforms consolidate AI functionality into familiar interfaces, reducing change management complexity. Integrating AI into tools employees already use can significantly lower training burdens.
“You can use your own language models with ServiceNow, you can use ours or you can use other language models in an interface that is already very familiar to so many organizations,” Hurt says.
Like Hurt, Shark believes the upfront work — data governance, asset visibility, use-case prioritization and workforce training — is well worth the effort.
“AI can be very powerful with the right data sets in that it can identify within milliseconds patterns, trends and predictions faster than a human ever could,” Shark says.
For enterprise IT leaders, the competitive risk may not be adopting AI too quickly; it may be waiting too long to build a foundation that enables it.
