Preparing early doesn’t mean committing early

I once heard a striking claim in a discussion about innovation strategy:

“Innovation is now all about the short term. Standards and patents belong to the past. Open source has replaced them.”

The statement stayed with me because it confused faster innovation with the disappearance of long-term strategic horizons.

Yet these are not opposing worlds.

Open source, standards, intellectual property and technological capabilities can coexist, interact and reinforce one another. Their roles may evolve, but none makes long-term preparation irrelevant.

The pace of innovation may accelerate. The time required to enable it does not necessarily shrink.

Consider Physical AI, the cloud–edge–device continuum, quantum technologies — or 6G, while 5G is still being deployed.

Their maturity and development paths differ considerably. Yet they raise a common strategic question:

When should an organization start preparing for something that is still taking shape?

More than a technology roadmap

Physical AI offers a revealing example.

Its development involves more than advances in AI models or robotics. It also depends on how devices, sensors, networks, edge infrastructure and cloud resources cooperate to perceive, decide and act.

The challenge goes beyond connectivity. It includes interoperability between perceptions, models, decisions and actions, alongside safety, trust and operational continuity.

Some building blocks already exist. Others are emerging, including potential new interoperability standards.

The same interdependence appears in the cloud–edge–device continuum, where computing, intelligence and control must not only be distributed, but also federated across systems with different constraints.

And in connectivity, asking why we should already consider 6G while 5G is still being deployed highlights another reality: deployment follows years of research, standardization, capability development and product preparation.

Different examples, one underlying challenge:

What needs to start when, across the entire enablement chain?

Every organization starts somewhere

There is no universal starting point.

A startup may begin with a small nucleus of knowledge, expertise and ambition, then progressively build the capabilities required to grow.

An established organization may possess decades of research, experience, IP, partnerships and technological capabilities — yet struggle to adapt them to a changing environment.

Having knowledge is not the same as being able to act on it.

For both, emerging change raises fundamental questions:

What do we know? What do we still need to understand?

This is where knowledge discovery matters — revealing not only relevant existing knowledge, but also what is missing.

The same applies to capabilities.

What can we already do? What do we need to become capable of doing?

Some capabilities can be developed from scratch. Others can be reused, combined differently, accessed through partners or transformed. Some may need to be abandoned.

This is where capability discovery becomes capability management: deciding what to build, acquire, access, mobilize, transform or preserve — and when.

Whether an organization is growing or transforming, the challenge is not simply to accumulate knowledge and capabilities.

It is to develop and evolve the ability to act as the environment changes.

Synchronizing the enablement chain

Technology, standards, capabilities and markets do not move on the same clock.

A technology may still be years from maturity, while some capabilities required to act on it may already take years to develop. Standards, industrialization and market adoption introduce additional timelines.

These trajectories overlap, influence one another and evolve as new knowledge emerges.

The challenge is therefore not simply to estimate a time-to-technology.

It is to understand how time-to-technology, time-to-standards, time-to-capability and time-to-market interact — and which decisions those interactions require.

Some activities may need to begin early. Others can wait.

An organization may choose to monitor developments, explore alternatives, participate in research or standardization, build partnerships, or invest in selected capabilities.

It may also decide not to engage.

Preparing early does not mean committing early.

It means understanding enough about emerging change — and about the organization’s own knowledge, capabilities and gaps — to distinguish what can wait from what cannot.

Preserving the ability to act

Waiting can provide greater clarity.

But clarity that arrives after the window to act has closed has limited strategic value.

Conversely, committing too early can create dependencies around assumptions that may prove wrong.

The objective is neither to predict the future nor to invest in every emerging technology.

It is to make informed decisions while there is still time to act — and to reassess them as technologies, standards, ecosystems and markets evolve.

Doing so requires more than monitoring emerging technologies or producing additional analysis.

It requires connecting external change with an organization’s evolving knowledge, experience and capabilities — understanding not only what it can do today, but what it needs to develop or transform to act tomorrow.

This is where Decision Intelligence for Emerging Change can make a difference.

Not by deciding for organizations, but by helping them understand their options, anticipate the implications of their choices, and adjust their preparedness trajectories as new knowledge emerges.

Because the real question is not simply what technologies are coming next.

It is what we need to understand, discover and enable today to still have a choice tomorrow.