Omnimodal AI
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Omnimodal Intelligence: AI that perceives the entire world

We’re taking a look at the trends that matter most for leaders navigating the next 12 to 36 months.

We organize trends into three categories based on their maturity and impact timeline:

Game Changers: Trends reshaping entire industries right now. These affect how businesses operate, how value is created, and how competitive advantage is built.

Foundational Breakthroughs: Scientific and engineering advances unlocking new possibilities. These create strategic optionality for the decade ahead.

Weak Signals: Early indicators of transformations that will dominate the late 2020s. These require positioning now, even if mainstream adoption is years away.

Today, we’re diving into one of the Foundational Breakthrough trends: Omnimodal Intelligence.

What is it?

Multimodal AI (text + image + audio) was only the beginning. Omnimodal systems can combine vision, language, spatial data, code, simulation, physics, and robotic action, enabling AI to understand not just our digital worlds but the physical world around us.

A good metaphor for this is the human brain. We don’t have one brain for seeing, one for hearing etc. We have one integrated brain that considers all our senses, all at once. This is the foundation for robotics, AR, autonomous systems, and digital environments that understand us as richly as we understand them.

Where we’re seeing it emerge

Every sense fused into physical action. Figure’s Helix 02 folds vision from head and palm cameras, tactile sensing fine enough to register forces as small as three grams, proprioception, and natural language into a single neural network that controls an entire humanoid robot.

Rather than bolting a locomotion controller onto a separate manipulation system, one model lets the robot walk, balance, and manipulate as a single continuous system, running entirely from onboard sensors with no human intervention. In one demonstration, it completed a continuous four-minute task made up of 61 separate actions, described as the longest-horizon autonomous task a humanoid has performed to date. It signals where omnimodal is heading: perception fused tightly enough that a machine can operate inside the physical world it senses.

One model that sees, listens, and moves a whole body. In July 2026, Google DeepMind introduced Gemini Robotics 2, a family of models that takes in live video, audio and language and turns them into action. Its reasoning model can talk with people, track its own progress on a task by watching it, and plan multi-step work, and in one demonstration it paused a humanoid when a person walked nearby and resumed only once the area was clear. The action model now directs an entire humanoid, not just its hands, and an on-device version runs on the robot’s own computer and can be adapted to a new robot with a few hours of training. Omnimodal perception is moving onto the machine itself, where the decisions happen.

3D worlds built for machines to train in. World Labs’ Marble generates persistent 3D worlds from text, images, video, or coarse layouts, and it does so as a multimodal world model rather than a single purpose image tool. What makes Marble a genuine signal of omnimodal progress is where its output goes next: exported as Gaussian splats or collider meshes, these AI generated environments are already being pulled directly into NVIDIA Isaac Sim to train robots, cutting scene setup from weeks to hours. Marble is a bridge between generative AI and the physical systems that have to operate in the real world.

A robot brain that imagines before it acts. NVIDIA’s next humanoid model, GR00T N2, previewed in March 2026, is built as a “world action model.” Rather than mapping what a robot sees straight to a movement, it first predicts how the scene will change, then plans the actions that get it to the goal. NVIDIA says this helps robots succeed at new tasks in new environments more than twice as often as leading vision-language-action models, and N2 currently ranks first on the MolmoSpaces and RoboArena benchmarks for generalist robot policies. It is slated to be available by the end of the year.

The world’s robots learn in are changing too. Cosmos 3, which NVIDIA released as an open model in June 2026, handles text, images, video and robot actions in one system, folding scene understanding, world generation and action simulation together. NVIDIA calls it the first open omni-model for physical AI reasoning and action. Together they show omnimodal perception moving from understanding a scene to controlling a body inside it.

Omnimodal perception as the eyes and ears of AI agents. Most agent systems today still stitch together separate models for vision, speech, and language, losing time and context every time information passes from one model to the next. NVIDIA’s Nemotron 3 Nano Omni, released in April 2026, folds text, image, audio, video, and document understanding into a single open model built for exactly this handoff problem, delivering up to 9x the throughput of comparable open omni models. It is built for computer use agents, document intelligence, and audio and video reasoning. Palantir and Foxconn are among the companies already adopting it, and others such as Oracle and Docusign are evaluating it, an early sign that omnimodal perception is moving from research benchmark to production agent stack.

Why it matters

For years, “multimodal” meant a model that could look at an image and describe it or transcribe audio into text. Omnimodal is a different ambition: systems that hold vision, sound, language, spatial structure, and physical dynamics in one coherent understanding, the way a person walking through a room does without thinking about it.

That shift changes what AI can do in the places where decisions actually get made:

Decisions made in context. An omnimodal system can read voice tone, facial cues, documents, dashboards, and live video at once, connecting a spike in sensor data to the line in a written report that explains it and picking up both what is said in a meeting and what is left implied. Leaders get fewer blind spots because the signals no longer sit in separate tools that never talk to each other.

Holistic diagnosis. In medicine, one model can weigh imaging, genomics, lab results, and a patient’s speech patterns together, flagging anomalies that surface across modalities rather than in any single test. That gives clinicians integrated insight instead of four disconnected readouts, pointing toward earlier detection and better outcomes.

Just-in-time operations. On the factory floor, omnimodal perception fuses visual inspection, IoT sensor streams, and maintenance logs into real-time anomaly detection across machines and whole facilities, with hands-free guidance delivered through voice and augmented visuals. The payoff is reduced downtime, safer environments, and predictive intervention before failure instead of after.

Immersive learning. In training and education, a system can learn from how a student speaks, writes, gestures, and interacts, then adapt instruction across AR/VR, video, and dialogue and drop the learner into experiential simulations. Because instruction meets each person where they actually learn, retention climbs and training moves faster.

The through line is the same in every case: when perception stops being fragmented, a system can act on the full picture instead of a slice. For enterprise leaders and founders building in this space, the strategic question is where perception stops being a bottleneck. Teams working on robotics, spatial computing, simulation, healthcare, industrial operations, or agentic AI are the ones who will feel this shift first, and the infrastructure choices they make now, which world models to build on, which perception stacks to bet on, will shape what they can ship over the next several years.

AI Blog
— Comments Off on Living AI: Models That Learn as They Go | 2026 Trends

Living AI: Models That Learn as They Go | 2026 Trends

We’re taking a look at the trends that matter most for leaders navigating the next 12–36 months.

