Artificial intelligence (AI) is transforming the world in unprecedented ways.

From healthcare to education, business to entertainment, and even staff augmentation, AI enables new possibilities and opportunities for human creativity and productivity.

But how can you harness the power of AI for your success? 

How can you leverage AI to enhance your skills, solve problems, and achieve your goals? 

This article will provide tips and best practices for becoming an AI-native organization. We will also share some examples of how AI is used in various domains and industries, and how you can learn from them.

TL;DR

• AI enablement is the process of equipping your organization with the tools, talent, data infrastructure, and governance frameworks needed to deploy AI responsibly and at scale, and not just experiment with it
• In 2026, the challenge has shifted: Only 31% of organizations have scaled their software coding agents, while enterprise-level financial impact or ROI remains unchanged. 
• The key steps to achieve AI enablement are: assess your data and infrastructure readiness, identify and prioritize high-value use cases, run structured pilots, deploy with governance built in, and close the talent gap through hiring or upskilling

MSH helps technology leaders build the AI/ML teams and strategy needed to move from experimentation to production (check out our AI/ML engineer staffing solutions or AI/ML staffing firms guide to learn more)

Before we dig deeper, our resident AI genius, Ryan Burns, has this to say regarding the most important consideration for AI enablement you should have from the start:

"Thoroughly understand what AI can and can’t do for your specific use case; most importantly, implement it responsibly. AI is an accelerant of unprecedented magnitude, and an organization needs to be on stable footing before applying it in a material way. If you add a large motor to a boat with a strong rudder, it will achieve superior performance. If you add a large motor to a boat with a weak rudder, it will capsize."

- Ryan Burns - Economist & Chief Data Officer @ MSH

AI Enablement In 2026: What Has Changed

The conversation around artificial intelligence enablement has fundamentally shifted. In 2023 and 2024, most organizations asked, "Should we use AI?" In 2026, that question is settled. So, the real question has shifted from intent to results. Does the organization have the capacity to get business value from it? (spoiler alert: most don’t yet.)

From Pilots To Production: The New Dividing Line

A deployed pilot proves a demo works but success means business adoption, making that model operational in production. McKinsey’s State of AI in 2026, finds the dominant enterprise AI pattern in 2026 continues: a small number of high-performing use cases surrounded by a larger number of stalled pilots. Only 31% of organizations have scaled their software coding agents; and the enterprise-level financial impact remains unchanged.

This chasm has only grown. Writer’s Room “2026 AI adoption in the enterprise” survey, polling  2,400 executives and employees, found that 79% of organizations face challenges in adopting AI, a double-digit increase from 2025.

Writer’s room says,

   “Last year’s theme was tension — budgets climbing, pilots multiplying, but progress messy. In 2026, that tension has evolved into something much more consequential: it’s now cultural, organizational, and deeply structural.”

AI enablement programs bridge this gap by providing the infrastructure to bring ROI. This includes clear roles and responsibilities, data integration, and governance.  

The AI Talent Bottleneck

The talent side of this equation is equally challenging. Stanford’s “AI Index Report 2026”  shows that demand for AI talent has increased across all economic sectors, up to 13.2% in the information sector. But the United States looks abroad for AI talent at 1.2 per 10,000 LinkedIn members.

This bottleneck matters for enterprise AI enablement because AI skills gap is now the top barrier to AI integration, ahead of data readiness, infrastructure, and budget. Organizations that can't staff their AI programs with AI/ML engineers in a competitive market are stuck in pilot without ROI. 

The Investment Signal

The scale of AI investment in 2026 makes clear that AI enablement is critical. Gartner projects worldwide AI spending will total $2.7 trillion in 2026, with infrastructure spending alone reaching $1.48 trillion. That spend is only growing. 

McKinsey’s “State of AI in 2026” reports that 60% of respondents expect their organizations to increase their AI spending over the final quarters of 2026 into 2027. Despite the increased investment, the ROI is not materializing for everyone.

Organizations investing in structured enterprise AI enablement programs are pulling ahead; those treating AI as a series of disconnected tools are not. To close that gap, companies need to invest in and expand their AI enablement capabilities.

Why is AI Enablement Important?

