When Will AI Actually Move the Needle?
Real transformation begins when we stop polishing old workflows and start reimagining them
At first, electricity didn’t change much.
When the first factories swapped steam engines for electric motors in the late 19th century, not much happened to factory output: The machines still sat in rigid rows, clustered around a central shaft. Leather belts still flapped through the air, distributing motion with Victorian inefficiency. The electric motor, more compact and controllable than steam, simply slotted in where the old system left off. Productivity gains? Minimal.
It took decades for manufacturers to realize that electricity was more than a better steam engine. It was an invitation to redesign. Freed from the tyranny of centralized, mechanical power transmission, factories began distributing individual motors, rearranging workstations, and rethinking the flow of production. Only then did output really start to soar through electrification.
The same is happening with AI.
Right now, we are in the same steam-to-electricity phase. We have plugged in something powerful, but we have barely changed the layout. The promises are loud: 10x productivity, zero marginal labor, autonomous organizations. But the results, so far, are quieter. Useful, yes. Transformative? Not yet to the extent advertised. Because transformation requires more than a new engine. It demands a new architecture.
Why AI Isn’t Moving the Needle—Yet?
Despite its astonishing fluency, AI today functions mostly as a throughput enhancer. It accelerates routine tasks: it is summarizing notes, drafting emails, transcribing meetings, surfacing patterns. In doing so, it boosts local efficiency without touching the structural logic of how work gets done. We still operate in organically grown systems optimized for flawed, messy humans: linear workflows, brittle hierarchies, document-driven decisions.
This is not transformation. It is optimization at the margins.
Historical productivity leaps, from mechanization to computation, only materialized when organizations rewired their internal logic. AI, like electricity, is more than a layer you bolt on. It is a capability you build around. And until workflows, tools, and teams are redesigned with AI’s strengths and limitations in mind, most of its promise will remain theoretical.
Why It’s Taking So Long?
The biggest roadblock is not AI itself. It is the environment we are putting it into.
Much of the economy remains under-digitized. Data is scattered, unstructured, or locked in analog formats. Processes are fragmented across departments, vendors, and outdated software. In this landscape, AI integration is less a revolution than a series of one-off experiments: a chatbot here, a summarization tool there, an analytics dashboard no one checks.
Unsurprisingly, digitally mature sectors like software, finance, digital media see the greatest gains. They are building on solid infrastructure. But for most organizations, the terrain is still too rocky. Without clean data, interoperable systems, and digitized workflows, AI is like an electric motor in a factory that still runs on gears and grease.
The Mirage of Short-Term Transformation
In the near term, AI adoption is incremental. It changes how fast you do things, not what you do and definitely not why. It is a co-pilot, not a planner. It does not own tasks, understand responsibility, or anticipate context. Despite media hype, AI is not triggering mass job losses or reshaping industries. Most “AI-driven” layoffs are economic recalibrations draped in tech narrative and aimed to investors and capital markets to signal future readiness in the midst of a hype cycle.
Agentic systems, the dream of autonomous agents that plan, coordinate, and act, at the same time remain largely aspirational. Today’s AI systems still struggle with comprehensive memory, persistence, reliability, and real-world interfacing. They hallucinate a lot, are prone to prompt injection by design and they forget what is relevant. They often give up halfway through a task. In short: they are still interns pretending to be executives.
The Middle Ground: Orchestration, Not Automation
Over the next several years, a shift will begin, not primarily because models leap forward, but because processes will do.
Early signs are emerging: AI-native workflows in content generation, customer interaction, and internal support. Systems that do not just automate tasks, but orchestrate them—chaining outputs across tools, triggering decisions, coordinating human intervention.
In this phase, AI becomes a layer between applications and operators. It helps manage complexity. It knows which agent to ask. It juggles timing and context. And it normalizes a new kind of collaboration: humans as strategic directors, AI as operational enablers.
This is not a universal outcome. Gains will accrue unevenly. Skill asymmetries will grow. Workers who understand how to design, prompt, and evaluate AI will flourish. Others may find their work increasingly fragmented and becoming more invisible.
What a Real AI Transformation Looks Like
If we zoom out to a 7–14 year horizon, the outlines of a deeper transformation come into view.
Here, AI stops being an add-on and becomes infrastructure. Legacy workflows—designed for slow, sequential, document-heavy operations—are replaced by modular, AI-compatible architectures. Organizations will probably begin to function more like interlinked ecosystems consisting of humans collaborating with semi-autonomous agents instead of being like pyramids of stacked human control and oversight.
AI becomes embedded in formal roles: synthetic researchers, planning assistants, compliance monitors. Enterprise software evolves to support multi-agent environments, interoperability, and memory-aware coordination. Human labor does not vanish but it changes its shape. While routine and interpretive tasks will likely decline. Synthesis, edge-case handling, judgment, guidance, architecture, and ethical calibration rise in importance.
The economic effects could be dramatic. Productivity spikes might induce deflation, forcing a rethinking of value capture and pricing. Entire sectors could be reorganized as AI challenges long-standing equilibria in how decisions are made, goods are priced, and knowledge is produced.
But none of this is guaranteed.
The Real Constraints Aren’t Technical
It is tempting to think we only have to wait for GPT-6 or any other quantum leap in model architecture. But the bottlenecks are more on the organizational side:
System readiness: Without interoperable platforms and digitized processes, AI can not plug in meaningfully.
Organizational inertia: Most firms retrofit AI into legacy workflows instead of redesigning from first principles.
Interface limitations: Current interaction paradigms (e.g. chat, API wrappers) restrict AI’s contextual memory and usability.
Trust gaps: Without robust explainability, auditability, and fallback mechanisms, humans have to remain always the decision-makers.
Knowledge fragmentation: AI is only as good as the information it can access. Siloed content limits insight and resilience.
Semantic compression: AI still struggles with nuance, ambiguity, and edge cases that require lived experience or tacit understanding.
These are not model problems. They are design problems. And they demand strategic thinking, not just technical fixes.
Back to the Factory Floor
If electrification has anything to teach us, it is this: powerful technologies will never automatically produce change. They enable it—if, and only if, we rethink the structures they enter.
We are standing at the same threshold with AI.
The models are strong enough. What is missing is the courage and clarity to redesign how we work, decide, and create. AI’s true strength is not to make the old system faster. It is here to make a new system possible. But possibility is not inevitability. It still has to be built.
And that building phase starts with a question every organization should be asking:
If this artificial cognition is now a resource—how would we design around it from scratch?
We are not at the end of the AI story. We are at the beginning of the electrification chapter—that moment where the power source has changed, but the machinery has not yet. The hard part is not deploying AI. It is reimagining the factory with the new possibilities the new paradigm is offering.
Because the real revolution begins when we realize:
We are not here to polish the gears of old machines.
We are here to lay the foundations of something fundamentally new!
The text is almost a manifesto.