We organize trends into three categories based on their maturity and impact timeline:

Game Changers — Trends reshaping entire industries right now. These affect how businesses operate, how value is created, and how competitive advantage is built.

Foundational Breakthroughs — Scientific and engineering advances unlocking new possibilities. These create strategic optionality for the decade ahead.

Weak Signals — Early indicators of transformations that will dominate the late 2020s. These require positioning now, even if mainstream adoption is years away.

Today, we’re diving into a Foundational Breakthrough that could redefine what AI is capable of.

Living AI: Models That Learn As They Go

Every AI model you’ve used, from the chatbot on your phone to the copilot in your enterprise software, knows only what it was taught during training. Ask it about something that happened after its cutoff date, or something outside its training data, and it simply doesn’t know. It’s frozen in time.

That’s about to change.

The next frontier in AI is about models that keep learning after deployment. Continuous, real-time learning would let AI systems absorb new information, adapt to shifting environments, and refine their own reasoning without waiting for the next training run. It’s a small conceptual shift with enormous implications: it’s widely considered one of the key unlocks on the path toward AI superintelligence.

Where This Is Emerging

Liquid AI is rethinking the neural network itself. An MIT spinout founded by Ramin Hasani and Daniela Rus, it builds “liquid” networks from neurons whose underlying equations keep changing at inference time, so the model adapts to new data on the fly instead of staying frozen after training — an architecture inspired by the 302-neuron nervous system of the C. elegans worm. The efficiency gains are striking: in one autonomous-driving test, just 19 liquid neurons matched the lane-keeping performance of a conventional network with more than 100,000 neurons, while drawing only 1 to 5 watts of power.

Safe Superintelligence (SSI) represents a new class of AI company built around a singular focus: systems that can learn and improve continuously, without compromising on safety, alignment, or human oversight. Founded in June 2024 by former OpenAI chief scientist Ilya Sutskever — co-creator of GPT-3 and GPT-4 — alongside Daniel Levy and Daniel Gross, SSI is a bet that today’s dominant recipe of more compute and more data will eventually plateau, and that reaching superintelligence will require fundamentally new ways of learning. It is a pointed wager from the person who did as much as anyone to prove that scaling works. With roughly 20 employees, no product, and no revenue, SSI has been valued at $32 billion — the market pricing a thesis rather than a business, and a signal that some of the smartest capital in AI believes continual, self-improving learning is the real path forward.

AMI Labs takes yet another route. Founded by Turing Award winner Yann LeCun after his departure from Meta, it is pursuing “world models” through an approach called JEPA — AI that learns how the physical world actually works, its cause, effect, and physics, rather than just the statistical patterns of text. The idea has drawn serious backing: a $1 billion seed round and a $4.5 billion valuation as of March 2026, from investors including Nvidia, Samsung, Jeff Bezos, and Eric Schmidt.

Why It Matters

Continual learning is the missing piece for AI superintelligence. Today’s models are static snapshots that are remarkably capable, but locked in place the moment training ends. A model that learns continuously can do something fundamentally different: discover new insights on its own, adapt to environments its creators never anticipated, and potentially develop reasoning capabilities that go beyond what it was explicitly trained to do.

That’s the shift from AI as a tool that recalls information to AI as a system that can genuinely innovate and invent.

For leaders, the strategic question is how quickly and where the first real advantages show up. Companies building products, security systems, or research tools on top of adaptive AI today are positioning themselves for a very different competitive landscape tomorrow.

Blog Futurism & Technology Trends Innovation
— Comments Off on Sovereign Stacks: Nations Build Independent AI and Chip Ecosystems

Sovereign Stacks: Nations Build Independent AI and Chip Ecosystems

We’re taking a look at the trends that matter most for leaders navigating the next 12–36 months.

We organize trends into three categories based on their maturity and impact timeline:

Game Changers — Trends reshaping entire industries right now. These affect how businesses operate, how value is created, and how competitive advantage is built.

Foundational Breakthroughs — Scientific and engineering advances unlocking new possibilities. These create strategic optionality for the decade ahead.

Weak Signals — Early indicators of transformations that will dominate the late 2020s. These require positioning now, even if mainstream adoption is years away.

Today, we’re diving into soverign stacks.

For decades, technology supply chains were optimized for efficiency. Design here, fabricate there, assemble somewhere else, ship everywhere. That model is being rewritten because efficiency is no longer the only thing governments are optimizing for.

Why now

This isn’t theoretical anymore. In just the past few months, escalating export controls, chip bans, and abrupt platform restrictions have made clear that shared global technology platforms can no longer be taken for granted, and governments are moving fast to reduce that exposure. Three pressures are converging behind the shift. Supply chain fragility, exposed during the pandemic, made clear how concentrated chip production was and how vulnerable every downstream industry was as a result. National security concerns followed close behind, as AI capabilities came to be seen less as a commercial advantage and more as a strategic asset. And economic competitiveness rounded it out: countries that can train and run their own AI models, on their own chips, are positioning to export that capability to others.

Countries are now building independent technology stacks from the ground up, from semiconductor fabs to the AI training infrastructure that runs on top of them. Since 2021, government incentives in the US have helped catalyze nearly $400 billion in announced semiconductor investments, with major production facilities breaking ground in Arizona, Ohio, and across Europe under parallel initiatives. The scale of capital being committed to domestic chip production hasn’t been seen in a generation.

The result is a layered build-out. Fabs at the bottom. Cloud and compute infrastructure in the middle. Sovereign AI models on top. Each layer reinforces the case for the others.

Recent movements

A few signals show how far this has already progressed.

China continues to invest heavily in domestic AI alternatives. DeepSeek is reportedly developing its own AI chip, aimed at inference rather than training, in a move that would reduce its dependence on Nvidia and Huawei silicon, a notable shift for a company built on squeezing more out of other people’s hardware. Alongside this, Harmony OS represents a parallel effort to reduce dependence on Western operating systems at the device level.

India is pursuing a similar strategy through BharatGen, a national generative AI initiative backed by the country’s Department of Science and Technology. The program has trained models on thousands of GPUs and introduced a 17-billion-parameter multilingual model built specifically for India’s languages and governance needs. More recently, BharatGen was named the anchor for India’s participation in Project Tapestry, a global AI Alliance consortium that lets nations fine-tune a shared base model on their own sovereign data while retaining full ownership of the result, an explicit hedge against depending indefinitely on AI systems trained on foreign contexts and governed elsewhere.