It comes down to controlling the technology in ways that serve your organization, as opposed to letting the technology control and guide your strategic decision-making.

"The essential first step is to confirm that your core systems and data are organized to accommodate AI; if the data isn’t accurate and well-structured, all AI will do is produce the wrong answers faster. Any time there is an emerging technology, an explosion of superficial expertise follows; the inherent power of AI increases the magnitude of this risk. Deliver AI to your customers and team members in the way they need it and chart a steady course for growth with the appropriate training and change management."

- Ryan Burns - Economist & Chief Data Officer @ MSH

At the end of the day, well-implemented AI enablement programs help you:

• Enhance your capabilities and competitiveness in a rapidly changing world.
• Solve complex problems and generate novel insights that would otherwise be impossible or impractical.
• Create personalized and engaging experiences for yourself and your customers or stakeholders.
• Improve your efficiency and productivity by automating repetitive or tedious tasks.
• Innovate and experiment with new ideas and solutions to create value and social good.

Why AI Enablement Stalls

An expert contributor has seen AI enablement stall when business strategies and goalposts are unclear. This leads to disjointed capabilities and failed adoption, which show up as:

• Messy or ungoverned business development data leading to fragmented AI inputs and incorrect AI agentic decisions
• Siloed systems where the AI module cannot access the documentation and its available metadata on a Microsoft SharePoint collection
• Unclear ownership, where an ML model needs updating and no one knows who or which team is responsible

AI technologies don’t fix these issues. It’s the people, processes and infrastructure, supporting AI, that do.

How to Achieve AI Enablement

AI enablement is not a one-time event, but a continuous journey that requires commitment, curiosity, and collaboration. When implemented correctly throughout an organization, it can have profound positive effects. Ryan, our internal AI expert whom you heard from earlier, has a great perspective on this:

"The universe of opportunity is expansive and accelerating; today’s benefits will become a small subset of tomorrow’s benefits. The enduring benefit, however, is unlocking the infinite potential of human creativity by providing resources that shorten the time between the creation of new ideas and their implementation."

- Ryan Burns - Economist & Chief Data Officer @ MSH

Here are some steps that your organization can take to make AI enablement a reality:

1. Define Your Goals and Objectives for Using AI: Develop a strategy on what you want to achieve with AI. Conduct a needs assessment or gap analysis; set SMART (specific, measurable, achievable, relevant, and time-bound) goals or objectives for using AI, along with ethical guidelines or principles.
2. Set up governance around AI: Governance supports and standardizes AI strategy alignment, data, technical infrastructure, roles, responsibilities, and operations. With the EU already enforcing traceability and fining for non-compliance, you want to have clear proof of where AI appears in your company-wide products and services.
3. Develop a plan and a roadmap for implementing AI Solutions: Take a structured approach to execute your AI initiatives with milestones. Consider business resources, risk, and metrics to measure progress (KPIs).
4. Get your data ready: ML models rely on quality data for learning, outputting, assigning metadata, and acting. If a term, like customer or email alert, means different things to different teams and systems, how will your AI system process it?
5. Pick a workflow milestone and execute: Based on business feedback and your available resources, choose a milestone from your roadmap to implement. Be sure that your AI prototypes fit that use case in production, instead of getting caught up in scope creep.
6. Monitor and test: Test your solutions in real-world scenarios, and collect feedback. Monitor AI model results for lineage, provenance, reliability, and drift. 
7. Measure and scale: Measure progress based on the KPIs you set up in the planning and roadmap step. Learn from your implementations and iterate to scale.

Throughout your AI enablement journey, become AI literate. You don’t need to be an expert, but you should understand what AI is, how it works, and what it can do for you. 

"The reality of the situation is you have to go in and rip out everything and rethink everything from first principles. That's how powerful this technology is. Most AI is not working right now because people are trying to do these little use cases. MSH can do it. We're doing that right now. We're training our whole team on AI tools, we're building universal skills, and we're rebuilding everything we used to do by data janitorial and admin work into something agentic so we can rethink everything we're doing." - Rashid, Founder & CEO, MSH‍

What This Looks Like In Practice‍

An expert contributor to MSH sums up AI enablement,  when an organization can take the AI model and:

• Reliably feed it appropriate information
• Constrain its authority
• Evaluate its output
• Integrate it into a real workflow
• Improve it through operations on its evidence.

