AI Strategies That Power Enterprise Digital Transformation (+ How Leading Organizations Put Them Into Practice)

You’ve likely seen other businesses turn to AI to speed up their digital transformations, only to find the finish line isn't quite what they expected.

Instead of the smooth operational efficiency promised, they're now facing siloed systems, mounting service backlogs, and clunky, inefficient workflows.

Even tougher to hear is that 95% of enterprise AI pilot programs never actually make it to production. That leaves you dealing with wasted investments and a team that loses confidence in every new change the business introduces.

When you add the daily stress of needing to modernize and deliver more with fewer resources, it’s hard to know where to even start.

The real answer is to build an AI strategy that actually blends with and supports your long-term business objectives, rather than working against them.

What is enterprise digital transformation?

When they hear the term "enterprise digital transformation," many organizations think about digitizing their business operations. But this process is actually more about "orchestration" than anything else.

Digital transformations are all about making sure your technology, people, and processes can work in harmony across every department. 

Getting to this point, though, isn't always straightforward. You might be dealing with legacy systems, sprawling tech stacks, or globally distributed teams. Each of these challenges can make it harder to find the right balance of tools and technology that brings everything together.

Another consideration is that not every digital transformation is the same:

• Traditional transformation usually stops once new tools are in place and processes are digitized — it doesn’t necessarily change or improve how those workflows actually function.
• With AI-powered transformation, the goal is to leverage intelligent systems to create connected, autonomous workflows that are powered by reasoning and orchestration.

Using this approach, AI may become the catalyst that helps position businesses to finally experience the transformation they’ve been planning for years.

Learn how other enterprises are leveraging AI to support their digital transformation strategies.

Why AI is central to modern digital transformation

Digital transformation strategies can look great on paper, but real-world execution isn't always as pretty. System silos, missing access permissions, or manual implementation steps can often create a massive execution gap.

AI is becoming central to closing this gap effectively, helping businesses translate high-level strategy into fast, intelligent, and consistent automated workflows across the org.

Enabling speed, efficiency, and scale

Companies around the world are already using AI to accelerate transformation by automating high-volume tasks that are typically slowed by manual actions or fragmented systems. They’re using advanced AI technologies with powerful reasoning engines to identify and resolve operational bottlenecks in real time.

Unlocking these capabilities in your own organization can allow your business processes to scale efficiently without requiring additional hiring.

Breaking down silos

Important enterprise data can sometimes get trapped in departmental vacuums. AI can help you reduce the risk of silos by unifying information and workflows across IT, HR, finance, and operations. 

Automated cross-system handoffs can help you maintain a single source of truth that spans departments. When there aren’t constant barriers to information, businesses can deliver more consistent, reliable employee experiences regardless of where teams work.

Unlocking enterprise data

AI agents can make it easier for teams to actually use both unstructured and structured data to drive more informed decision-making across the business. 

Instead of manually hunting through disconnected data silos, agents can act as an interface layer across your existing system, helping make hard-to-find information more accessible, searchable, and actionable.

How to use AI to drive enterprise digital transformation

AI is a broad technology that serves many different purposes for businesses, and an implementation strategy built for specific use cases or departments might not translate well to full organizational transformation. Below is a high-level framework for leveraging AI to drive an enterprise digital transformation.

Step One: Assess enterprise readiness

Before you add new AI tools or systems to your business, it’s a good idea to take time to assess your enterprise readiness. Evaluating your data quality, integration maturity, and system accessibility may be a good place to start this process.

Reviewing your business's governance and security standards early in the process can help you identify and address potential roadblocks that may pop up during implementation. Cross-functional sponsors across all critical departments, like IT, HR, finance, and operations, are often critical to keep business segments aligned. 

Taking these initial steps can help you lay a solid foundation for new technology rollouts, one that you can continue to build on long term.

Step Two: Identify high-impact, repeatable workflows

During digital transformations, some of the most beneficial improvements may be the high-volume, time-critical tasks that slow your teams down. When you identify and prioritize the cross-functional workflows that impact multiple teams, you get the opportunity to secure some quick, visible wins up front and build momentum.

Some good starting points could be:

• IT support: Automating password resets or software access requests to free up your help desk and speed up support
• HR service delivery: Streamlining onboarding tasks or benefits administration for a better employee experience
• Finance approvals: Simplifying approval workflows for expense reports, invoice processing, and other financial processes to reduce the risk of errors and delays
• Employee troubleshooting: Providing immediate answers to common questions or issues via a user-friendly, conversational interface

Step Three: Centralize and unify enterprise data

AI tools have the potential to do a lot for your business, but they're also limited by the quality of the datasets you feed them. So it’s important to provide consistent, reliable data to unlock real value from your implementations.

