Quote for the day:
"People respect leaders who share power and despise those who hoard it." -- Gordon Tredgold
TheCUBE Research 2026 predictions: The year of enterprise ROI
Fourteen years into the modern AI era, our research indicates AI is maturing
rapidly. The data suggests we are entering the enterprise productivity phase,
where we move beyond the novelty of retrieval-augmented-generation-based
chatbots and agentic experimentation. In our view, 2026 will be remembered as
the year that kicked off decades of enterprise AI value creation. ... Bob
Laliberte agreed the prediction is plausible and argued OpenAI is clearly
pushing into the enterprise developer segment. He said the consumerization
pattern is repeating – consumer adoption often drives faster enterprise adoption
– and he viewed OpenAI’s Super Bowl presence as a flag in the ground, with Codex
ads and meaningful spend behind them. He said he is hearing from enterprises
using Codex in meaningful ways, including cases where as much as three quarters
of programming is done with Codex, and discussions of a first 100%
Codex-developed product. He emphasized that driving broader adoption requires
leaning on early adopters, surfacing use cases, and showing productivity gains
so they can be replicated across environments. ... Paul Nashawaty said
application development is bifurcating. Lines of business and citizen developers
are taking on more responsibility for work that historically sat with
professional developers. He said professional developers don’t go away – their
work shifts toward “true professional development,” while line of business
developers focus on immediate outcomes.Snowflake CEO: Software risks becoming a “dumb data pipe” for AI
Ramaswamy argues that his company lives with the fear that organizations will
stop using AI agents built by software vendors. There must certainly be added
value for these specialized agents, for example, that they are more accurate,
operate more securely, and are easier to use. For experienced users of existing
platforms, this is already the case. A solution such as NetSuite or Salesforce
offers AI functionality as an extension of familiar systems, whereby adoption of
these features almost always takes place without migration. Ramaswamy believes
that customers have the final say on this. If they want to consult a central AI
and ignore traditional enterprise apps, then they should be given that option,
according to the Snowflake CEO. ... However, the tug-of-war around the center of
AI is in full swing. It is not without reason that vendors claim that their
solution should be the central AI system, for example because they contain
enormous amounts of data or because they are the most critical application for
certain departments. So far, AI trends among these vendors have revolved around
the adoption of AI chatbots, easy-to-set-up or ready-made agentic workflows, and
automatic document generation. During several IT events over the past year,
attendees toyed with the idea that old interfaces may disappear because every
employee will be talking to the data via AI.Will LLMs Become Obsolete?
“We are at a unique time in history,” write Ashu Garg and Jaya Gupta at
Foundation Capital, citing multimodal systems, multiagent systems, and more.
“Every layer in the AI stack is improving exponentially, with no signs of a
slowdown in sight. As a result, many founders feel that they are building on
quicksand. On the flip side, this flywheel also presents a generational
opportunity. Founders who focus on large and enduring problems have the
opportunity to craft solutions so revolutionary that they border on magic.”
... “When we think about the future of how we can use agentic systems of AI to
help scientific discovery,” Matias said, “what I envision is this: I think
about the fact that every researcher, even grad students or postdocs, could
have a virtual lab at their disposal ...” ... In closing, Matias described
what makes him enthusiastic about the future. “I'm really excited about the
opportunity to actually take problems that make a difference, that if we solve
them, we can actually have new scientific discovery or have societal impact,”
he said. “The ability to then do the research, and apply it back to solve
those problems, what I call the ‘magic cycle’ of research, is accelerating
with AI tools. We can actually accelerate the scientific side itself, and then
we can accelerate the deployment of that, and what would take years before can
now take months, and the ability to actually open it up for many more people,
I think, is amazing.”Deepfake business risks are growing – here's what leaders need to know
The risk of deepfake attacks appears to be growing as the technology becomes
more accessible. The threat from deepfakes has escalated from a “niche
concern” to a “mainstream cybersecurity priority” at “remarkable speed”, says
Cooper. “The barrier to entry has lowered dramatically thanks to open source
software and automated creation tools. Even low-skilled threat actors can
launch highly convincing attacks.” The target pool is also expanding, says
Cooper. “As larger corporations invest in advanced mitigation strategies,
threat actors are turning their attention to small and medium-sized
businesses, which often lack the resources and dedicated cybersecurity teams
to combat these threats effectively.” The technology itself is also improving.
Deepfakes have already improved “a staggering amount” – even in the past six
months, says McClain. “The tech is internalising human mannerisms all the
time. It is already widely accessible at a consumer level, even used as a form
of entertainment via face swap apps.” ... Meanwhile, technology can be helpful
in mitigating deepfake attack risks. Cooper recommends deepfake detection
tools that use AI to analyse facial movements, voice patterns and metadata in
emails, calls and video conferences. “While not foolproof, these tools can
flag suspicious content for human review.” With the risks in mind, it also
makes sense to implement multi-factor authentication for sensitive
requests. The Big Shift: From “More Qubits” to Better Qubits
As quantum systems grew, it became clear that more qubits do not always mean
more computing power. Most physical qubits are too noisy, unstable, and
short-lived to run useful algorithms. Errors pile up faster than useful
results, and after a while, the output stops making sense. Adding more fragile
qubits now often makes things worse, not better. This realization has led to a
shift in thinking across the field. Instead of asking how many qubits fit on a
chip, researchers and engineers now ask a tougher question: how many of those
qubits can actually be trusted? ... For businesses watching from the outside,
this change matters. It is easier to judge claims when vendors talk about
error rates, runtimes, and reliability instead of vague promises. It also
helps set realistic expectations. Logical qubits show that early useful
systems will be small but stable, solving specific problems well instead of
trying to do everything. This new way of thinking also changes how we look at
risk. The main risk is not that quantum computing will fail completely.
