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We’re in what’s being dubbed the ‘year of AI ROI’. Four years on from the AI boom - ignited by the launch of ChatGPT - many businesses now believe that they’re AI-ready, using successful early-stage chatbot and copilot rollouts as evidence.

Yet there is a significant difference between experimenting with AI and embedding it across complex business processes and operations.

The real barrier to this transformation sits beneath the models themselves. While most AI outcomes-related conversations focus on model performance, GPUs, and compute capacity, organisations are increasingly realising that it’s their data infrastructure holding AI projects back.

Up until now, businesses have got away with operating successfully despite their disconnected file environments, inconsistent data governance, and information spread across multiple repositories.

This is because historically, data has been accessed sporadically, mostly by human employees who could compensate for shortcomings when data lacked context or wasn’t where it should be.

Simply put, data infrastructure didn’t need to be perfect, so it wasn’t. However, these fragmented file environments are now impossible to ignore as AI demands 24/7 consistent access to information, strong governance, and trusted data.

This puts enterprises in an uncomfortable situation: under pressure to deploy AI, but missing the foundations needed to be truly successful with AI projects at scale.

AI makes the data puzzle more complicated

Traditional file environments were built for a world where information lived in separate locations, individual teams managed access, and gaps in governance or data availability could be worked around.

This means that enterprises are now operating with fragmented information spread across multiple locations, with inconsistent governance and varying levels of accessibility. When data is accessed primarily by human employees, these inefficiencies are a source of frustration rather than a barrier.

This is where AI changes things. Models depend on fast, reliable access to information. If data is difficult to find, lacks context or cannot be accessed consistently, its value diminishes rapidly. In this instance, enterprises have data, but it’s completely unusable.

Overconfidence is coming at the expense of real success

Most organizations realize that their data infrastructure is far from perfect. Yet, the pressure to demonstrate progress on AI is intense. If businesses aren’t investing in and experimenting with AI, they’re seen as behind the curve. This is creating a strong incentive to deploy new tools as quickly as possible.

The problem is that organizations' early-stage AI learnings, if not outright success, are creating an overconfidence in their wider AI readiness and ability to scale up. Chatbots and copilots, which provide a lower barrier to entry to AI, are enabling organizations to demonstrate early, tangible AI outcomes.

When these projects deliver value, enterprises start to think that perhaps their infrastructure is future-proofed after all, despite a lack of sustained attempts to address and resolve deeper structural issues around their data accessibility, governance and infrastructure. These same businesses are also struggling with data recovery after cyber incidents - the warning signs are there, but current overconfidence is blurring reality.

As a result, businesses are moving rapidly onto major agentic projects, only to face delays, questions over ROI, and often, an implementation that fails. The irony is that an urgency to deploy AI is actually slowing long-term progress.

Organizations hellbent on deploying AI are risking overlooking the deeper risk management and data management work required to support it, while those that invest in strong data foundations are more likely to achieve sustainable success as their AI ambitions grow.

Shifting data from a cost to an asset

Most businesses don’t suffer from a lack of data; if anything, they have the opposite problem. Unfortunately, this is still viewed as an operational cycle driven by capacity planning, refresh cycles and expansion requirements, rather than a strategic asset. Industry research consistently shows IT teams still struggle to get a grip on their unstructured data - where most of their data assets reside - despite most of them believing their file data setup is robust.

The difference now is that AI systems operate continuously, drawing on information created today, yesterday, and sometimes decades ago. To deliver meaningful outcomes, they require consistent access to trusted data, rich context, and clear governance.

This means that organizations need to rethink their approach to enterprise information. When data is treated purely as a storage refresh challenge, AI quickly exposes the limitations of that approach. However, when it’s treated as a strategic asset, organizations can create environments where information is accessible, governed and ready to behave in the way that AI expects.

