AI Is Our Electricity Moment: Why Productivity Follows Process
Ariticle Summary/Thesis
Every transformative technology arrives twice. First as hardware and headlines—motors, wires, GPUs, data centers—then, years later, as redesigned work that finally shows up in the productivity numbers. Electricity followed that arc. The earliest adopters simply swapped steam engines for electric motors and wondered why little changed. The real step-up came only after managers re-laid out factories, moved machines closer to tasks, rewrote roles, and cut the number of handoffs. Output rose, lead times shrank, and the accounting caught up.
Skeptics calling today’s build-out a bubble miss this rhythm. Electricity had its speculative booms, too—the 1880s and the late 1920s—yet the grid survived the hype cycle and powered a century of growth. The same will be true here.
There’s also a scale story worth stating plainly. Electrification required years of persistent, utility-grade capital spending, especially on the grid. AI is triggering a similarly macro-relevant wave—turning over compute, re-architecting data pipelines, and, inevitably, pulling more investment into power and connectivity. That capex is not a detour from productivity, however, it is the on ramp.
This article makes the case for treating AI as our electricity moment. We show why the payoff lags the purchase, how to manage the J-curve of investment before returns, and what it looks like to redesign core workflows end-to-end rather than bolt new tools onto old processes. Most importantly, we outline the metrics leaders should track—cycle time, first-pass yield, rework, cost per outcome—so the benefits become visible in the business and, over time, in the broader economy.
If electricity taught us anything, it’s this: you don’t get productivity from buying motors; you get it from moving the machines. AI will change everything, but only after we change how everything is done.
The Buildout
The first wave of electrification disappointed many observers. Firms dutifully replaced steam engines with electric motors but left the rest of the factory unchanged: the same line-shafts, the same belt-driven machines, the same rigid workflow. Accounting records showed only modest gains, and some concluded electricity had been oversold. The big unlock came later, when managers re-imagined how work should flow in an electrified plant—smaller motors at each station, new floor plans, different sequencing of tasks, and new roles for workers and supervisors. Once the process changed, measured productivity surged. That sequence—technology first, productivity second—captures the moment we are entering with artificial intelligence.
AI today is where electricity was when factories were merely “wired.” We are building out new infrastructure, evolving models, negotiating access to private clouds, and piloting copilots in individual tools. Those investments are necessary; they are the wires and motors of our era. But they are not sufficient. The real economic impact appears when we redesign the work around what the technology is actually good at. For electricity, the advantage wasn’t just cheaper energy; it was the freedom to decouple machines from a single power shaft and rearrange production into faster, safer, more flexible cells. For AI, the advantage is not merely faster autocomplete; it is the ability to decompose a process into judgment calls and pattern-recognition steps, assign the latter to AI systems, and orchestrate the whole flow so humans spend their scarce time on supervision, exceptions, and decisions that carry real risk.
This is why measured productivity lags the hype cycle. In the early phase, an organization’s costs go up: it buys compute, licenses models, builds data pipelines, and trains people. Meanwhile, workflows remain mostly intact and metrics barely move. Leaders who do not anticipate this “J-curve” mistake the lag for failure and pull back precisely when they should be redesigning the process. The correct reading is that intangible complements—data quality, governance, workflow engineering, metrics, and change management—are still being built. Once those complements are in place, the same headcount and capital produce more output, quality improves, and lead times shrink. Accounting finally sees what the technology always promised.
The practical question is how to cross that gap. The answer, again, is process. Start by choosing one end-to-end workflow, not a scattered set of tasks. If a customer-support operation touches intake, triage, research, drafting, approval, and follow-up, the goal is to refactor that chain so more happens in a single pass with fewer handoffs. In an agentic (outcome based process) AI can summarize context, propose a resolution, check policy, and draft a response; humans approve exceptions and improve the system through feedback. In software, the target is not a faster editor; it is a delivery system where AI proposes designs, generates scaffolds, writes tests first, surfaces known failure modes, and documents as code changes—so the unit of work is a deployable change with fewer defects, not a line of code typed more quickly. In finance or back-office workflows, the aim is to compress collect-check-enter-reconcile into one validated pass, with AI enforcing rules and flagging anomalies while humans adjudicate edge cases. The common thread is fewer queues, fewer re-entries, and fewer opportunities for rework.
Measurement must evolve alongside the redesign. Traditional input metrics—hours logged, tickets touched, lines of code—are poor guides to AI-enabled productivity. What matters are factory-style outputs: cycle time from request to resolution, first-pass yield, rework rate, queue length and abandonment, service-level attainment, cost per successful outcome. These reveal whether the new process is actually better. They also create the feedback loop that AI needs: without consistent targets and labeled outcomes, models cannot be tuned to the business’s real goals. Put differently, if electricity rewarded plants that measured throughput and uptime, AI will reward firms that define success precisely and instrument the path to get there.
Leadership’s role in this transition is to name the J-curve upfront and budget for the complements that bend it upward. That means funding data engineering and quality assurance, clarifying governance and risk boundaries, and investing in role training so people know when to trust the system and when to override it. It means staging the rollout so lessons from one lane can be copied to the next, rather than running dozens of disconnected pilots. And it means aligning incentives: managers should be recognized for retiring steps and handoffs, not just for adding AI to the old way of working. The winners in an electricity era were not the firms that bought the largest motors; they were the firms that moved the machines and rewrote the playbook.
It is also important to recognize that AI, like electricity, will produce gains that national accounts undercount. Some benefits arrive as quality and convenience—answers on demand, fewer errors caught upstream, documentation that appears automatically—rather than as priced transactions. That does not make the gains any less real. In practice, firms will see them as better customer satisfaction, lower churn, faster onboarding, fewer compliance misses, and more resilient operations under stress. Over time, these non-price improvements accumulate into market share and profitability; at scale, they show up as productivity growth.
Skeptics often reach for transportation analogies—railroads, highways, ports—to explain technology’s impact through market access and spatial reallocation. Those analogies are valuable for thinking about diffusion and who benefits where. But if your goal is to understand a broad step-up in within-firm productivity, electricity is the cleaner comparison. Both require complementary investments inside the enterprise; both deliver their biggest gains after process redesign; both produce an early period where spending is visible and measured output is not. The lesson from history is not that electricity changed everything overnight, but that it changed everything once organizations learned to work differently.
This perspective reframes how we should evaluate AI programs. A deployment that merely adds a copilot beside every worker is a capital project with an unclear return. A deployment that eliminates handoffs, compresses cycle time, and raises first-pass yield across an entire workflow is a new operating model. The former will be easy to start and easy to cut. The latter will be harder to start—and it will be the thing competitors cannot quickly copy once it works. Boards and executives should ask to see redesigned flows, not just model invoices; outcome metrics, not just usage statistics; training plans for new roles, not just lists of new tools.
The arc from electricity to productivity was not linear, but it was predictable once you saw the pattern: wire the building, move the machines, measure the throughput. The arc from AI to productivity will follow the same path: provision the compute, redesign the work, measure the outcomes. If we accept that order of operations, we can set expectations realistically, invest in the right complements, and steer through the J-curve with confidence. AI will change everything, but only after we change how everything is done.