Europe and South Korea are moving at similar speed. The European Commission’s new technology sovereignty package pairs a “Chips Act 2.0” with a Cloud and AI Development Act aimed at building out domestic compute and reducing reliance on non-European providers. South Korea, meanwhile, has laid out more than $1 trillion in combined public and private investment behind what its government calls a “triple axis” strategy spanning semiconductors, physical AI, and data centers, with Samsung and SK Hynix anchoring new fabs at home.

And in the US, the shift is as much institutional as it is industrial. The agency formerly known as the US AI Safety Institute has been reformed into the Center for AI Standards and Innovation, with a mandate that includes representing US interests internationally and pushing back against what it considers burdensome foreign regulation of American AI technology. CAISI has since expanded its pre-deployment testing agreements to Google DeepMind, Microsoft, and xAI, alongside existing partners OpenAI and Anthropic, and has now completed more than 40 model evaluations, some in classified environments. The renaming reflects a broader pattern: countries aren’t just building their own technical stacks, they’re building the standards and institutions to defend them.

Why it matters

Technology, geopolitics, and economic leverage are converging into a single conversation. A chip is no longer just a chip, and a foundation model is no longer just a product. Each is also a statement about whose infrastructure, whose standards, and whose rules a country plans to operate under.

For global businesses, this means navigating an increasingly nationalized landscape, where the device manufacturer, cloud provider, the AI model creator, and even the operating system a customer uses may be shaped less by what’s best and more by where it was built. A strategy that assumes a single global technology stack is a blind spot.

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The Agency Economy: Why Work Is Shifting From Doing to Directing

For decades, the dominant promise of technology was productivity. Do the same work faster. Automate the repetitive. Reduce friction at the margins. It was a compelling proposition, and it delivered real value. But it left the fundamental nature of work largely unchanged: humans still executed, and technology assisted.

Now, that relationship is flipping.

As AI agents take on full execution of complex, multi-step workflows, human value is moving upstream. Planning, judgment, creativity, ethical reasoning, and strategic direction are no longer just differentiators. They are becoming the primary deliverables.

We are entering the Agency Economy: a moment where human agency, the ability to define what work should accomplish rather than how to accomplish it, becomes the core professional competency.

From Execution to Direction

The clearest signal of this shift is the speed at which agentic AI is moving from experimentation to operations. According to Deloitte’s 2025 survey of nearly 2,000 executives, 57% are already using agentic AI in some capacity, with deployment expanding beyond pilots into customer service, fraud detection, IT operations, and knowledge management.

The shift is significant. Teams that previously spent most of their time on task execution are now spending it on goal-setting, constraint definition, and outcome evaluation. The question has changed from “how do we complete this workflow?” to “what should this workflow accomplish, and how do we know when it’s done well?”

This represents a different cognitive mode, and most organizations haven’t designed for it.

What Changes, and What Doesn’t

In the Agency Economy, several things become more valuable: the ability to decompose a complex goal into actionable agent instructions; the judgment to recognize when an AI-generated output is technically correct but strategically wrong; the creativity to frame problems in ways that unlock novel solutions; and the accountability to own outcomes that AI helped produce.

Execution speed on routine, well-defined tasks is becoming less valuable. Those tasks matter, but AI agents increasingly handle them with accuracy and scale that outpace human throughput.

What this looks like in practice:

  • A marketing team no longer writes every content variant. They define brand parameters, audience segmentation logic, and quality standards, then direct agents to generate, test, and iterate at scale.
  • A procurement team doesn’t process every vendor communication. They set negotiation guardrails and relationship priorities, then direct agents to handle the execution layer.

In each case, the human contribution is upstream. The work of directing is harder than it looks, and far more consequential than the execution work it replaces.

The ROI Gap Is a Design Problem

Despite rapid adoption, the Deloitte survey found that only 10% of organizations using agentic AI currently report significant ROI. Most expect returns within one to five years. That’s a design problem.

Organizations are deploying agents into workflows built for human execution. The instructions are ambiguous. The success criteria are undefined. The escalation paths are unclear. Agents operating in that environment do what any intelligent system does when given poor direction: they optimize for the wrong thing, or they stall.

The Agency Economy only delivers on its promise when organizations redesign roles and workflows around the assumption that humans are directors. That means being explicit about what good outcomes look like, building feedback loops that let agents learn from human judgment, and creating governance structures that keep humans accountable for what agents produce.

This is the organizational challenge that separates the organizations capturing value from those still waiting for it.

Strategic Implications

  1. Redefine roles around judgment, not task completion. Job descriptions built around execution will need to be rebuilt around direction. The question for every function is: what decisions here genuinely require human judgment, and what can be specified well enough to delegate to an agent? Organizations that answer this question deliberately will move faster than those that let it happen to them.
  2. Invest in the human skills that agents can’t replace. Critical thinking, ethical reasoning, cross-functional communication, and the ability to evaluate AI outputs against strategic intent are not soft skills. They are the core competencies of the Agency Economy. L&D programs that focus on AI tool adoption without developing these upstream skills are solving the wrong problem.
  3. Design agentic workflows for auditability, not just automation. When agents are executing on behalf of humans, the humans remain responsible for outcomes. That requires clear records of what instructions were given, what outputs were produced, and where human judgment overrode agent recommendations. Organizations that build this visibility in from the start will have a significant governance advantage as regulatory frameworks mature.
  4. Build the hardware layer that makes agency possible. Agentic AI workflows generate a different kind of compute demand than prompt-response interactions: they require sustained, multi-step reasoning, often on sensitive organizational data. Running those AI workflows locally at the edge, rather than routing everything to the cloud, addresses both the performance and the data sovereignty requirements that enterprise adoption demands.

HP’s latest AI PCs are built with NPUs designed to support agentic AI workloads locally, enabling fast inference without cloud dependency. HP IQ runs a 20-billion-parameter model on-device, handling routine task execution so knowledge workers can stay focused on the direction layer.

Recently, our team was tracking edge inference trends, including how AI workloads are shifting from cloud to on-device processing. We’d been capturing and tagging relevant articles, podcasts, and analyst reports on this trend.

Our Intel Digest agent, which synthesizes research into actionable intelligence, picked up those highlights, scored them against HP’s strategic priorities, and clustered them with related signals it had already been tracking. It revealed that several startups already in our pipeline were part of the same emerging opportunity, connecting companies we hadn’t previously linked. It generated a themed briefing, created a standalone Insight note linking the trend and related startups to specific HP business units, and produced follow-up actions: update the relevant startup profiles, flag startups for our AI PC team, and monitor others for investment outreach.

