AI doesn’t fix organizational silos, but with the right approach, it reveals them while augmenting user skill sets to drive better, faster business results.
There’s been a shift in attitude among CEOs and executives over the past couple of years. Top leaders are now convinced that AI can help their people drive enterprise transformation. The next challenge is realizing that potential. Project success will depend not so much on the technology itself but on the organization’s readiness, commitment to shared governance, and alignment across business functions.
It also requires experienced people who understand the business, the technology, and the processes connecting them. AI can accelerate transformation, but that expertise is what helps organizations determine what should change, how it should work, and whether it’s delivering the intended outcome.
A survey cited by HR Magazine last year sheds light on a possible gap between c-suite enthusiasm for AI and organizational readiness when it’s time to execute the AI strategy. The article notes that while 91% of business leaders say they’re embracing AI, less than half of surveyed HR, payroll, and finance professionals felt fully ready to deploy the technology across critical business functions.
Here’s a closer look at some of the real-world readiness and governance challenges organizations face during AI deployment projects. It includes an overview of how AI reveals internal data silos and process misalignment, as well as practical tips drawn from current use cases that show how leaders can leverage the technology to break down barriers and accelerate enterprise transformation.
How Alignment and Governance Issues Can Stall AI Projects
Beyond the AI hype, many organizations currently engaged in AI-driven transformation now meet the foundational requirement for project success: They’ve clearly defined what outcomes they want to achieve with AI, and the company’s top leadership team is on board. However, deployment projects can still stall at the next level when it’s time to execute without proper alignment across departments and sound governance.
Here’s a real-world example that illustrates how misalignment can negatively affect an AI deployment project: a retailer with approximately 30,000 employees was deploying AI to automate and streamline core functions. HR leaders had defined the desired outcomes and secured buy-in at the top.
So far, so good, but the challenges occurred at the execution phase when the IT team was belatedly brought into the loop and identified serious planning gaps. The HR team used a homegrown system-built decades before and maintained by IT. It included a 401(k)-integration created for a specific vendor that operated behind the scenes, but because the IT team hadn’t been included during the initial planning phase, that integration instance hadn’t been addressed.
The planning gap delayed project completion and highlighted the need to bring all relevant parties to the table earlier in the process. Specifically, early-stage planning should include tasks like assigning people to take ownership of relevant data as well as agreement on governance standards. Data doesn’t need to be perfect to move forward, but it’s critical to ensure that all relevant data is addressed and that all parties responsible for it are on board.
This is also where experienced technology and process expertise continues to matter. AI can help teams identify dependencies and surface issues faster, but people who understand the systems, processes, and business context are still needed to determine why those dependencies exist, what should carry forward, and what needs to change.
How Alignment and Shared Governance Can Drive Efficiency
On the opposite side of the coin, organizations that clearly define desired outcomes, secure executive buy-in, and align relevant teams around shared governance standards can generate efficiency wins quickly. For example, as part of a recent AI deployment project, relevant stakeholders were assigned roles to promote accountability.
One of their objectives was to simplify and standardize change management processes, but because relevant parties were in on planning from day one, the team took a deliberative approach rather than reflexively seeking to automate every single change management function.
Early in the planning process, the team discovered there were five separate processes to initiate a change management action. Instead of simply using AI to automate the existing process, the team first evaluated why those separate processes existed and whether they were still necessary. They ultimately consolidated the five processes into one using dynamic AI controls, increasing efficiency by 70% while providing more consistent outcomes.
This demonstrates how a team that is aligned across multiple functions and has insight into operations at the user level can engage in process redesign at the outset and leverage AI to drive efficiencies. Multiplied across complex functions like change management, those efficiencies can add up to transformative time savings.
How AI Improves Internal and Customer-Facing Processes
Examples from that same AI deployment show how an aligned team with clear goals and shared governance standards can improve both internal and customer-facing functions. On the internal side, that same organization used AI to connect data stored in disparate systems that were used to track project investments and performance.
With information connected across functional units, the company was able to provide a dashboard that enables the board to access a big-picture view of how investments are applied and how projects perform. And leveraging that information, AI can take the analysis a step further by identifying patterns and surfacing recommendations that help leaders understand where there may be opportunities to improve performance, rather than simply providing a snapshot of what has already happened.
Customer-facing processes are also getting a boost from AI, including project workflows. With automated tools to help implementation teams map data into new formats, team members can spend less time laboriously loading data into spreadsheets, doing conversions, and fixing errors and more time applying their expertise to process design, solving complex business problems, managing exceptions, and helping customers make the right decisions for their organization.
The same is true for the project scoping processes. AI accesses all the data generated by the sales and account management teams, including information from Zoom calls to create an initial project scope and pricing inputs. This reduces manual effort and the risk of missing important requirements across customer conversations. From there, experienced teams can validate the scope, challenge assumptions, identify dependencies, and ensure the proposed approach reflects what the customer actually needs; AI simplifies the process, while people remain accountable for getting the solution right.
One key point to note: The company that is realizing value from its AI implementation created an AI council to provide a governance framework for the deployment. By bringing stakeholders together and assigning roles to ensure accountability, the organization established clear ownership not only for how AI is used, but for reviewing its outputs and making decisions based on them.
How AI Improves Enterprise Transformation by Augmenting People’s Skills
As these examples illustrate, an AI-driven enterprise transformation project that begins with clear objectives, strong executive backing, designated roles for relevant stakeholders, and a commitment to shared governance can quickly generate value. It can significantly streamline internal and customer-facing processes while freeing humans from laborious processes, so they can focus on more strategic work.
But that’s only the beginning. Once automation can handle behind-the-scenes work involved in accessing, organizing, and applying relevant data, AI can expand the capabilities of individual team members too. This can begin to bridge the gap between top leadership expectations about AI deployments and business process owners’ readiness to move forward.
In one real-world example, an enterprise transformation project team included people from HR, which owned human capital processes, IT, which owned technology components, and finance, which owned the finance data. AI is starting to break down the traditional divides between those team members and technology.
What that means in practice is that an HR expert who isn’t a technologist can now leverage AI to accomplish some tasks that previously required more direct IT involvement. That doesn’t eliminate the need for technical expertise. Instead, it allows business and technology teams to contribute differently, with process owners bringing business context and IT providing the architecture, security, integration, and governance expertise needed to ensure solutions work within the broader enterprise environment.
In this example, AI didn’t fix organizational silos, but it exposed them and gave the relevant team members better tools to address them. The technology accelerated what people could see and accomplish; their expertise, judgment, and shared accountability determined what happened next. That combination can significantly accelerate the pace of business process improvement and is already changing what enterprise transformation can look like.