Connectors and integrations play a major role in syncing information across existing systems. When your tools can talk to each other, it becomes easier to unify data across the business environment, which helps your AI solutions take more accurate, reliable actions.

Step Four: Deploy AI agents that can reason and act

While basic generative AI tools and robotic process automation (RPA) are helpful, their limitations can make scaling their capabilities challenging. As your business grows, you may consider deploying intelligent AI agents that can learn and reason based on your unique business data and employee needs.

These more advanced AI systems can help your business orchestrate a variety of multi-step tasks, including:

• Automatically resolving IT tickets by interacting directly with your ITSM and identity management tools
• Handling HR workflows by syncing and referencing data distributed across your HRIS and team collaboration platforms
• Managing cross-departmental requests and approval processes that require permission validation and system access reasoning

Step Five: Embed AI directly into the employee experience

It's important to make your AI tools accessible wherever your employees need them the most. By integrating your new solutions across popular platforms like Slack, Microsoft Teams, company portals, mobile apps, and any other platforms your teams regularly use, you can reduce barriers to adoption.

Seamless AI integrations help minimize friction, so teams can access helpful support or powerful automations no matter where or when they work.

Step Six: Standardize governance, security, and oversight

A blueprint for your security and compliance rules gives you a repeatable approach to scale your AI initiatives over time. Setting clear guardrails and technology usage expectations can help reduce the risk of teams running uncoordinated or fragmented AI experiments that might impact data security or compliance. 

When you create more standardized requirements for your technology, teams can get space to innovate without sacrificing control over data privacy and security.

Key challenges that slow digital transformation

As you begin your digital transformation, you might run into some pitfalls that slow down your progress. Many times, the root cause of these issues is fragmented systems or legacy infrastructure.

Trying to integrate new tools into older systems or workflows without a clear roadmap can often lead to inconsistent results, higher ticket volumes, and operational disruptions. IT teams then become overwhelmed with manual, repetitive work, taking their focus away from the strategic modernization initiatives. 

Even if you launch a successful pilot, teams might find it difficult to scale and optimize it across the business due to:

• Data governance gaps: Without clear security standards and clean data, it gets harder to maintain data security and compliance the more you scale a tool.
• Siloed systems: When departments operate in silos, they often end up with inconsistent processes and disconnected tools that create more problems than solutions.
• AI fragmentation: If multiple teams experiment with separate AI vendors or models, it creates a messy, inconsistent experience for employees and makes unified visibility hard to achieve.

When you understand the main reasons digital transformation projects fail, you can get the context you need to build a more resilient strategy going forward.

Overcoming transformation challenges with AI

Nearly all businesses face challenges with their AI implementations, just like with any other major business shift. But that doesn't mean a successful digital transformation is out of reach. Practical, proven strategies can help you overcome initial roadblocks and unlock your technology's full potential.

Minimize silos through cross-system orchestration

AI solutions can help you address fragmented business systems by acting as a connective tissue that integrates your legacy systems with modern tools. Orchestration layers and advanced operating models facilitate communication across your tech stack, helping you automate workflows more effectively.

By unifying siloed systems, you can create a more optimal environment for AI tools to execute actions seamlessly and autonomously, with minimal need for manual intervention.

Free teams from manual, repetitive work

The more you can offload repetitive tasks from IT, HR, and finance teams, the more time they have to focus on strategic transformation objectives.

Individually, tasks like password resets, basic HR requests, and onboarding/offboarding workflows aren’t particularly demanding. The problem is that they’re nonstop and continuously interrupt more value-adding work.

With an AI-driven approach, you can automate many of these necessary but tedious tasks, benefitting both sides. Operational teams can get help fast, while support teams can finally focus on meaningful work.

Enable scalable transformation with enterprise-grade AI solutions

"Pilot purgatory" happens when AI implementations stall indefinitely before delivering real value to the business. Scalable AI platforms help you pave the way to actual deployment and ROI by unifying your data while providing enterprise-wide governance.

These platforms let you use AI agents with shared templates and processes to enforce more consistency across all your departments and help prevent fragmented adoption.

Real-world use cases that demonstrate AI transformation

Understanding the value of AI is important, but seeing how other businesses have leveraged it to drive their own digital transformations can inform the blueprint your business needs for success.

Below are three real-world examples of AI-driven transformations, along with proven outcomes from businesses like yours.

IT service modernization

Modernizing IT service processes is often the first place businesses focus on during digital transformation, since high ticket volumes and extended wait times affect both IT teams and employees. 