There is a workforce dimension here, too. HP’s 2025 Work Relationship Index found that only 20% of knowledge workers report a healthy relationship with work. A properly-designed Agency Economy is part of the answer. When people spend less time on execution and more time on work that genuinely requires their judgment, the nature of that relationship changes.

What Needs to Mature

The Agency Economy is a compelling direction, but several things need to develop in parallel for it to reach its potential.

Role redesign at scale. Most organizations are still adding agents to existing workflows rather than redesigning workflows around agent capabilities. The productivity gains remain incremental until the redesign happens. That requires organizational will, not just technical investment.

Agent governance frameworks. As agents take on more consequential work, the question of who is responsible for what they produce becomes urgent. Clear accountability structures, audit trails, and escalation protocols are not optional features. They are prerequisites for operating at scale especially in regulated environments.

The direction skills gap. Instructing an AI agent well is a skill. Knowing when to override it is a skill. Evaluating whether an output is technically correct but strategically wrong is a skill. Most organizations don’t yet explicitly train for these capabilities, and the gap is showing up in adoption results.

Compute accessibility. The Agency Economy’s benefits currently accrue most strongly to organizations with the budget and infrastructure to deploy agentic workflows at scale. As on-device AI matures and the cost curve compresses, that access will broaden, but the pace matters. Organizations that build the infrastructure foundation now will have a meaningful head start.

Looking Ahead

The reframe is: it’s not about humans doing less. It’s about humans doing fundamentally different things.

The work of direction, setting goals, defining constraints, evaluating outcomes, making judgment calls that require context and values and accountability, has always been what organizations pay most for.

This is the moment when the infrastructure catches up to that reality. The execution layer becomes reliable enough to delegate. The direction layer becomes consequential enough to deserve full human attention.

The organizations that will lead this transition are the ones designing for it now: rebuilding roles around judgment, investing in the human skills that agents can’t replicate, and building the compute infrastructure that makes agentic workflows fast, private, and controllable.

The shift from doing to directing is already underway. The question is whether you’re redesigning around it or waiting to react.

Blog Futurism & Technology Trends
— Comments Off on The Agency Economy: Why Work Is Shifting From Doing to Directing

The Agency Economy: Why Work Is Shifting From Doing to Directing

For decades, the dominant promise of technology was productivity. Do the same work faster. Automate the repetitive. Reduce friction at the margins. It was a compelling proposition, and it delivered real value. But it left the fundamental nature of work largely unchanged: humans still executed, and technology assisted.

Now, that relationship is flipping.

As AI agents take on full execution of complex, multi-step workflows, human value is moving upstream. Planning, judgment, creativity, ethical reasoning, and strategic direction are no longer just differentiators. They are becoming the primary deliverables.

We are entering the Agency Economy: a moment where human agency, the ability to define what work should accomplish rather than how to accomplish it, becomes the core professional competency.

From Execution to Direction

The clearest signal of this shift is the speed at which agentic AI is moving from experimentation to operations. According to Deloitte’s 2025 survey of nearly 2,000 executives, 57% are already using agentic AI in some capacity, with deployment expanding beyond pilots into customer service, fraud detection, IT operations, and knowledge management.

The shift is significant. Teams that previously spent most of their time on task execution are now spending it on goal-setting, constraint definition, and outcome evaluation. The question has changed from “how do we complete this workflow?” to “what should this workflow accomplish, and how do we know when it’s done well?”

This represents a different cognitive mode, and most organizations haven’t designed for it.

What Changes, and What Doesn’t

In the Agency Economy, several things become more valuable: the ability to decompose a complex goal into actionable agent instructions; the judgment to recognize when an AI-generated output is technically correct but strategically wrong; the creativity to frame problems in ways that unlock novel solutions; and the accountability to own outcomes that AI helped produce.
Execution speed on routine, well-defined tasks is becoming less valuable. Those tasks matter, but AI agents increasingly handle them with accuracy and scale that outpace human throughput.

What this looks like in practice:

  • A marketing team no longer writes every content variant. They define brand parameters, audience segmentation logic, and quality standards, then direct agents to generate, test, and iterate at scale.
  • A procurement team doesn’t process every vendor communication. They set negotiation guardrails and relationship priorities, then direct agents to handle the execution layer.

In each case, the human contribution is upstream. The work of directing is harder than it looks, and far more consequential than the execution work it replaces.

The ROI Gap Is a Design Problem

Despite rapid adoption, the Deloitte survey found that only 10% of organizations using agentic AI currently report significant ROI. Most expect returns within one to five years. That’s a design problem.

Organizations are deploying agents into workflows built for human execution. The instructions are ambiguous. The success criteria are undefined. The escalation paths are unclear. Agents operating in that environment do what any intelligent system does when given poor direction: they optimize for the wrong thing, or they stall.

The Agency Economy only delivers on its promise when organizations redesign roles and workflows around the assumption that humans are directors. That means being explicit about what good outcomes look like, building feedback loops that let agents learn from human judgment, and creating governance structures that keep humans accountable for what agents produce.

This is the organizational challenge that separates the organizations capturing value from those still waiting for it.

Strategic Implications

  1. Redefine roles around judgment, not task completion. Job descriptions built around execution will need to be rebuilt around direction. The question for every function is: what decisions here genuinely require human judgment, and what can be specified well enough to delegate to an agent? Organizations that answer this question deliberately will move faster than those that let it happen to them.

  2. Invest in the human skills that agents can’t replace. Critical thinking, ethical reasoning, cross-functional communication, and the ability to evaluate AI outputs against strategic intent are not soft skills. They are the core competencies of the Agency Economy. L&D programs that focus on AI tool adoption without developing these upstream skills are solving the wrong problem.

  3. Design agentic workflows for auditability, not just automation. When agents are executing on behalf of humans, the humans remain responsible for outcomes. That requires clear records of what instructions were given, what outputs were produced, and where human judgment overrode agent recommendations. Organizations that build this visibility in from the start will have a significant governance advantage as regulatory frameworks mature.

  4. Build the hardware layer that makes agency possible. Agentic AI workflows generate a different kind of compute demand than prompt-response interactions: they require sustained, multi-step reasoning, often on sensitive organizational data. Running those AI workflows locally at the edge, rather than routing everything to the cloud, addresses both the performance and the data sovereignty requirements that enterprise adoption demands.

