The AI Paradox: Why Companies Aren’t Seeing Returns and How to Fix It
Despite a staggering $93 billion invested in AI startups in 2023 alone, a harsh reality is setting in: most companies aren’t reaping the promised rewards. While 78% report using AI, over 80% haven’t seen a tangible impact on their bottom line. This isn’t a failure of the technology itself, but a fundamental misunderstanding of how to deploy it. The key isn’t simply doing things with AI, but fundamentally rethinking how work gets done – a concept mirroring the lessons learned from the early days of digital transformation.
The Bimodal Approach to AI: Optimization and Innovation in Parallel
The rush to integrate AI often resembles applying a new coat of paint to an old house. Companies are using AI to incrementally improve existing processes, rather than reimagining their core business models. This is where the concept of “bimodal IT,” first introduced by Gartner in 2014, offers a powerful framework. Bimodal IT advocates for simultaneously focusing on two distinct modes: Mode 1, focused on stability and predictability, and Mode 2, emphasizing agility and innovation.
Financial institutions provide a compelling example. They’ve successfully blended traditional branch services with cutting-edge online banking, catering to diverse customer preferences. This isn’t a sequential shift, but a parallel investment in both maintaining core operations and exploring new digital frontiers. With AI, this parallel approach is more critical than ever. It’s about running the existing business while building the future.
“Changing the Wings While Flying”: AI’s Two-Pronged Strategy
Over the next two years, many organizations will face the ultimate challenge: “changing the wings while the plane is flying.” This means leveraging AI to free up resources for organizational transformation, which will then drive broader business transformation. To succeed, companies need to define clear AI objectives that extend beyond simple cost-cutting. These fall into four key categories:
- Reduce Bottom-Line Costs: Implement agentic AI solutions to automate tasks, increase efficiency, and optimize throughput.
- Increase Top-Line Revenue: Utilize AI to optimize supply chains, maintain production lines, and streamline processes.
- Improve Satisfaction: Enhance experiences for employees, customers, and partners to foster loyalty and ease of doing business.
- Drive Business Transformation: Create capacity for entirely new business units, service offerings, products, or geographical expansion.
As PwC US chief AI officer Dan Priest notes, top-performing companies aren’t chasing AI use cases; they’re using AI to fulfill their overall business strategy. This requires a shift in mindset – from viewing AI as a standalone project to integrating it as a core component of long-term growth.
Mode 1: Fortifying the Foundation with AI
The first step is to identify your mission-critical operations – the core systems that generate revenue and provide essential value. What absolutely cannot fail? Apply AI to optimize these areas. Can AI eliminate delivery bottlenecks? Resolve customer frustrations? Remove operational constraints? Focusing on these improvements in Mode 1 will yield immediate cost savings, revenue gains, and improved experiences.
Examples of Mode 1 AI Applications
- Predictive Maintenance: Using AI to anticipate equipment failures and schedule maintenance proactively, minimizing downtime.
- Fraud Detection: Leveraging AI algorithms to identify and prevent fraudulent transactions in real-time.
- Automated Customer Support: Deploying AI-powered chatbots to handle routine inquiries and free up human agents for complex issues.
Mode 2: Unleashing Exponential Growth Through Innovation
While Mode 1 delivers incremental improvements, Mode 2 unlocks exponential growth. This is where the freed-up resources – capital, personnel, and capacity – are channeled into new initiatives. Think new product development, geographical expansion, or entirely new service lines. Your strategic vision should drive these Mode 2 initiatives, evolving alongside advancements in AI technology.
Importantly, exponential output doesn’t necessarily require exponential growth in company size. A recent McKinsey report highlighted that a significant number of small and medium-sized enterprises (SMEs) founded in the early 2000s now represent a substantial portion of publicly traded companies valued at $10 billion or more. This demonstrates the power of nimble organizations to accelerate innovation cycles and capitalize on transformation opportunities.
The Human Element: Empowering Employees in the Age of AI
The success of both Mode 1 and Mode 2 hinges on a crucial element: your people. AI-generated efficiencies shouldn’t be viewed solely as opportunities for cost-cutting. Instead, strategically reallocate the time freed up by AI to upskilling, skills mapping, and innovation initiatives. Foster an environment where employees feel empowered to explore AI applications, take calculated risks, and learn from both successes and failures.
We are in the midst of an intelligence revolution, and the organizations that master this dual approach – optimization and innovation – will define the next generation of business. Your AI strategy isn’t about the technology itself; it’s about how AI helps you achieve exponential performance, relevance, and market leadership.
What are your biggest challenges in implementing an effective AI strategy? Share your thoughts in the comments below!