An example from his experience illustrates AI enablement.

The Problem

One of his clients faced fragmented business-development information. They needed a system that could unify this data, correctly flag opportunities requiring follow-up, and retain the reasoning behind the notification.

The Solution

The expert created an internal platform called R-BOSS/CHIP. He was the product owner, systems architect, hands-on developer, and primary operational user. 

The Timeline

• Weeks 1–2: Workflow and decision discovery

Map the sources, decisions, failure modes, existing judgment criteria, and actions that must remain human-controlled.

• Weeks 3–5: Data and integration foundation

Build ingestion workflows, the common opportunity model, normalization rules, source traceability, and exception handling.

• Weeks 6–8: AI-assisted classification and scoring

Introduce AI only where contextual interpretation adds value. Keep eligibility, safety, and other objective constraints in deterministic code.

• Weeks 9–10: Human-review workflow

Create review states, evidence display, override capture, next-action controls, and protections against autonomous external action.

• Weeks 11–12: Operational pilot

Run the system against live work, examine missed or questionable recommendations, measure reviewer behavior, and refine the decision logic.

The Outcome

The business actively used the R-BOSS/CHIP platform in its development operations. 

• Previously fragmented inputs became one governed review queue.
• Every recommendation retained its supporting evidence and rationale.
• One hundred percent of consequential external actions remained subject to human approval.
• Exceptions and uncertain records were surfaced rather than silently forced through the workflow.
• The platform became part of the recurring process for opportunity review, relationship management, outreach planning, and proposal preparation.
• Production feedback was used to improve ingestion, prompts, rules, and scoring over time.
  

MSH builds AI Centers of Excellence (COEs)  to connect AI capabilities to workflows, essential for business operations. An AI COE:

• Identifies where to start, the workflow and AI tooling needed
• Defines ROI metrics
• Assembles the right mix of product, integration, data, AI, and change-management capabilities.
• Builds your first win with a governed pilot, operational adoption, and knowledge transfer.

Examples of AI Enablement

Here are examples of how organizations use AI across industries and what you can learn from them.

Healthcare

AI in healthcare has moved well beyond detection and diagnosis into operational transformation. The use cases with the clearest ROI in 2026:

• Clinical decision support surfaces relevant research and patient history at the point of care, reducing documentation time and improving diagnostic accuracy.
• Predictive staffing models use real-time census data to anticipate patient volume and adjust workforce scheduling before shortages occur
• Automated prior authorization cuts approval turnaround from days to hours, reducing administrative overhead and improving patient experience

Supporting these workflows requires good infrastructure. For example, MSH created an AI COE for a healthcare provider and built a delivery-pipeline engagement. AI implementation consulting cut that organization’s total tooling cost by 50 percent, pulled 2,000 hours of manual deployment work out of the process, and helped the team ship a new feature every week.

Building these capabilities in healthcare organizations by specialized roles combining clinical knowledge with AI/ML engineering are among the hardest to source in any sector.

Financial Services

Financial services is one of the fastest-growing industries for AI investment, accounting for over 20% of global enterprise AI spend. Plus, the ROI profile here is among the clearest of any sector. Key use cases driving adoption:

• Fraud detection: AI-powered transaction monitoring flags anomalous activity in milliseconds, with leading institutions reporting significant reductions in false positives that previously consumed analyst time
• Algorithmic risk assessment: moving credit and underwriting decisions from multi-day processes to near-real-time approvals
• Regulatory compliance automation: continuously monitors transactions, flags potential violations, and generates audit-ready documentation, addressing one of the industry's most resource-intensive obligations

The infrastructure supporting these AI use cases requires engineers with regulatory compliance expertise and enterprise-scale engineering. MSH has provided American Express with highly talented engineers through campus recruitment and administrative services to support high volumes of engineering talent. 