HP’s latest AI PCs  are built with NPUs designed to support agentic AI workloads locally, enabling fast inference without cloud dependency. HP IQ runs a 20-billion-parameter model on-device, handling routine task execution so knowledge workers can stay focused on the direction layer.

Recently, our team was tracking edge inference trends, including how AI workloads are shifting from cloud to on-device processing. We’d been capturing and tagging relevant articles, podcasts, and analyst reports on this trend.

Our Intel Digest agent, which synthesizes research into actionable intelligence, picked up those highlights, scored them against HP’s strategic priorities, and clustered them with related signals it had already been tracking. It revealed that several startups already in our pipeline were part of the same emerging opportunity, connecting companies we hadn’t previously linked. It generated a themed briefing, created a standalone Insight note linking the trend and related startups to specific HP business units, and produced follow-up actions: update the relevant startup profiles, flag startups for our AI PC team, and monitor others for investment outreach.

There is a workforce dimension here, too. HP’s 2025 Work Relationship Index found that only 20% of knowledge workers report a healthy relationship with work. A properly-designed Agency Economy is part of the answer. When people spend less time on execution and more time on work that genuinely requires their judgment, the nature of that relationship changes.

What Needs to Mature

The Agency Economy is a compelling direction, but several things need to develop in parallel for it to reach its potential.

Role redesign at scale. Most organizations are still adding agents to existing workflows rather than redesigning workflows around agent capabilities. The productivity gains remain incremental until the redesign happens. That requires organizational will, not just technical investment.

Agent governance frameworks. As agents take on more consequential work, the question of who is responsible for what they produce becomes urgent. Clear accountability structures, audit trails, and escalation protocols are not optional features. They are prerequisites for operating at scale especially in regulated environments.

The direction skills gap. Instructing an AI agent well is a skill. Knowing when to override it is a skill. Evaluating whether an output is technically correct but strategically wrong is a skill. Most organizations don’t yet explicitly train for these capabilities, and the gap is showing up in adoption results.

Compute accessibility. The Agency Economy’s benefits currently accrue most strongly to organizations with the budget and infrastructure to deploy agentic workflows at scale. As on-device AI matures and the cost curve compresses, that access will broaden, but the pace matters. Organizations that build the infrastructure foundation now will have a meaningful head start.

Looking Ahead

The reframe is: it’s not about humans doing less. It’s about humans doing fundamentally different things.

The work of direction, setting goals, defining constraints, evaluating outcomes, making judgment calls that require context and values, and accountability, has always been what organizations pay most for.

This is the moment when the infrastructure catches up to that reality. The execution layer becomes reliable enough to delegate. The direction layer becomes consequential enough to deserve full human attention.

The organizations that will lead this transition are the ones designing for it now: rebuilding roles around judgment, investing in the human skills that agents can’t replicate, and building the compute infrastructure that makes agentic workflows fast, private, and controllable.

The shift from doing to directing is already underway. The question is whether you’re redesigning around it or waiting to react.

Blog Futurism & Technology Trends
— Comments Off on The CVC Problem Nobody Talks About — and How AI Agents Solve It

The CVC Problem Nobody Talks About — and How AI Agents Solve It

The corporate venture capital problem isn’t a lack of deal flow or capital. It’s becoming the strategic intelligence engine your parent company needs — delivering the market foresight, strategic insights, and partnership opportunities that help the organization leverage external innovation to drive growth.

Every week brings new startups, funding rounds, market signals, and competitive shifts. AI captured over half of all global VC funding in 2025, surpassing $200 billion. For CVC teams operating with small, lean groups and enormous scope, the challenge isn’t access to information. It’s synthesizing and acting on that information fast enough to matter.

This is the problem we set out to solve at HP Tech Ventures. Not by hiring more people, but by building a team of specialized AI agents — each with a defined role, structured workflows, and the ability to work together. They research, profile, analyze, and track the startup ecosystem continuously, consistently, and at a scale no human team could match.

Why CVC needs this more than anyone

Corporate venture capital programs face a structural challenge that pure financial VCs don’t: the dual mandate. You need to find great investments and drive strategic value for the parent company. That second part is notoriously difficult.

As one practitioner in Masters of Corporate Venture Capital put it: “Finding the right company is the easy part, but navigating the large corporation to unlock value — that is much harder to achieve successfully.”

The average CVC program survives less than four years — far short of the decade-long horizon venture returns require. Programs rarely fail because they pick bad investments. More often, it’s a lack of perceived strategic impact, organizational misalignment, or both. They struggle to prove strategic value fast enough. They struggle to survive executive turnover. And they struggle to maintain the flow of portfolio intelligence and broader strategic insights — the kind of connecting the dots across markets that a CVC is uniquely positioned to deliver — to the internal business units that need it.

Research from McKinsey, Bain, Stanford, and INSEAD points to a consistent set of failure modes. Our goal in building an Agentic AI system was to directly address the most impactful ones:

  • Sporadic sourcing becomes systematic coverage. McKinsey found that over 70% of CVC activity is sporadic or opportunistic. Automated market scanning and startup profiling replace ad hoc sourcing with continuous, disciplined coverage.
  • Strategic mission drift becomes measurable alignment. Stanford found that most CVCs struggle to stay aligned with their parent company’s evolving priorities. When every startup, insight, and partnership opportunity is evaluated against those priorities — and re-evaluated automatically when they shift — CVC drift becomes visible early.
  • Institutional memory survives turnover. A third of active CVCs were mothballed or shut down in the past three years. When team members rotate or leadership changes, a structured, connected knowledge base persists — the system remembers what your organization has learned, even when people move on.
  • Decision-making accelerates without sacrificing rigor. Bain found that applying M&A-style due diligence to early-stage startups is fundamentally broken. Automated research, profiling, and market analysis compress the time from “first heard of a company” to “informed evaluation” from weeks to minutes.
  • Insights become tracked actions. Most CVC intelligence dies in a document. When research, insights, meetings, and other CVC activities generate follow-ups — and an orchestration layer surfaces what’s overdue — nothing falls through the cracks.
  • Stakeholder communication shifts from scramble to stream. INSEAD found that 61% of senior executives at CVC parent companies don’t understand venture capital norms. Automated report generation — executive briefings, portfolio dashboards, trend digests — turns the CVC from a black box into a visible, productive intelligence function.