C-Suites’ goal should be to move from steady-state storage capacity to data utility, creating centralized environments with fewer systems, where data can be used no matter where it is located. This shift reduces fragmentation, simplifies data management and creates a unified view of the enterprise’s information, and with it the path to competitive edge.

The fewer barriers that exist between AI and the data it needs, the easier it becomes to move from isolated AI successes to meaningful deployment at scale for productivity and efficiency benefits.

Fix the foundations before scaling AI

The success of chatbot or copilot rollouts does not indicate AI readiness. It is instead measured by data utility: whether the data underpinning AI tools is accessible, secure, and fit for purpose at scale.

The organizations that succeed with AI in the coming years will not be those deploying the most advanced models or the biggest number of agents, but those who can provide AI with consistent access to trusted, well-governed data.

Those boards that keep investing in sophisticated AI tools without upgrading their data layer and governance will find their operations shackled for years to come by the same underlying data access and governance limitations.

It’s time to stop viewing data as an operational burden and start treating it as a strategic asset, helping unleash organizations' true IP and powering their ability to innovate. Building strong data foundations today will set organizations on the path to realizing long-term AI value, instead of discovering down the line that AI ambitions outpace their ability to support them.

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FARNBOROUGH, England, July 17 (Reuters) — Spiraling security risks are expected to push defense to the forefront of Britain’s Farnborough Airshow, as aerospace and arms makers struggle to keep pace with demand for weapons while cementing a fragile recovery in civil jet and engine production.

With the Ukraine war in its fifth year and a ceasefire in tatters in the Gulf, those risks are also likely to move the ritual contest between Boeing and Airbus for jetliner orders down the pecking order at the July 20 to 24 show.

“The global security environment is arguably more complex and volatile today than we have seen in many, many decades, and we are watching security threats evolve at a breakneck pace,” Air Chief Marshal Harv Smyth, head of the Royal Air Force, told an International Air Chiefs Conference ahead of the show.

Weapons makers enter their biennial bazaar witnessing the biggest rise in European defense spending since the Cold War but with unresolved questions about where and how it will be spent.

First the coast guard, then an ICBM: China tests new, long-term ways to hold off rivals in Asia

One of the industry’s most influential figures warned the shift to drones and AI systems could disrupt defense as much as SpaceX transformed the launch business, as wars in Ukraine and Iran expose the need for faster developments and mass production.

“I know both worlds and they couldn’t be further apart,” said ex-Airbus CEO Tom Enders, who is co-chairman of German defense startup Helsing and heads foreign policy think tank DGAP.

“We used to have a saying that traditional defense companies … would only pick up a pen if the government paid for it,” he said in an interview on the eve of the show.

“The younger companies are aggressive, not risk-averse. They spend their own money. Procurement agencies and armed forces increasingly understand that this is the way for a dynamic fast-moving industry,” said Enders, who also chairs tank maker KNDS.

While some new budgeted funds will be spent on today’s warplanes like the Lockheed Martin F-35 and the Eurofighter — both performing displays next week — startups like Helsing and U.S.-based Anduril are pushing AI-driven systems like uncrewed fighter cohorts, despite initial setbacks.

“Valuations are tilting in favor of the defense entrants but … most militaries are still spending the vast amount of their resources on manned platforms,” said Byron Callan, managing partner of research firm Capital Alpha.

Organizers said defense will represent half of the record 1,600 exhibitors at the show, up from 40% historically, with a sharp increase in AI, deep-tech and finance companies.

Commercial jet sales, supply chains

On the commercial side, Airbus and Boeing are expected to announce new orders and disclose customers behind previously booked deals.

But with delivery slots largely sold out well into the next decade, the usual scrum of media announcements is likely to attract less attention as investors focus instead on aircraft deliveries, where manufacturers generate most profits.

Air shows can still produce surprises, but industry sources said total deals may struggle to climb far above 300 aircraft, well below some pre-show forecasts of as many as 800 jets. The tally could also include deals that have already been announced.