Technology And IT Operations

For technology leaders, AI enablement for business often starts closest to home – IT operations itself. These use cases deliver immediate value within the IT function while building the data infrastructure that broader programs depend on:

• AIOps handles incident detection, correlation, and initial response across complex infrastructure stacks, catching anomalies before they escalate
• AI-assisted code review reduces time senior engineers spend on routine review cycles while improving defect catch rates
• Intelligent infrastructure monitoring uses predictive models to flag capacity constraints and security vulnerabilities before they become incidents

Technology leaders need a strong infrastructure to handle rapid growth and fast changes while covering all these use cases. MSH has demonstrated this capability, partnering with Blackstone to form a strategic team that addressed operational challenges and relieved volume constraints.

Governance and Guardrails

AI enablement requires an AI governance (AIG) framework with appropriate guardrails.

Without governance, AI adoption creates risks that compound with every new use case. With governance built in from the start, your AI enablement programs grow confidently without creating technical debt or regulatory liability.

Plus, AI governance helps you understand and standardize what it is, how it works, and why they’re used in business operations. This knowledge opens business opportunities with vendors or new customers that need a service your AI systems or models provide.

For the security-minded, here are some governance and guardrail components to consider.

AI Governance Policy

An AI governance policy defines the purpose, scope, guiding principles, and outcomes.  It gives your technical and business teams guidance in managing AI design, development, and scaling. 

Usefulness outweighs comprehensiveness, and it should support a practical roadmap. For example, you may focus the AI governance policy on aligning AI with decision stewardship. That could help lower personnel costs by clarifying who owns the security of agentic AI actions.

How AI Filters the Data

ML models stream two kinds of inputs:

•  The data products from the enterprise’s ecosystem, including those from data warehouses to entire documents and knowledge graphs
• The knowledge the ML model gained during training and prior interactions.

The ML model needs to combine both properly. It needs  proper access to enterprise data, and that data needs to be walled off from any external data the organization gathers. The AI tool also needs to know when and how to use its learning so it doesn’t violate privacy rules or become susceptible to bias.

Data Access Controls/Metadata

Companies use metadata in assigning data access controls. This information contains who should access the data and when and where.

With an AI model, you need to consider controlling its data access so it doesn’t violate your customer’s privacy. Also, you need to decide which employees have access to the AI model and for what purposes. Finally, AI needs to produce accurate metadata about its adherence to data privacy rules and reasoning if it updates the data privacy controls metadata. 

Traceability

As of August 2026, the EU is enforcing its traceability requirements specified in its AI Act. Failing to do so will result in a significant fine. To mitigate this risk, an AI governance framework needs way to identify which applications or system components the AI or ML model has touched. 

You need to know where AI has gathered its information, what it did with it, and where its output went next. Knowing where AI was used and what resulted will give you evidence to understand, prevent, and remediate security issues.

Human Oversight

AI Governance requires humans in the loop to decide which use cases are appropriate for what tooling. They need to coordinate AI’s decision-making and understanding with how the organization works and solves problems.

Humans also need to decide who owns the AI and its output, training, design, development, and results. Companies need policies on who has responsibility for AI’s explainability, reproducibility, deployments, transparency, and operations. Stewards need to assess whether the AI model is secure, accurate, ethical, and fair in its replies and activities.

Incident Reporting

An AI governance framework needs to define how to handle  AI security issues.

Maryanne Emomeri notes that AI governance needs to advise organizations on incident reporting for misuse of AI.

Considerations include what appropriate use of an AI tool looks like and when it is out of scope. What constitutes an incident, and how should incidents be prioritized? 

Then, who solves the incident? How will itt be tracked and audited for compliance? How should a successful resolution be validated? Figuring all this out ahead of time will help build trust with your customers regarding your AI implementations.

