Why Companies Struggle to Realize Value from AI and Automation
Despite the hype, many organizations fail to generate meaningful ROI from AI and automation. Here’s why—and how to shift from tactical tools to transformative results.
AI and Automation: Promises vs. Practical Outcomes
Artificial Intelligence (AI) and automation continue to dominate headlines as must-have technologies. But for many companies, the promised benefits—cost savings, productivity gains, better decision-making—remain elusive.
Why? Too often, AI is deployed in isolated, low-impact ways that don’t move the needle. To succeed, organizations must shift from experimenting with tools to strategically transforming how work gets done.
The Productivity Trap
It’s common to start small: automate document summaries, draft emails, write code snippets. While these save time for individuals, they rarely deliver meaningful enterprise-level value.
This mirrors early experiences with Robotic Process Automation (RPA), where companies automated steps within a workflow instead of redesigning the full process. The result? Minimal gains, stalled progress, and growing skepticism.
Scaling AI: Complex and Costly Without a Plan
Scaling AI from pilot to production is rarely straightforward. It requires:- Robust, integrated data infrastructure
– Ongoing model training and governance
– Seamless integration with core systems
– Strong change management and user adoption
When early efforts yield only marginal improvements, leaders hesitate to invest further. The real challenge is not the technology—it’s the absence of a clear strategy and ownership.
From Automating Tasks to Transforming Processes
To unlock real value, organizations need to think bigger. That means reimagining entire business processes—like customer onboarding, supply chain planning, or financial reporting—and embedding AI as part of a broader transformation strategy.
Key enablers include:
– Intelligent automation (e.g., RPA with workflow orchestration)
– IoT and real-time data to enhance decision-making
– Predictive analytics and AI-driven insights
– Scalable, cloud-native infrastructure
When integrated effectively, these technologies don’t just make tasks faster—they make processes smarter, more scalable, and more resilient.
Why End-to-End Transformation Is So Difficult
Reimagining full business processes sounds compelling—but executing it is another story.
End-to-end transformation cuts across departments, functions, and sometimes geographies. It doesn’t just change how tasks are performed; it redefines who owns them, how they’re measured, and what success looks like. This introduces a level of complexity that’s organizational, not just technical. This is where Design Thinking can assist. By bringing process owners, functional managers and market leaders together, and applying the design thinking process, we can generate a very different future state. A future operating model that takes full advantage of AI possibilities and scale. By contemplating AI capabilities in concert with the design process, we can achieve the types of market and productivity gains that capture attention.
The Pain/Gain Curve of AI Adoption
Most organizations begin at the bottom left of the transformation curve:
– Low pain, low gain efforts focused on individual productivity (e.g., email summarization, meeting notes)
– As automation expands to team workflows, both effort and value increase
– The highest returns come in the high pain, high gain zone—where AI reshapes processes that span departments and functions
Climbing this curve requires more than technical skill—it demands strong alignment, disciplined execution, and change leadership.
Case in Point: Financial Reporting Reimagined
Instead of automating individual tasks like report formatting or journal entries, finance teams can rethink the full reporting cycle:
1. Real-time data pipelines eliminate manual data collection
2. AI-assisted variance analysis surfaces anomalies instantly
3. Dynamic dashboards provide decision-ready insights
4. Narrative generation drafts executive commentary in minutes
The result: faster close cycles, better insight, better cash management, and stronger financial governance—demonstrating what’s possible when AI is applied at the process level.
Final Thought: Aim Higher with AI
AI and automation hold real potential—but only when they’re part of a broader strategy focused on how work flows across the organization.
The question isn’t just “what can we automate?”—it’s “how can we rethink the way this process delivers value to our customers and our organization?”
The organizations that win won’t be the ones with the most pilots. They’ll be the ones that tackle the real problems —the hard to achieve, high-gain opportunities that redefine how the business runs
