When Will Technology Actually Matter? The Two Clocks of Industrial Innovation

ReadIndustry InsightsStats & TrendsInfographic
Written By Jeff Winter

I started with what seemed like a simple question: when will each technology have a meaningful impact on industrial operations? It turns out that is a terrible question. Or at least an incomplete one.

The World Economic Forum’s Intelligent Industrial Operations Outlook 2026 evaluates technologies across NOW, NEAR and NEXT horizons. That is useful, but impact does not move along only one axis. There is the external timeline, meaning when a technology becomes mature, affordable and useful. Then there is the internal sequence, meaning what a company must already have in place before that technology can accomplish much of anything.

Those two clocks rarely agree.

Anyone who has connected through O’Hare understands this problem. Your second flight may technically be ready to leave. That information becomes less comforting when your first flight is still circling somewhere over Indiana.

A technology can be ready before a company is

Agentic AI could become commercially viable across industrial operations within three years. That does not mean every manufacturer is three years from benefiting from it. If operational data is fragmented, equipment is poorly connected, processes change by shift and nobody agrees which system contains the truth, the prerequisite flight has not landed. The agent may be ready. The organization is still over Indiana.

This is what makes forecasting difficult and planning even worse. Companies must prepare for technologies whose timing keeps changing, while completing earlier work that often takes longer than expected. Meanwhile, budgets are annual, equipment may remain in service for 20 years and the executive who approved the original programme has possibly left to “spend more time with family,” which is corporate language for something that could mean almost anything.

The report acknowledges this sequencing problem, and I strongly agree with it. Accenture found that only 25% of surveyed companies had begun moving toward supply-chain autonomy, with median autonomy maturity sitting at just 16%. The technology conversation is moving considerably faster than the operational reality. A company can therefore be early to a technology and late to its impact. It can also be late to a technology and early to value because it already did the less exciting work.

What the Numbers Made Me Reconsider About Technology Impact

The World Economic Forum’s Intelligent Industrial Operations Outlook 2026 groups technologies as foundational, orchestrating or autonomous, then assesses their impact across NOW, NEAR and NEXT. It also measures how many of 13 operational functions each technology could materially affect. A few numbers immediately pulled my attention away from the others.

First, the Two 13s

Digital twins and agentic AI are the only technologies considered relevant across all 13 operational functions. I agree with the breadth of digital twins. A twin can represent equipment, products, processes, factories or entire supply networks. I am less convinced that every function is equally close to meaningful value. The National Institute of Standards and Technology’s research into digital-twin interoperability helps explain why more than half of the impact remains in NEAR and NEXT. Creating an individual twin is increasingly practical. Getting many twins, models and organizations to understand one another remains messy.

The second 13, agentic AI, is where I hesitate more. I agree with its eventual reach, but 53% of the expected impact arriving in NEAR feels aggressive. The McKinsey State of AI 2025 survey found that 23% of organizations were scaling an agent somewhere, yet no individual function exceeded 10%. I would move some agentic impact into NEXT, particularly where agents influence safety, quality or physical production. Summarizing maintenance history is one thing. Deciding whether the machine should continue running creates a considerably livelier meeting with Legal.

Then I Looked at NOW

Predictive and prescriptive AI has roughly 66% of its impact in NOW. Edge/IoT follows at about 65%. I think WEF nailed predictive AI. It improves decisions companies already make without requiring them to surrender those decisions completely. The Deloitte 2025 Smart Manufacturing and Operations Survey found manufacturers reporting improvements of up to 20% in production output and employee productivity, plus 15% in unlocked capacity, largely from connected data, analytics and automation.

Edge/IoT surprised me for the opposite reason. I expected its NOW share to be even higher because nearly every later technology needs operational data. But Deloitte’s adoption numbers helped explain the assessment: only 46% of surveyed manufacturers used IIoT at the facility or network level, while 57% used cloud computing and data analytics and 42% used 5G. Edge/IoT may be foundational, but it is hardly universal. I still think its seven-function score understates its indirect importance. Infrastructure has the strange habit of disappearing inside whatever it enables.

Quantum, AR/VR and My Revised Assumptions

Quantum surprised me most. Five applicable functions and roughly one-third of its impact arriving before NEXT initially felt high, so I looked into it expecting to disagree. Instead, I came around.

