How to build an enterprise AI framework for your business

Businesses that want to be at the forefront of AI need to enter a new phase of AI adoption. One where AI is embedded into daily operations, decision-making and business processes. To realize the full value of AI, organizations need more than a collection of disconnected tools and siloed use cases. They need an enterprise AI framework.

An enterprise AI framework provides the foundation for scaling AI across the organization. It aligns people, data, technology and governance so AI can support employees, automate repetitive work and deliver business insights. Rather than treating AI as a standalone tool, businesses can create an environment where AI is one part of an enterprise approach that drives workflows.

The next AI boom will not be driven solely by generative AI assistants, but by systems and AI agents that understand business operations, access enterprise data and execute tasks enterprise-wide. To prepare for that future, organizations should focus on these five critical building blocks of enterprise AI frameworks.

1. Create an AI-first workforce

A successful enterprise AI framework isn’t built on technology alone. Your staff needs training and skills to work alongside AI.

You also need to install an AI-first culture. Train your employees to ask themselves, “Can AI improve this process?” before starting a new project or trying to solve a problem.

To create an AI-first workforce, organizations need to make sure employees have access to effective and vetted tools, understand how to use them effectively and feel confident incorporating AI into their daily responsibilities.

Here are the steps to building an AI-first workforce:

Invest in training and education

Training is essential for employees at every level. Many of your workers probably know how to use tools such as ChatGPT or Microsoft Copilot for simple tasks. But they may not realize how AI can support analytics, automation, decision-making and process improvement.

Give your staff training that isn’t just focused on how to use AI tools, but on helping them understand AI's broader capabilities and how to use it as part of most business processes.

Establish AI policies and governance

To address security, privacy and ethical concerns, employees need clear guidance on appropriate AI use. Responsible use policies, security guardrails and governance frameworks help employees understand what is safe, compliant and effective.

Employees need clear guidance on how to use AI responsibly. As AI tools gain access to business data, customer information and internal processes, organizations must establish governance frameworks that balance innovation with security, compliance and risk management. Without clear guardrails, businesses may struggle with inconsistent use, data privacy concerns and regulatory challenges.

Effective AI governance should define approved tools, acceptable use cases, data handling requirements and oversight responsibilities. Employees need to understand what information can be shared with AI systems, when human review is required and how AI-generated outputs should be validated before use.

Policies should also address issues such as intellectual property, data retention, model transparency and compliance with industry-specific regulations.

Address workforce concerns

Many workers are afraid that AI will take their jobs. Clearly communicate that AI is intended to improve productivity and enhance employee capabilities, not replace them. To improve adoption rates, position AI as a tool that allows workers to focus more on higher-value tasks. Emphasize that acquiring AI skills will enhance career opportunities.

Demonstrate real-world value

Many of your employees have probably used AI primarily for drafting emails, summarizing meetings or generating content. While these are valuable use cases, they only scratch the surface of what AI can accomplish. To drive meaningful adoption, organizations need to show employees how AI can make their lives easier by solving real business problems, streamlining decision-making and eliminating repetitive work.

Focus on demonstrating practical, role-specific applications that employees can immediately connect to their day-to-day responsibilities.

Examples include:
• A salesperson might use AI to gather insight on a client’s key initiatives to prepare for an account review.
• A finance team member might use AI to identify and analyze budget variances. They can then communicate those variances to the CFO with recommendations for new budget limits. When the CFO approves the new budget cap, AI adjusts purchasing rules to require approval before purchasing identified services or raw materials.
• An operations manager could leverage AI to identify bottlenecks or forecast staffing needs.

When employees see AI helping them complete tasks faster and make better decisions, they are more likely to embrace it as a valuable business tool.

Be sure to share success stories across departments. Highlighting examples of employees who have saved hours of manual work, improved customer service or uncovered new business insights helps others recognize the technology’s potential.

Think of AI as an additional workforce

Don’t look at AI as just another piece of software. AI is a digital workforce that works alongside employees. And just like your new human employees, AI needs onboarding.

Before AI can start delivering quality outputs, it needs:
• Employees properly trained in AI use
• Access to high-quality data
• An understanding of established processes
• Knowledge of the organization’s operations

Before you can give your AI systems access to quality data that will drive their success, you need to execute building block #2.

