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-enabled 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: 72% of enterprises now have at least one AI workload in production, but most organizations are still struggling to move from isolated wins to enterprise-wide returns 
• 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 were asking "should we use AI?" In 2026, that question is settled. So, the real question is actually focused on whether your organization is structured to get value from it (spoiler alert: most aren't yet).

From Pilots To Production: The New Dividing Line

72% of enterprises now have at least one AI workload in production, up from 55% in 2024. But production deployment and scaled value are two different things. The dominant enterprise AI pattern in 2026 is a small number of high-performing use cases surrounded by a larger number of stalled pilots. 42% of companies abandoned most AI initiatives last year, not because the technology failed, but because the organizational infrastructure wasn't in place to support them.

The shift from experimental AI to operational AI requires more than better models. It requires RAG (Retrieval-Augmented Generation) architectures that connect AI to your proprietary data, agentic AI systems that can take action across workflows, and governance frameworks that give leadership visibility and control. 

Nearly all executives (97%) say their company deployed AI agents in the past year, yet only 29% see significant organizational ROI. The gap between deployment and value is where enterprise AI enablement programs live.

The AI Talent Bottleneck

The talent side of this equation is equally challenging. AI talent demand now exceeds supply by a 3.2 to 1 ratio globally, with over 1.6 million open AI roles and only 518,000 qualified candidates available. Financial services and healthcare organizations are waiting 6 to 7 months to fill a single AI role. The roles in highest demand like MLOps engineers, LLM specialists, and AI governance practitioners, barely existed as defined job categories 30 months ago.

This bottleneck matters for enterprise AI enablement because the 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 are the ones stuck in pilot. MSH's AI/ML engineer staffing and recruitment expertise directly address this constraint.

The Investment Signal

The scale of AI investment in 2026 makes clear that this is not a passing trend. Gartner projects worldwide AI spending will total $2.5 trillion in 2026, with infrastructure spending alone reaching $1.36 trillion. At the enterprise application level, the average company's AI spend hit approximately $7M in 2025 and is projected to jump 65% to $11.6M in 2026. 

The organizations investing in structured enterprise AI enablement programs are pulling ahead; those treating AI as a series of disconnected tools are not.

What is AI Enablement?

AI enablement is the process of equipping individuals or organizations with the tools, skills, and data needed to use AI effectively and responsibly. It combines proprietary data and custom tools to accelerate workflows, enhance decisions, and unlock human creativity at scale.

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

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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.

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 who you heard from earlier, has 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

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

1. Educate Yourself on the Fundamentals of AI

You don’t need to be an expert, but you should understand what AI is, how it works, and what it can do. 

You can start by reading some introductory books or articles on AI, taking some online courses or workshops on AI, or attending some events or webinars on AI.

2. Explore the Applications of AI in Your Domain or Industry

You should have a clear idea of how AI can benefit your field or sector, current trends and developments in AI, and best practices and standards for using AI. 

You can do this by researching some case studies or examples of how AI is used in your domain or industry, talking to experts or practitioners with AI experience, or joining some communities or networks focusing on AI.

3. Define Your Goals and Objectives for Using AI 

You should have a clear vision of what you want to achieve with AI, the specific problems or opportunities you want to address with AI, and the expected outcomes or impacts of using AI. You should also consider the ethical implications of using AI, such as how it will affect your values, principles, and responsibilities. 

You can do this by conducting some needs assessment or gap analysis, setting some SMART (specific, measurable, achievable, relevant, and time-bound) goals or objectives for using AI, or creating ethical guidelines or principles for using AI.

4. Develop a Plan and a Roadmap for Implementing AI Solutions

You should have a realistic plan for executing your AI initiatives, what resources and requirements you will need for using AI, and what milestones and indicators you will use to measure your progress and success. 

You should also consider the risks and challenges you may face when using AI, such as data quality, security, privacy, bias, explainability, accountability, etc. 

To prepare for implementing AI solutions, start by creating prototypes or pilots, then determine the data sources and tools you'll utilize, and establish metrics for evaluating their performance. 

This structured approach will ensure a successful AI deployment.

5. Execute Your Plan and Monitor Your Results

You should implement your plan according to your roadmap, test your solutions in real-world scenarios, and collect feedback from yourself or your users. 

You should also monitor your results regularly, analyze your data, and evaluate your performance, impact, and ethics. You should also learn from your mistakes, celebrate your achievements, and share your learnings with others. 

You can do this by deploying your solutions in production environments, conducting user testing or surveys, generating reports or dashboards, or communicating your results with stakeholders.

Examples of AI Enablement

To inspire you, here are some examples of how AI is used in various domains and industries, and how 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

For healthcare organizations building these capabilities, 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

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

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 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.

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's the difference between having AI tools available and actually building an organizational capability that compounds over time, covering everything from data infrastructure and model selection to change management and ongoing governance.

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 is the difference between AI enablement and 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. AI enablement is a specific discipline within digital transformation focused on building the capability to deploy, govern, and scale AI systems. You can digitally transform without AI; you can't effectively enable AI without some level of digital foundation in place first.

What does an AI enablement consultant do?

An AI enablement consultant helps organizations move from AI ambition to operational AI. In practice, that means assessing your current data and infrastructure readiness, identifying which use cases have the clearest ROI path, designing pilots with defined success criteria, and building the governance and talent structure needed to support production deployment. The best AI enablement consulting engagements are grounded in your actual business objectives rather than generic AI frameworks.

How much does AI enablement cost for an enterprise?

The average enterprise AI spend reached approximately $7M in 2025 and is projected to reach $11.6M in 2026, but that figure spans infrastructure, tooling, and talent. A focused AI enablement engagement scoped around two or three use cases is typically a fraction of that. 

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 don't need perfect data, but you do need honest data. The most important starting point is 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. 

Most organizations discover during this assessment that data accessibility is a bigger constraint than data existence: the data is there, but it's siloed, inconsistently labeled, or locked in systems that aren't integrated. Addressing that infrastructure before building models is what separates programs that reach production from those that don't.

Big Takeaway

AI enablement is a key skill for the 21st century that can help your organization succeed. By following the tips and best practices we shared in this blog post, you can become an AI-enabled organization that can use AI effectively and responsibly. 

Or, enable you to properly partner with a trusted advisor.

We hope this blog post has inspired you to start or continue your AI journey. If you're looking for someone to partner with to build out your AI strategy, learn more about our technology consulting solutions.