Scan markets. Align priorities. Retain knowledge. Accelerate decisions. Track follow-through. Communicate value. That’s the full cycle most CVC programs piece together manually — if they get to it at all. A well-designed AI agent system can automate the heavy lifting across every stage, freeing the team to focus on what matters most: human relationships, investing, and strategic impact.

What this looks like in practice

Here’s a real example. Recently our team was tracking edge inference trends — how AI workloads are shifting from cloud to on-device processing. We’d been capturing and tagging relevant articles, podcasts, and analyst reports on this trend.

Our Intel Digest agent, which synthesizes research into actionable intelligence, picked up those highlights, scored them against HP’s strategic priorities, and clustered them with related signals it had already been tracking. It revealed that several startups already in our pipeline were part of the same emerging opportunity, connecting companies we hadn’t previously linked. It generated a themed briefing, created a standalone Insight note linking the trend and related startups to specific HP business units, and produced follow-up actions: update the relevant startup profiles, flag startups for our AI PC team, and monitor others for investment outreach.

That chain — research to synthesis to cross-reference to action — used to take days of manual work across browser tabs, email threads, and scattered notes. It happened automatically while the team focused on the strategic decisions that actually require human judgment.

The architecture: structured knowledge, specialized agents

The current platform runs on Claude Code (Anthropic’s CLI tool) with Obsidian as the persistent knowledge layer. We chose Obsidian because every entity (startup, VC firm, market analysis, strategic insight) lives as a markdown file with structured metadata and bidirectional links to every related entity. Over time, these connections compound. The knowledge graph surfaces patterns that no individual document would reveal — which VCs are clustering around a technology, which market trends connect to multiple portfolio companies, where white spaces exist.

Rather than one monolithic AI assistant, the system uses a team of purpose-built agents. One profiles startups. Another tracks VC investment activity to catch emerging companies early. Another synthesizes our research into executive briefings. Another evaluates companies for investment with market sizing and valuation scenarios. Another prepares meeting briefing packages. An orchestration agent we call the Chief of Staff tracks scheduling, detects stale data, and maintains system health.

The key design principle: agents share context but own their domain. They all read from a canonical strategic priorities file. They all check the Insights library for relevant themes. They all log their work to a shared changelog. But each agent has its own workflow, its own templates, and its own definition of “done.” This mirrors how high-performing human teams work — shared awareness, specialized execution.

Where most AI tools stop — and where this goes further

Most AI tools generate a report, and you file it. The intelligence dies in a document.

This system closes the gap between “that’s interesting” and “someone should follow up.” Insights flow into leadership recommendations and are used to automatically monitor the market for signals and trends to watch. The Chief of Staff agent reviews these continuously, detects stale items, and surfaces what needs attention at the start of every session.

Different stakeholders then consume that intelligence in whatever format drives action — branded PDFs for executives, interactive HTML sites for business unit leads, annotatable Word documents for meeting attendees, polished slides for board presentations. Same intelligence, different delivery.

The entire infrastructure is remarkably simple. No databases. No cloud services. No enterprise software licenses beyond what most corporations already have. The system runs locally, which also means full data sovereignty. Sensitive deal flow intelligence never leaves the corporate environment.

What we’ve learned building this

Start with structure, not features. The most important decision was defining how every entity in the system would be structured before building any agents. Consistent templates with structured metadata mean every agent downstream — deal flow scoring, market analysis, meeting prep — can reliably parse and reference the data. If the foundation isn’t structured, no amount of AI sophistication on top will help.

The hardest part is the last mile. Getting intelligence out of the system and into the hands of decision-makers requires real investment in output formatting, distribution channels, and workflow integration. We built a Microsoft Teams intake system so colleagues can request analysis by submitting a form — no technical knowledge required. If users can’t easily consume the output, the system’s intelligence is wasted.

Compounding returns are real. The system becomes qualitatively more useful as it grows. Cross-references surface patterns. Market analyses reference existing profiles. Meeting prep pulls from past analyses. The knowledge graph doesn’t just store information — it creates new connections that didn’t previously exist in anyone’s head. Over time, the graph becomes the product.

A single source of truth prevents drift. All agents that assess strategic relevance read from one shared priorities file. When we correct an assessment, the feedback propagates to every agent. Without this, each agent develops its own biases and inconsistencies over time — and you lose the coherence that makes the system trustworthy.

Treat insights as first-class objects. We initially treated insights as byproducts of weekly digests — a section in a report that got read and forgotten. Making them standalone documents with their own lifecycle and action tracking was a turning point. Now every analytical agent checks the Insights library before starting work, building on previously synthesized intelligence rather than starting from scratch.

What this means for CVC

The average CVC program operates with a small team and enormous scope. AI agents don’t replace the team — they amplify it. The judgment calls, relationship building, and strategic intuition still require humans. But the research, profiling, monitoring, formatting, and action-tracking work that consumes most of a CVC professional’s day? That can and should be automated.

The shift mirrors what we’re seeing across the broader economy. As AI takes over execution, human value moves upstream — to direction, judgment, creativity, and decision-making. For CVC teams, this means spending less time researching startups and more time deciding which ones matter, building relationships with founders, and driving internal adoption of the technologies they invest in.

We believe this will become standard practice for CVC programs within the next few years. The teams that build these systems now — even imperfectly — will have a compounding advantage over those that wait. Not because the technology is hard to replicate, but because the structured knowledge base underneath it takes time to build. Every startup profiled, every insight captured, every connection mapped adds to a foundation that can’t be created overnight.

If you’re running a CVC program and spending more time gathering information than acting on it, ask yourself: What would change if every meeting had a briefing document waiting? If every startup in your pipeline was profiled to the same analytical standard? If your system remembered everything your organization has learned, even after people move on? If insights were automatically turned to action and action to opportunity?

The technology to build this exists today. You don’t need a massive engineering team or an enterprise AI platform. You need structured templates, a connected knowledge base, specialized agents that work together, and the willingness to dive in and start building.

The hardest part isn’t the technology. It’s taking an honest look at how your team works today and being willing to change it.

Blog corporate venture capital
— Comments Off on Human-AI Collaboration: 7 Skills You’ll Need in an AI-Driven Workplace

Human-AI Collaboration: 7 Skills You’ll Need in an AI-Driven Workplace

As artificial intelligence becomes deeply embedded in our daily work, we’re entering an unprecedented era of human-machine partnership that will redefine how we work, innovate, and create value.
 