The McKinsey Quantum Technology Monitor 2026 reports that more than 300 companies now have quantum initiatives and quantum-computing companies generated over $1 billion in 2025. Early applications in scheduling, logistics, materials and optimization make five functions more believable, while the heavy NEXT weighting still reflects reality.

AR/VR caught my attention because it no longer catches much attention. WEF gives it five functions, with about 46% of impact in NOW and 36% in NEAR. According to Counterpoint Research’s Global XR Market Tracker, global VR headset shipments fell 12% in 2024, their third consecutive annual decline. The spotlight clearly moved elsewhere. But diminished attention is not diminished usefulness. The HTC VIVE State of Extended Reality in Manufacturing survey found that 75% of industrial XR adopters reported a clear return, with training the most common application. Perhaps WEF has this one right too: AR/VR may be becoming less of a headline and more of a tool.

This brings me back to the two clocks. The report estimates the external clock by asking when each technology’s impact will materialize. The categories expose the internal clock by showing the order in which capabilities must develop. Companies have to plan against both, while the eight forces discussed later in the report keep changing the weather, the route and occasionally the destination.

The eight forces are also moving the clocks

The report identifies eight forces shaping industrial strategy: global trade, regulatory complexity, customer expectations, climate disruption, technology evolution, cybersecurity, workforce skills and social equity. It then connects each to an imperative, including regional supply networks, digital compliance, better traceability, circular operations, faster technology adoption, integrated security, human-machine collaboration and broader workforce participation.

My first instinct was to read these as eight more things companies need to address. Which is probably how many organizations will treat them. Eight forces become eight workstreams, eight steering committees, at least 47 PowerPoint slides and, if everyone is particularly unlucky, a new transformation office. But I think the more interesting interpretation is that these forces change the two clocks.

Technology evolution affects the external timeline. It determines what becomes technically possible and how quickly the cost falls. Cybersecurity, workforce readiness and regulatory complexity influence the internal sequence because they determine whether a company can responsibly use what has become possible. Customer expectations and climate pressure shape where impact will actually be valued. Global trade can rearrange the entire operating footprint while the plan is being executed. Social equity affects who participates in the change, who benefits from it and whether adoption survives contact with the people expected to make it work.

This means NOW, NEAR and NEXT cannot be universal labels. They are partly characteristics of the technology, but they are also characteristics of the company and its environment. Predictive AI may be NOW for a manufacturer with connected assets and clean maintenance history, NEAR for one still integrating plants and essentially “please stop asking” for one whose most reliable source of asset history is a binder in Steve’s desk.

The forces also interact in ways that can accelerate or delay impact. A trade disruption may suddenly make supply-chain visibility economically urgent, pulling a NEAR investment into NOW. A new regulation can do the same for traceability. A cyber incident can push an organization in the opposite direction, slowing connectivity plans while access, segmentation and governance are rebuilt. A workforce shortage may accelerate robotics, but a shortage of automation engineers may then delay the robotics. The future has a sense of humor. This is why I particularly like the report’s leadership imperatives around process discipline, decision-critical data and selecting a small number of financially meaningful use cases. They are not glamorous predictions. They are ways to increase the odds that an organization will be ready when the external timeline reaches it.

I also think NOW planning should deliberately preserve options for NEAR and NEXT. That does not mean designing every current project around a speculative autonomous future. It means avoiding choices that make the next step unnecessarily painful. Can the data be reused? Can another plant connect without rebuilding everything? Is decision logic visible and governed? Can a person challenge what the system recommends? Those questions are far less exciting than announcing an AI strategy, but they determine whether future technology arrives as an upgrade or another demolition project.

The statistics reinforce the problem. IBM’s 2026 threat research found that manufacturing remained the most targeted industry, while employers expect 39% of workers’ core skills to change by 2030 according to the Future of Jobs Report 2025. So companies are being asked to connect more, automate more and delegate more decisions while simultaneously protecting a larger attack surface and helping people adjust to work that is changing underneath them.

That is not an argument for waiting. It is an argument for understanding that readiness is part of the forecast. This is ultimately what I attempted to visualize: not simply when technologies might arrive, but how their expected timing intersects with the sequence of capabilities required to use them. The eight forces make that intersection even less predictable because they can move the destination, accelerate the schedule or close the runway while everyone is already boarding.

The departure board is only half the story. Someone still has to make the connection.

References:

Jeff Winter
Next

Thinking Harder Is Not the Same as Thinking Better