Instead, the risk is that organizations will misunderstand early progress and
either invest too much because of hype or too little because of old ideas.
Knowing how important error correction is helps clear up this confusion. One
of the clearest signs of maturity is how failure is handled. In early science,
failure can be unclear. Reimagining digital value creation at Inventia Healthcare
“The business strategy and IT strategy cannot be two different strategies altogether,” he explains. “Here at Inventia, IT strategy is absolutely coupled with the core mission of value-added oral solid formulations. The focus is not on deploying systems, it is on creating measurable business value.” Historically, the pharmaceutical industry has been perceived as a laggard in technology adoption, largely due to stringent regulatory requirements. However, this narrative has shifted significantly over the last five to six years. “Regulators and organisations realised that without digitalisation, it is impossible to reach the levels of efficiency and agility that other industries have achieved,” notes Nandavadekar. “Compliance is no longer a barrier, it is an enabler when implemented correctly.” ... “Digitalisation mandates streamlined and harmonised operations. Once all processes are digital, we can correlate data across functions and even correlate how different operations impact each other,” points out Nandavadekar. ... With expanding digital footprints across cloud, IoT, and global operations, cybersecurity has become a mission-critical priority for Inventia. Nandavadekar describes cybersecurity as an “iceberg,” where visible threats represent only a fraction of the risk landscape. “In the pharmaceutical world, cybersecurity is not just about hackers, it is often a national-level activity. India is emerging as a global pharma hub, and that makes us a strategic target.”Scaling Agentic AI: When AI Takes Action, the Real Challenge Begins
Organizations often underestimate tool risk. The model is only one part of the
decision chain. The real exposure comes from the tools and APIs the agent can
call. If those are loosely governed, the agent becomes privileged automation
moving faster than human oversight can keep up. “Agentic AI does not just stress
models. It stress-tests the enterprise control plane.” ... Agentic AI requires
reliable data, secure access, and strong observability. If data quality is
inconsistent and telemetry is incomplete, autonomy turns into uncertainty.
Leaders need a clear method to select use cases based on business value,
feasibility, risk class, and time-to-impact. The operating model should enforce
stage gates and stop low-value projects early. Governance should be built into
delivery through reusable patterns, reference architectures, and pre-approved
controls. When guardrails are standardized, teams move faster because they no
longer have to debate the same risk questions repeatedly. ... Observability must
cover the full chain, not just model performance. Teams should be able to trace
prompts, context, tool calls, policy decisions, approvals, and downstream
outcomes. ... Agentic AI introduces failure modes that can appear plausible on
the surface. Without traceability and real-time signals, organizations are
forced to guess, and guessing is not an operating strategy.Security at AI speed: The new CISO reality
The biggest shift isn’t tooling, we’ve always had to choose our platforms carefully, it’s accountability. When an AI agent acts at scale, the CISO remains accountable for the outcome. That governance and operating model simply didn’t exist a decade ago. Equally, CISOs now carry accountability for inaction. Failing to adopt and govern AI-driven capabilities doesn’t preserve safety, it increases exposure by leaving the organization structurally behind. The CISO role will need to adopt a fresh mindset and the skills to go with it to meet this challenge. ... While quantification has value, seeking precision based on historical data before ensuring strong controls, ownership, and response capability creates a false sense of confidence. It anchors discussion in technical debt and past trends, rather than aligning leadership around emerging risks and sponsoring a bolder strategic leap through innovation. That forward-looking lens drives better strategy, faster decisions, and real organizational resilience. ... When a large incumbent experiences an outage, breach, model drift, or regulatory intervention, the business doesn’t degrade gracefully, it fails hard. The illusion of safety disappears quickly when you realise you don’t own the kill switches, can’t constrain behaviour in real time, and don’t control the recovery path. Vendor scale does not equal operational resilience.Why Borderless AI Is Coming to an End
Most countries are still wrestling with questions related to "sovereign AI" -
the technical ambition to develop domestic compute, models and data capabilities
- and "AI sovereignty" - the political and legal right to govern how AI operates
within national boundaries, said Gaurav Gupta, vice president analyst at
Gartner. Most national strategies today combine both. "There is no AI journey
without thinking geopolitics in today's world," said Akhilesh Tuteja, partner,
advisory services and former head of cybersecurity at KPMG. ... Smaller nations,
Gupta said, are increasing their investment in domestic AI stacks as they look
for alternatives to the closed U.S. model, including computing power, data
centers, infrastructure and models aligned with local laws, culture and region.
"Organizations outside the U.S. and China are investing more in sovereign cloud
IaaS to gain digital and technological independence," said Rene Buest, senior
director analyst at Gartner. "The goal is to keep wealth generation within their
own borders to strengthen the local economy." ... The practical barriers to AI
sovereignty start with infrastructure. The level of investment is beyond the
reach of most countries, creating a fundamental asymmetry in the global AI
landscape. "One gigawatt new data centers cost north of $50 billion," Gupta
said. "The biggest constraint today is availability of power … You are now
competing for electricity with residential and other industrial use cases."
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