2. Create a data foundation

Your AI framework will only be as strong as the data supporting it.

To truly be productive, AI systems need high-quality, connected and accessible information. Without a strong data foundation, AI tools lack the context needed to deliver accurate, meaningful results.

Data infrastructure mistakes

When building a data foundation to support an enterprise AI framework, here are some roadblocks you may encounter:

• Shared file systems don’t work well for enterprise AI development. Problems include not being indexed, lacking metadata and not keeping track of document versions.
• On-premise infrastructure lacks the horsepower for modern AI systems.
• When data from different systems (ERPs, HR systems, CRMs, email, etc.) is stored in different locations, it creates silos that prevent AI tools from accessing all your business data when making decisions.

Steps to prepare your data for AI

Here are some steps to take to build a data foundation that will support enterprise-level AI:

Move toward cloud-enabled infrastructure

Cloud-enabled environments provide the flexibility and computing power needed to support enterprise AI initiatives. They make it easier to integrate data from multiple sources, scale storage as you accumulate more data and leverage advanced AI services without significant investments in new hardware. Moving to the cloud also helps organizations create a more connected technology ecosystem, positioning them to adopt new AI capabilities as they emerge.

For many businesses, transitioning from an on-premise to a cloud environment is a critical first step toward building a sustainable enterprise AI strategy.

Create a unified data platform

Many organizations are adopting data lakehouse architectures that bring structured and unstructured data together in one environment.
Eliminating data silos allows AI systems to draw insights simultaneously from financial records, operational metrics, policies, customer communications and other sources. This broader context enables more accurate analysis, better decision-making and more powerful AI-driven automation. By breaking down data silos and creating connections across systems, organizations establish the foundation needed for AI assistants and agents to understand and support the business effectively.

3. Build execution agents

Once organizations have an AI-ready workforce and a strong data foundation, they can begin developing AI execution agents.

Execution agents are responsible for gathering information, performing calculations and analyzing business results. They understand where data resides and how to access the information needed to complete specific tasks, such as:

• Calculating sales by product line
• Analyzing labor costs across departments
• Retrieving operational performance data
• Consolidating information across multiple business systems
• Generating reports and dashboards

In many ways, execution agents perform activities traditionally completed by business analysts. They locate information, aggregate data and provide answers to operational questions quickly and consistently.

As these capabilities mature, execution agents can automate increasingly complex business processes and reduce manual effort across the organization.

4. Build intelligence agents

Execution agents provide information. Intelligence agents provide understanding by leveraging your company’s business ontology, operating model, policies and institutional knowledge to apply context to the results generated by execution agents and recommend actions.

Examples include:

• Identifying inventory issues and recommending adjustments
• Interpreting financial performance trends
• Evaluating operational risks
• Recommending process improvements
• Supporting strategic decision-making
Intelligence agents are built on in-depth company knowledge. In addition to accessing business data, they need to be trained on company-specific information such as organizational structures, workflows, policies, performance metrics and operational priorities. The more deeply these agents understand how the business operates and what success looks like, the better they can interpret results, provide meaningful recommendations and support decision-making in ways that align with the organization’s goals.

Together, execution agents and intelligence agents create a powerful framework that supports both operational efficiency and business insight.

5. Bring it all together

The end goal of an enterprise AI framework is a connected environment where employees and AI work together seamlessly.

In this model:

• Employees interact through AI assistants
• AI assistants coordinate tasks and requests
• Execution agents gather and process data
• Intelligence agents interpret results and provide recommendations
• A unified data foundation supports every interaction

Rather than jumping between disconnected systems, employees can work through AI-powered interfaces that coordinate activities across the organization. Time-consuming tasks that once required multiple applications and manual data transfers can be completed through coordinated AI workflows.

The most successful organizations will start from the ground up. Instead of unleashing dozens of agents from the outset, they will begin by building an AI-ready workforce and establishing a strong data foundation. Once those elements are in place, new AI use cases become significantly easier to implement.

Read more

• AI readiness for your business: It starts with leadership
• AI governance checklist: A guide to managing AI risk by establishing governance
• AI risk management: How to adopt AI securely