By using chatbots or AI assistants to automatically resolve many common but time-consuming employee requests, you can significantly reduce ticket wait times and improve your mean time to resolution (MTTR).

Unity, a global leader in 3D content creation, is a perfect example of this use case in action. When they shifted to a remote work model, their IT tickets doubled. With an AI assistant, they provided employees with immediate support via Slack, reducing average resolution time from three days to under one minute.

Today, Unity's AI tools handle 30% of all their tech issues autonomously, maintaining a 91% employee satisfaction rate.

HR automation and employee support

Many employees have regular questions relating to things like company policies, employee handbooks, PTO, and payroll. While these questions are easy enough to answer, when hundreds or thousands of employees continuously ask them, it can lead to major backlogs for HR teams.

By bringing AI and machine learning into your HR workflows, you can build support solutions that give your teams the instant answers they need while actually reducing the burden on HR.

AI can assist in a variety of HR processes, including:

• Streamlining onboarding workflows and training modules
• Answering benefits-related questions
• Managing PTO balance inquiries and request form submissions
• Locating and referencing policies for employees as needed

Companies like Ciena, a global leader in networking systems, have already demonstrated this potential. Using an AI assistant. Ciena was able to automate over 100 enterprise use cases and reduce approval times for certain internal requests from three days to 30 minutes.

See how other HR leaders are automating their employee support: Explore real-world examples of AI in action.

Finance and procurement workflow automation

Your business's finance and procurement processes, important as they are, can often slow teams down. Long approval processes and highly manual workflows can create bottlenecks that throttle how quickly your business can grow.

The right AI implementations can help you reduce these slowdowns by automating routine tasks like:

• Approval workflows for employee expenses and purchase orders
• Creating intuitive reports for better spend visibility
• Answering invoice-related questions
• Responding to vendor inquiries

Hearst, the media corporation behind brands like Cosmopolitan and Esquire, is a strong example of how this works in practice. They launched an AI assistant named "Herbie" to help their teams connect multiple financial workflows across an extensive network of systems.

Herbie now answers over 1,200 questions every month and handles 57% of support issues autonomously, saving Hearst tens of thousands of productivity hours.

Implementing AI tools for successful transformation

AI tools can serve as the engine that helps you automate complex workflows and bridge the gaps between disconnected systems. By unlocking siloed data, these systems enable you to deliver a more consistent, high-quality experience for your teams at scale. 

As you evaluate your options, consider AI models that are more advanced than simple chat-based solutions. Agentic reasoning and cross-system orchestration features can help you deliver the measurable, enterprise-wide outcomes your organization needs.

However, while the technology itself is important, how you implement it is equally critical. Below are some best practices to inform your rollout strategy:

• Start with high-impact wins: Targeting repeatable, high-volume workflows in IT or HR, like password resets or basic inquiries, can deliver immediate results. This initial success helps build momentum and lets everyone in the organization see the benefits of AI right away.
• Involve your leaders early: Include IT, HR, and business stakeholders from the very beginning. Their early input and visible buy-in build trust in the solution and support your change management processes.
• Prioritize the ecosystem: Look for vendors that offer strong integrations across your entire technology stack. The better your connections, the easier it is to centralize data and execute AI workflows across systems.
• Focus on change management: Prioritize regular training and clear communication to help your employees feel confident when using new AI tools. When your team understands how these tools support their specific roles, it can help drive long-term adoption.

Accelerate AI digital transformation with Moveworks

If digital transformation always feels just out of reach, AI may be the catalyst you need to finally break down those barriers that are holding your business back. 

But not just any AI tool will do. Real value comes from systems that can reason and act across the enterprise. 

Moveworks enables AI digital transformation by acting as a unified AI layer that connects to all your business systems and channels, so employees can get help and take action from one place. 

• AI Assistant understands intent, plans multi-step workflows, and executes tasks across tools like ITSM, HRIS, ERP, and CRM to reduce manual friction, silos, and boost productivity.
• Agent Studio for building custom automations, so organizations can deploy AI agents at scale and roll out new AI use cases quickly across departments while maintaining governance, trust, and control. 
• Experience Insights surface bottlenecks and measure impact, helping leaders continuously refine their digital transformation strategy and show ROI. 

Moveworks empowers teams to explore how AI can deliver value across the organization, a comprehensive agentic AI solution designed to integrate deeply with your existing tech stack, helping you eliminate the challenges of fragmented systems, service backlogs, and slow, manual workflows.

Ready to finally move your digital transformation forward? Schedule a free demo of Moveworks today.