AI is transforming the workplace. Are you developing the skills to thrive alongside it?

The Shift is Already Here

Across our portfolio companies and the broader tech ecosystem, AI adoption in the workplace is increasing. In the U.S., studies estimate that around 80% of workers will see AI affect at least 10% of their tasks, with initial 2025 data indicating that AI tools are already in active use for at least 25% of tasks in 36% of occupations.
 
This is a story about augmentation and collaboration. The most successful organizations aren’t those deploying the most AI, they’re the ones creating the most effective partnerships between human ingenuity and machine capability.

From Creation to Curation: The New Nature of Work 

One of the most significant shifts I’m observing is the move from creation to curation and direction. Workers using AI are spending less time creating content from scratch and more time reviewing, refining, and directing AI-generated outputs. This fundamentally changes the skills required for many roles.
 
When HP Tech Ventures evaluates startups, we’re no longer just looking at founders who can build everything themselves. We’re looking for entrepreneurs who can orchestrate and who understand how to leverage AI tools and synthesize AI-generated insights into strategic decisions. It’s a different skill set entirely.

The Skills That Will Define Success

Here are the critical capabilities we all need to develop:

  1. AI Literacy and Prompt Engineering

    You don’t need to be a programmer, but you absolutely need to understand how AI works, its capabilities, and its limitations. This includes:
    • Understanding Large Language Models (LLMs) and their applications
    • Mastering prompt engineering: crafting precise inputs to optimize AI outputs
    • Recognizing when AI excels and when human judgment is non-negotiable
    • Understanding biases, data privacy concerns, and ethical considerations

    Prompt engineering is becoming as fundamental as digital literacy was a decade ago. It’s the interface between human intent and machine execution.

    2. Critical Thinking and Judgment
     
    As AI handles more data processing and pattern recognition, human judgment becomes even more valuable. The ability to:

    • Evaluate AI-generated insights for accuracy and relevance
    • Provide context that machines cannot understand
    • Make nuanced decisions that require cultural awareness and empathy
    • Question assumptions and think strategically about AI recommendations

    3. Adaptability and Continuous Learning
     
    Here’s a stark reality: 76% of employees believe AI will create entirely new skills that don’t yet exist. Continuous learning is the skill.
     
    The most successful professionals I’m working with treat learning as a daily practice. They’re tracking thought leaders, learning about the technology, and experimenting with new AI tools as they emerge. At HP, we’re seeing this reflected in how quickly teams adapt to new technologies when they’ve cultivated a learning mindset.

    4. Interpersonal and Collaboration Skills
     
    This might seem counterintuitive, but as AI takes over information-processing tasks, interpersonal skills become even more critical. Research suggests that key human competencies are shifting from information-processing skills to interpersonal and organizational capabilities.
     
     Why? Because AI can analyze data, but it can’t:

    • Build trust with stakeholders
    • Navigate complex organizational dynamics
    • Inspire teams during uncertainty
    • Facilitate meaningful human connections
    • Lead with empathy and emotional intelligence

    These uniquely human capabilities are becoming the primary differentiators.

    5. Creative Problem-Solving and Innovation
     
    While AI excels at optimization and pattern recognition, human creativity remains indispensable. The ability to:

    • Ask outlandish questions and free your mind
    • Identify problems that AI hasn’t been trained to see
    • Combine insights from disparate domains in novel ways
    • Imagine entirely new applications of existing technologies

    We need to adopt long-term, futuristic thinking. AI can help us get there faster, but it’s human imagination that defines where we’re going.

    6. Data Interpretation and Storytelling
     
    Insights are useless if they can’t be shared. A critical emerging skill is distilling AI-driven information into clear, actionable narratives that stakeholders can understand and act upon.
     
    This means:

    • Translating complex AI outputs into business language
    • Creating compelling visualizations of data insights
    • Communicating uncertainty and confidence levels appropriately
    • Bridging technical and non-technical audiences

    The founders who succeed aren’t necessarily those with the best technology. They’re the ones who can tell the most compelling story about what that technology means.

    7. Ethical AI Oversight and Governance
     
    As AI takes on more responsibility, someone needs to ensure it’s being used responsibly. This includes:

    • Understanding algorithmic bias and fairness concerns
    • Ensuring transparency in AI decision-making
    • Protecting privacy and data security
    • Maintaining human oversight for high-stakes decisions
    • Advocating for responsible AI development and deployment

    This is a business imperative. Companies that get this wrong will face significant consequences.

    How Can You Start Building These Skills Today?

    Experiment Fearlessly 
    Start using AI tools in your daily work. ChatGPT, Copilot, Gemini, Claude — they’re all available now. The best way to learn is by doing. Learn what works, what doesn’t, and why.
     
    Invest in Structured Learning     
    Platforms like Salesforce’s Trailhead offer engaging, AI-powered learning paths. Identify skills gaps and fill them systematically.
     
    Stay Curious     
    Subscribe to technology newsletters, follow thought leaders, and monitor startup activity. I personally stay on top of trends by reading the latest tech news, speaking with startups, VCs and industry experts, and tracking venture investing patterns. Build your own trend-watching system.
     
    Build a Learning Community     
    Connect with others who are navigating this transformation. Share insights, challenges, and strategies. The pace of change is too fast for anyone to figure out alone.
     
    Embrace the Uncomfortable     
    The shift to AI-augmented work will feel awkward at first. That’s normal. Push through the discomfort. The companies and individuals who adapt fastest will have enormous advantages.

    The Future Belongs to Orchestrators

    The most valuable professionals over the next five years will be the orchestrators, the people who can seamlessly coordinate human creativity, AI capabilities, and strategic objectives.
     
    They’ll be the ones who can envision what’s possible with AI assistance, direct AI tools toward meaningful problems, synthesize machine insights with human wisdom, lead teams through this transformation with empathy, and create value that neither humans nor machines could achieve alone. 
     
    The companies that will thrive will be those building cultures where humans and AI work together, where technology complements human potential rather than replacing it.
     
    So ask yourself: Which of these seven skills will you start developing this week? Because the choice isn’t whether to adapt to an AI-driven workplace — it’s whether you’ll lead the adaptation or follow it.

