Case 2: The Adoption Curve — Innovare AI Analysis

Module 3 · Video Walkthrough

Module 3 · Quiz Cheat Sheet

As you read — hold this question

Why would a well-implemented AI system cause measured performance to drop in the first six to twelve months?

30 yrs

between the widespread installation of electric motors in US factories (1890s) and the measurable productivity gains. The dip between adoption and value creation is not new.

Factory owners in the 1890s bought electric motors and bolted them where the steam shafts used to be. Productivity barely moved. The productivity gains only came when factories were redesigned from scratch around electric power — flexible floor layouts, decentralised work stations, specialised machinery. The technology had been in place for thirty years before the organisational redesign caught up. Brynjolfsson's J-curve says the same thing about AI: the dip is structural, not incidental. And the companies that survive it don't do so because the AI was better. They survive because they were funded, committed, and willing to redesign the organisation.

Two curves, one story

Quiz: J-curve / S-curve

The S-curve and the J-curve — what each one tells you

The S-curve — technology adoption over time

The S-curve describes how a technology spreads through a market or organisation: slow start (innovators and early adopters), rapid acceleration (early majority), plateau (saturation). Used at the market level, it predicts when a technology goes from niche to standard. AI is currently in the steep acceleration phase of its S-curve — which means the majority of organisations are just entering the adoption phase that early movers completed 2-3 years ago.

The J-curve — value realisation over time

The J-curve describes what happens to performance within an implementing organisation. Value drops first — because learning takes time, workflows are disrupted, and the old way of doing things has been abandoned before the new way is working. Then value climbs, often to levels significantly above the starting point. The gap between the bottom of the J and the new steady state is where most organisations give up, cut the project, and declare AI doesn't work.

The Insight — Two curves, one lesson

The S-curve tells you when to start (answer: earlier than you think). The J-curve tells you what to expect when you do (answer: it gets worse before it gets better). These aren't contradictory — they describe different dimensions of the same adoption challenge. You need the S-curve to justify the investment decision and the J-curve to survive the board meeting six months after go-live.

Inside the dip — what it looks like from inside an organisation

The J-curve is not abstract — it has a specific anatomy

Phase 1
Investment

What happens: Technology is acquired, teams are trained, data is prepared, processes are mapped. Nothing has changed yet for the end user or customer. Costs are running. Returns are zero. The ROI calculation at this point would show a large negative number — which is when internal sceptics get loudest.

Phase 2
Disruption

What happens: The AI goes live. The old workflow is abandoned. The new one isn't working at full efficiency yet — staff are learning, edge cases aren't handled, integrations have gaps. Performance measurably drops. This is the bottom of the J. Companies that pull the project here call it a failure. Companies that survive it look back and call it normal.

Phase 3
Learning

What happens: The model improves as it sees more data. Staff develop fluency. Workarounds get resolved. Processes get redesigned around the new capability rather than bolted onto the old ones. Performance climbs past the pre-AI baseline — and the gap opens between this organisation and those still in Phase 1.

Phase 4
Value

What happens: The investment is earning its ROF. The organisation has capabilities it didn't have before. The platform scales. Data assets compound. The competitive gap widens. This is where the return is measured — but only by organisations that survived the dip.

Applied — three organisations, three positions on the curve

The same technology, different positions in the dip

Organisation	Where they are	What it looks like	The risk
Early mover (2022–23)	Phase 3–4: Learning → Value	Past the dip. Platform is embedded. Data assets are compounding. Teams are fluent. ROI is becoming measurable.	Complacency — the gap only stays open if they keep investing.
Early majority (2024–25)	Phase 2: Disruption	Implementation is live but underperforming. Internal pressure to pull the project. Board asking hard questions. Leadership losing nerve.	Abandoning the project at exactly the wrong moment — the bottom of the dip.
Late adopter (now)	Phase 1: Investment	Starting to build the business case, procure tools, prepare data. Not yet in the dip. Still has the option to learn from early movers' mistakes.	Underestimating the dip — assuming that because the technology is more mature, the organisational disruption will be smaller. It won't.

Take this away

The dip isn't a sign the project is failing. It's a sign it's real. The question is whether the organisation has the runway, the leadership commitment, and the change management to survive it. Most AI failures don't happen because the technology didn't work. They happen in Phase 2.

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Case 3: The 35% Problem →

Quick recall — without looking back

Test yourself on this case

Question 1 of 3

What is the difference between the S-curve and the J-curve — and what does each one help you understand about AI adoption?

S-curve: describes how a technology spreads across a market or organisation over time — slow start, rapid acceleration, plateau at saturation. Tells you when to start adopting (earlier than most organisations do). J-curve: describes what happens to performance within a single implementing organisation — value drops during the disruption phase, then climbs above the starting point as the organisation redesigns itself around the new capability. Tells you what to expect after you've started. Together: the S-curve justifies the investment timing decision; the J-curve prepares leadership for the dip that follows.

Question 2 of 3

The electrification analogy is central to this case. What happened in US factories in the 1890s, and why does it matter for AI?

US factories installed electric motors in the 1890s but bolted them where steam shafts used to be — the new technology onto old organisational structures. Measurable productivity gains took roughly 30 years to materialise, because the gains only came when factories were redesigned from scratch around electricity: flexible floor layouts, decentralised workstations, specialised machinery. The parallel for AI is direct: organisations that deploy AI into existing workflows without redesigning those workflows around AI capability will see minimal gains. The technology enables the redesign — it doesn't cause it. Brynjolfsson uses this analogy to explain why AI hasn't yet produced the economy-wide productivity gains many expected.

Question 3 of 3

Phase 2 of the J-curve is described as the highest-risk moment for AI projects. Why — and what does leadership need to do differently to survive it?

Phase 2 (Disruption) is when the old workflow has been abandoned but the new one isn't yet performing at full efficiency. Measured performance drops — which is exactly when internal sceptics point to the numbers and argue for cancelling the project. This is the "dip" where most AI failures happen — not because the technology was wrong, but because the organisation ran out of patience. Leadership needs to: (1) set expectations in advance that performance will drop before it rises, (2) ensure there is enough financial runway and political commitment to get through Phase 2, (3) focus on Phase 3 indicators (model improvement, staff fluency, process redesign) rather than Phase 2 output metrics, and (4) resist the pressure to declare the project a failure at the bottom of the curve.

Sources

Brynjolfsson: Brynjolfsson, E. & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. Norton.
Brynjolfsson (2022): Brynjolfsson, E., Li, D. & Raymond, L.R. (2023). Generative AI at work. NBER Working Paper 31161.
David (1990): David, P.A. (1990). The dynamo and the computer: an historical perspective on the modern productivity paradox. American Economic Review, 80(2), 355–361. (The electrification productivity paradox.)
Module transcript: BUSN9049 Module 3 Part 1 — Strategic Implications of AI. Flinders University, 2026.