Separately, Air China said it would order 55 Airbus jets.

“Winning orders is not the question. It’s not the relevant measuring stick that it used to be because of production capacity constraints,” said Jerrold Lundquist, managing director of advisory firm The Lundquist Group.

Aerospace has been wrestling with supply problems since COVID-19, especially for castings and forgings — critical parts made to exacting standards from molten or solid metal.

Fixing such issues is key to a repeatedly delayed target by Airbus to lift single-aisle jet output by about 25% to 75 a month in 2027. Boeing, looking to narrow a gap against its rival and put a floor under declining market share, has signaled it is studying production above currently targeted levels.

“The supply chain...has improved relative to where it was a year or two ago but (not) to the point where Airbus can pursue its goal of 75,” said manufacturing expert Kevin Michaels, managing director of AeroDynamic Advisory.

“And as Boeing raises rates, it’s surely going to cause issues there as well,” he added.

Delays in engines and interiors have also been among the most persistent supply-chain headaches. But the show will open on a more positive note from the world’s largest engine maker.

“I do think the supply chain has really turned the corner,” GE Aerospace CEO Larry Culp told Reuters. “(There is) more work to do.”

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QUICK FACTS

Name: Tianchi Lake, or Heaven Lake

Location: China-North Korea border

Coordinates: 42.0091, 128.0593

Why it's incredible: It is the deepest lake in China and sits at the top of a giant volcano.

Heaven Lake is a crater lake at the top of Mount Changbaishan (also called Changbai Mountain) — a colossal, dormant volcano on the border between China and North Korea that formed through successive eruptions over the past 2.6 million years.

Known in China as Tianchi, the lake is the highest and largest crater lake in Northeast Asia, according to UNESCO . It is also the deepest lake in China, according to a study published in March. The lake sits at an elevation of around 7,200 feet (2,200 meters), covers roughly 3.6 square miles (9.2 square kilometers), and has a maximum depth of 1,224 feet (373 m), according to the study.

Heaven Lake is surrounded by 16 peaks that belong to Mount Changbaishan. The lake fills a caldera created by past eruptions, the biggest of which was the "millennium eruption" that took place in A.D. 946 and remains one of the largest eruptions in modern history.

Water started to pool on Mount Changbaishan's summit after the prehistoric Tianwenfeng eruption, which scientists dated to between 70,000 and 40,000 years ago. Heaven Lake has periodically emptied and refilled since then due to precipitation, snowmelt, and an active geothermal system beneath the volcano that forces water up through fault lines .

Although hundreds of reports in the early 2000s claimed there was a creature with a head shaped like that of a horse living in the water, scientists have repeatedly said they are skeptical that any large creature could survive in Heaven Lake.

Mount Changbaishan is one of the best-preserved stratovolcanoes from the past several million years, according to UNESCO . Stratovolcanoes, also known as composite volcanoes, are volcanic mountains built from alternating layers of solidified lava, volcanic ash and rocky debris. Mount Changbaishan is an "open-air classroom for volcanism" because it recorded the different stages of multiple eruptions in astounding detail.

In North Korea, Mount Changbaishan is known as Mount Paektu or Paeku Mountain, meaning "white-topped mountain." The volcano's Chinese name means "forever white mountain."

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China, North Korea and South Korea have clashed over the volcano in the past , as the mountain holds cultural and geopolitical significance for the three countries. Two border treaties between China and North Korea in 1962 and 1964 divided the volcano and Heaven Lake roughly down the middle, with North Korea securing 54.5% of the lake.

In the 2000s, China moved to develop the region around the volcano, opening the Mount Changbai Airport and the Mount Changbai Eastern Railroad to connect the region with the rest of the country.

A geological park surrounding the Chinese site of the volcano was officially designated a UNESCO Global Geopark in 2024.

Discover more incredible places , where we highlight the fantastic history and science behind some of the most dramatic landscapes on Earth.