Nick Heddy serves as President and Chief Commerce Officer (CCO) at Pax8.getty​Every organization, regardless of size or sector, carries three types of legacy debt: technology, process and culture. The instinct for C-suite leaders, especially as agentic AI solutions have entered the market with the promise to immediately transform workflows, is to start by eliminating technology debt. The assumption is that implementing new AI or SaaS solutions will quickly fix broken or fragmented processes. While speed is crucial to keep up with how fast AI and market innovation are evolving, approaching transformation in this way can accelerate complexity rather than eliminate it. ​Transformation Starts Before TechnologyAs my company found that 97% of SMBs plan to either increase or maintain technology spending over the next year, transformation without the right foundation for change may widen the gap between a business’s ambitions and driving real ROI with these automated systems.Leaders believe they’re driving efficiency, but without a clear view of how technology, people and processes actually interact across the organization, AI can amplify the challenges it's supposed to solve. Real transformation requires a different starting point: Leaders must address cultural and process debt first to create the right foundation for AI to deliver meaningful results. This means asking harder questions about readiness for change. Does your team have the mindset to truly evolve, or are they more committed to maintaining the status quo? Are they prepared for the level of disruption required to fundamentally change how work gets done? This translates to culture debt, which is the belief that the value of employees is defined by what they do: the tickets they close, endpoints they manage and the fires they put out. Perhaps that was the right model in the services era, where proximity to the work was the differentiator, but when work can be automated, being close to it stops mattering. Success today means shifting value proposition toward prioritizing client trust and outcome-driven intelligence models—the use of AI to execute work and tie value directly to the results customers care about, not just the activities behind them. My company's research also found that 70% of operational business leaders believe AI will be essential to competitiveness within three years, but only 56% of owners and founders share that urgency. That gap between those driving day-to-day execution and those setting strategy is not just an issue of technology; it’s cultural. If it goes unaddressed, it becomes a barrier to successful AI adoption. At the same time, AI introduces new operational demands. Without clear governance, data discipline and defined ownership, even the most advanced tools will struggle to produce reliable outcomes. AI doesn’t fail because the technology isn’t ready—it fails because the organization isn’t. To navigate this shift, leaders need a more structured approach to change. Moving from mindset to skill set to tool set provides a clear progression for building AI readiness. Executives should map roles, processes and services based on two factors: the value created by human expertise and the value created by AI systems. The sequence is important: Standardize first, automate second and then scale. Tools are a multiplier for success, but only when teams are aligned on objectives and properly upskilled. The mistake I see most often is investing in tools before fixing the processes they're supposed to automate. The Four Zones Of Human And AI CollaborationBusinesses must evaluate four distinct zones. These are the ways humans and automation interact, and each requires a different strategic response. ​1. Human Core: Protect And Invest This category includes client relationships, strategic advisory conversations and the trust built with customers over time. These are areas where outcomes depend on human judgment, empathy and credibility. These actions should remain firmly human-led, supported by technology where appropriate but never replaced. Maintaining trust with customers and stakeholders requires preserving this human core. It's where partner trust, strategic judgment and emotional confidence are critical because employees are ultimately accountable for outcomes, not machines. ​2. Augmented Expert: Build Alongside Your People High-performing employees often spend time on repetitive work like answering common questions, compiling reports or managing administrative workflows. In these cases, AI agents don’t replace the seller or the strategist; they enable them to operate at a much higher level. By offloading repetitive execution, organizations allow their experts to focus on decision-making, strategy and relationship building. The human and the system should be working together, each improving the other. Business leaders should think about their best seller and imagine they walk into every client conversation with real-time demand signals, interpreted and ready to use.3. Legacy Drag: Eliminate Or Redesign Every organization has processes that persist out of habit rather than value. These include outdated workflows, disconnected systems and manual work-arounds that rely on a few individuals to keep things functioning. Automating these processes doesn’t create efficiency; it just makes inefficiency move faster. Before introducing AI, these areas must be redesigned or removed entirely. Otherwise, legacy drag becomes the foundation AI is built on, limiting its impact from the start. ​4. System Core: Automate With Intention This is where AI delivers its greatest operational value: high-volume, low-judgment processes supported by clean data and well-documented workflows. But automation only works when the fundamentals are in place. Leaders must ensure processes are clearly defined and that data is reliable before introducing AI. Automating an undefined or inconsistent process scales the problem rather than solving it.​ Redesign processes first, then automate and replace the old process entirely. ​Organizational Transformation: More Than New ToolsAI transformation is not just a technology shift; it’s an organizational one. It requires a new level of cross-functional collaboration, stronger data discipline and deliberate change management. Speed without structure creates more debt. Organizations risk adding new layers of technology, process and cultural misalignment on top of what already exists. Those that succeed will align leadership and front-line teams, define ownership, establish governance and invest in the foundational changes required to make AI work. They themselves will adopt this new outlook, becoming "customer zero" and leading their own journey through intelligent AI transformation. ​In the end, the goal is to adopt and implement AI more intelligently, so it becomes a lasting competitive advantage and not another layer of complexity.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?