What AI Enablement Services Look Like

For organizations moving from AI interest to AI execution, understanding what a structured engagement involves helps set realistic expectations. AI enablement services and AI/ML enablement services vary by provider, but the core components of a well-structured program are consistent:

1. AI readiness assessment: Evaluates your data maturity, infrastructure capacity, and organizational readiness before any model gets built. Skipping this is the most common reason pilots don't reach production.
2. Use case identification and prioritization: Identifies which AI opportunities have clear ROI potential, available data, and a realistic path to production. Good AI enablement consulting helps organizations pick two or three high-value use cases over ten speculative ones.
3. Proof of concept and pilot development: Validates the use case with real data before committing to a full build. A well-designed pilot has defined success metrics, a time limit, and a clear decision framework for advancing to production.
4. Production deployment and integration: This is where most pilots stall. Moving from a controlled environment to production requires data pipelines, API integrations, monitoring infrastructure, and security reviews. This is typically where the need for dedicated AI/ML engineering talent becomes most acute.
5. Change management and training: Determines whether people who interact with AI systems actually use them effectively. AI enablement programs across departments succeed or fail based on this component. Role-specific training and clear output review policies matter as much as the technology.
6. Ongoing optimization and governance: Models drift, data changes, and regulatory requirements evolve. Effective AI enablement consulting builds monitoring and governance into the program architecture from day one rather than retrofitting it later.

Build vs. Partner vs. Staff

AI enablement services connect with the organization’s operating model, the  AI Center of Excellence (COE). A COE isn’t a project or department but an alignment across the organization through processes, roles, and governance structure.

Organizations that want to implement AI enablement services need to decide whether to build, hire a  partner, or staff their AI COE. 

• Building an AI COE: Build an AI COE by clearly understanding the business problem and the tooling needed, and having the resources to do so. In the example of AI-enablement, our expert contributor saw a clear problem with business-development information, developed the R-BOSS/CHIP, and served as the product owner, systems architect, hands-on developer, and primary operational user. 
• Partnering with a vendor: If the company has the AI tools and pilots in place but doesn’t know how to connect them to business operations, then partner with a vendor. Tapping into a third-party for AI COE consulting helps you figure out where you are and where you want to go in your AI enablement.
• Staff your AI COE with ML and AI engineers when existing teams don’t have the bandwidth to deliver the AI solutions, or you don’t have the right resources to design, build, or scale the AI platform you need. It’s important to onboard people with the right experience and values to avoid rework or missed requirements.
  

Frequently Asked Questions About AI Enablement

What is AI enablement?

AI enablement is the process of equipping an organization with the tools, talent, data, and governance frameworks needed to deploy AI responsibly and at scale. It builds an organizational capability, covering everything from data infrastructure, model selection, change management, and ongoing governance.

How AI enablement different from digital transformation?

Digital transformation is the broader initiative of modernizing how an organization operates using technology, moving from legacy systems, automating processes, and shifting to cloud infrastructure. 

Why do most AI projects stall before production, and how do you avoid it?

AI projects stall due to a lack of AI enablement, caused by poor business strategies with unclear goalposts. These lead to disjointed capabilities and failed adoption. You avoid this situation by building the infrastructure, including the people and processes, ahead of time.

How long does an AI enablement program take?

It depends on scope and starting point. A focused AI enablement program targeting one or two high-value use cases can move from readiness assessment to production deployment in 3–6 months. Organization-wide AI enablement programs across departments typically take 12–18 months to reach scaled adoption. 

What does an AI enablement consultant do?

An AI enablement consultant helps organizations move from AI ambition to operational AI. The best AI enablement consulting engagements are grounded in your actual business objectives rather than generic AI frameworks.

Should you hire for AI, bring in a partner, or do both?

If you have the AI tools and pilots in place but do not know how to connect them to business operations, then bring in a partner to consult about an AI Center of Excellence (COE). Staff your AI COE with ML and AI engineers when existing teams don’t have the bandwidth to deliver the AI solutions or the skill set to deploy within a deadline. If you face both, then consider both.

How much does AI enablement cost for an enterprise?

The more important cost conversation is around talent: with AI roles commanding 67% salary premiums over traditional software positions and 3.2x demand-to-supply ratios, staffing is often the largest and most variable component of an AI enablement program budget.

What data do I need before starting an AI enablement program?

You need a clear-eyed assessment of what data you actually have, how clean it is, and whether it maps to the use cases you're targeting. Then you need to fix data accessibility problems: data that’s siloed, inconsistently labeled, or locked-in systems that aren't integrated.

Big Takeaway

AI enablement is key to delivering, deploying, and scaling AI solutions your business actually uses in operations. If your AI enablement has stalled or you are wondering how to get started, let an MSH AI COE consultant get you moving.