LeadingAgile

Discover the role of organizational design in AI readiness and adoption.

In a high-cost-of-capital environment, traditional PE playbooks aren’t enough. When money was cheap, you could generate exceptional returns by using leverage, cutting costs, and making incremental improvements. Today, relying on rip-and-replace tech migrations and/or doubling down on products that work without reinvention puts you at risk of getting left behind. Instead, success now depends on identifying untapped product and technology value and performing strategic implementation to capture them. This means PE portfolio companies have to?fundamentally improve their operational, technical, and strategic systems by doing things like: ?? Tightening technology and operational integration ?? Reducing technology and enterprise debt ?? Building differentiated software and AI capabilities ?? Owning more of the vertical value chain ?? Fostering team capabilities to take advantage of new technology and products At LeadingAgile, we have a reliable and repeatable change management approach that provides PE firms and the companies in their portfolio with the clarity they need before the deal, and a predictable roadmap post-close. Our goal is to help you minimize surprises as you integrate, allowing you to focus on strategic multipliers rather than reacting to unforeseen challenges that lurk in your investment hypothesis.

Digital transformations fail because they're largely viewed as a technology problem when they're actually more of an organizational design and dependency problem.

Why do so many modernization efforts stall, run over budget, or fail to deliver meaningful impact even when executives are pouring millions into new architectures, refactored code, and platform migrations every year? If you're trying to modernize but your business is still slow, costs are still high, and innovation is still out of reach: maybe you've been thinking about modernization too narrowly. Modernization isn’t just about buying a tool, hiring a vendor, or making discreet technology improvements alone. It's about removing the organizational impediments that are standing in the way of your technology becoming the competitive differentiator you want it to be. In fact, most modernization failures can be traced back to deeper organizational issues like: ?? Siloed Teams Technology and business teams work in isolation, leading to misalignment and wasted effort. ?? Deeply Embedded Dependencies Technology, systems, funding models, approval networks, processes, and teams, are so intertwined that change becomes a slow, high-risk endeavor. ?? Conflicting Incentives Misaligned incentive structures that cause local optimization, creating built-in tension within your organization. ?? Legacy Governance and Funding Models Annual budgeting cycles, centralized control, and heavy compliance processes kill agility. Modernization isn’t a project—it’s a structural shift. It requires aligning the organization around business outcomes, reducing dependencies, and creating an environment where modernization isn’t an initiative, it’s just how work happens.

For organizations that prioritize risk management, business continuity, and cost efficiency, Product Extraction is an essential strategy that allows you to reap the benefits of technology modernization earlier, without bringing operations to a standstill.

If you want systematic, long-term, meaningful changes made to your operating model, you have to remove the things that are getting in the way.

The world is waking up to the power of AI and more and more executives are looking to automate their business, cut costs, and improve customer satisfaction using this critical technology. But, they can't afford to get it wrong. AI-native startups are eating up market share every day and key stakeholders demand results now, not in 5 or 10 years. Too often, companies can't get out of the experimental phase of AI adoption, let alone use it to usher their business into the future. If your AI initiatives are stalling, you probably don't just have technology problems, you likely have an organizational design problem too. Ask yourself, is my organization designed for the adaptability that AI requires to be successful? Or does it look like this: ?? Siloed teams and poor incentives ?? Rigid governance that slows decision-making ?? Misalignment of the business and technology ?? Redundant processes ?? Bureaucratic bottlenecks ?? Legacy architectures that reinforce outdated ways of working. ?? Low-trust Data ?? Sensitive Data ?? Lack of Data Governance If this looks familiar, do you really think you're built for AI? Rather than trying to build AI capabilities on top of broken systems, companies need to?address the impediments standing in the way of adaptability and AI Readiness. That means reducing dependencies, improving alignment between business and technology, rethinking your teaming strategies, and creating a flow-based governance model that emphasizes value delivery and enables you to track metrics you can use to impact the things you care about. That doesn't mean you stop moving toward AI until it's all fixed. The reality for many executives and business owners is that there’s never an ideal moment to fully address every weakness. So, instead of pausing your AI efforts, the solution lies in tackling these systemic impediments incrementally while continuing to drive your AI initiatives forward.

Transformation isn't exclusively about training and methodologies. It's about enabling rapid delivery of business value.

True business agility requires encapsulation throughout the organization, at every level. The idea is that you want autonomous entities that can pivot quickly while working together seamlessly to bring value to your customers.

Transformation requires meeting people where they are, helping them see the vision, and showing them how to safely get there.