    Futurism & Technology Trends
    — Comments Off on How corporate venture capital de-risks emerging technology development

    How corporate venture capital de-risks emerging technology development

    As the pace of technological change accelerates, corporations are no longer just adapting to disruption — they are actively investing in it. Through their corporate venture capital (CVC) arms, established companies are entering partner-investor relationships with startups across frontier domains such as artificial general intelligence (AGI), humanoid robotics, quantum computing, and other potentially game-changing technologies.

    In this post, I’m exploring how corporates are using CVC to de-risk exploration in these emerging tech domains, the competitive advantage of corporate-startup partnerships, which categories are particularly well suited to CVC versus traditional VC, and how emerging-tech startups can best position themselves for CVC investment.

    How CVC reduces technology risk for corporations

    Corporations face an inherent tension: they need to pursue innovation and new business models, but they also must manage risk, protect core business margins, and maintain operational stability.

    That’s why more than 25% of the funding deals last year included CVCs. CVC offers a hybrid path: by investing in and partnering with startups in nascent and emerging technologies, corporates can explore adjacent or disruptive opportunities while externalizing much of the technology risk.

    In domains such as AGI, quantum computing, space tech, or next-gen energy storage, the technologies are capital-intensive, have long horizons, involve high technical and commercialization risk, and often require domain-specific assets, manufacturing, regulatory engagement, or ecosystem partnerships. For corporations, investing via CVC is a strategic way to gain early exposure, build optionality, secure technology rights or vantage, and integrate promising startups into their ecosystem — enabling them to stay ahead of both disruptive threats and complementary opportunities.

    However, not all companies should invest in all emerging technologies — the key is strategic selectivity, focusing on technologies that could either disrupt their industry or offer complementary capabilities to enhance their competitive position.

    CVC is a de-risking engine for corporates exploring emerging tech — it lets them access options in high-uncertainty spaces without the full burden of building in-house, while building strategic alignment with their core business and future growth vectors.

    How CVCs reduce technology risk through strategic value creation

    While most emerging tech startups will need venture capital funding, nearly all funding deals can benefit significantly from CVC participation. The question isn’t whether corporate venture capital adds value, but rather how CVCs uniquely reduce technology risk for both corporates and startups across different technology categories.

    What makes CVC different from traditional VC?

    CVC brings strategic value beyond capital: manufacturing capabilities, distribution networks, supply chain access, regulatory expertise, and direct integration pathways. Traditional VC focuses primarily on financial returns and rapid scaling without operational entanglement.

    Especially suited for CVC

    Best for: Hardware, long horizons, strategic fit, high-capex technologies

    • Hardware + embedded systems (e.g., humanoid robots, advanced compute, quantum computing, next-gen energy storage, nuclear energy) — These domains require supply chain, manufacturing, and integration with existing platforms, often with regulatory or domain-specific partnerships. Corporations with manufacturing or platform assets can add real value. For example, HP Tech Ventures’ investment in EdgeRunner AI demonstrates how corporates can accelerate AI hardware integration. EdgeRunner builds domain-specific, air-gapped, on-device AI agents for military and enterprise applications that operate entirely without internet connectivity. The company’s platform delivers mission-specific AI assistants that ensure low latency, enhanced data privacy, and reduced cloud costs — critical advantages that scale when coupled with AI hardware platforms and edge computing products and expertise. Similarly, Intel Capital’s investment in Rigetti Computing showcases how corporate backing accelerates quantum computing development. Rigetti builds full-stack quantum computers, and Intel’s expertise in chip manufacturing, supply chain access, and deep semiconductor knowledge provides strategic advantages that pure financial investors cannot match, reducing both technical and commercialization risk.
    • Platform or ecosystem technologies (technologies that require broad industry adoption and create value through network effects, such as 6G/hyperconnectivity, clean-tech infrastructure etc.) — Corporates are often deploying or will deploy these platforms themselves, so investing via CVC gives them inside access and optionality.
    • Strategic technology adjacencies for the corporate. For example, if a corporate sees synthetic biology or biotech as a future adjacency to their business, then CVC allows them to explore while leveraging internal capabilities (e.g., R&D, supply chain, global operations).
    • High-capex / long-horizon technologies — Traditional VCs demand high returns within a fixed timeline, but corporates can afford longer horizons if strategic alignment is strong.

    Related to the above, trending data show that CVCs have recently been prioritizing AI and Robotics, which exemplify both platform technologies and strategic adjacencies that many corporates are exploring. For example, nearly 30% of CVC deals in 2024 revolved around AI.

    HP Tech Ventures’ recent investment in Multiverse Computing — a quantum-inspired AI company that compresses large language models by up to 95% while maintaining performance — exemplifies this trend. Multiverse’s technology addresses a critical infrastructure challenge in AI deployment, enabling models to run on edge devices and dramatically reducing computing costs and energy consumption.

    HP’s strategic support helps Multiverse scale this technology across enterprise applications, bringing AI benefits to companies of all sizes.

    Making it work for both CVC and startup

    In the accelerating wave of frontier technologies — from AGI and quantum computing to next-gen energy storage, synthetic biology, and space tech — the smart corporates will not wait passively. They will deploy their CVC as a strategic lever to access, partner with, and accelerate startups that can redefine their future business models.

    How do startups benefit from CVC partnerships?

    For startups operating in these domains, the path to growth means not just securing capital, but forging the right strategic partnerships: ones that bring scale, integration, and access to a corporate ecosystem that would otherwise take years to build.

    For HP Tech Ventures, this means offering portfolio companies access to HP’s world-class technology, one of the world’s largest channel and distribution partner networks, and a vast global manufacturing and supply chain — resources that help startups scale rapidly and achieve significant market impact.

    Emerging tech sectors alignment

    From humanoid robots to synthetic biology, the next wave of innovation is rewriting the boundaries of what’s possible — and CVCs are uniquely positioned to shape that future. The following table maps how each major emerging-tech sector aligns with corporate venture capital, and what founders should keep in mind as they navigate this evolving landscape.

    What should founders consider when pursuing CVC?

    By aligning technology, business model, partner strategy, and timing, both corporations and startups can ride the emerging-tech wave with lower risk and higher impact.

    If you’re a startup in one of these frontier domains and are thinking about CVC, ask yourself: Which corporations in my value chain have scale, distribution, or manufacturing that could accelerate me? How much risk are they willing to take? Am I ready for strategic integration?

    Blog corporate venture capital
    — Comments Off on How to future-proof your business in the age of AI

    How to future-proof your business in the age of AI

    The pace of AI advancement has moved from gradual evolution to explosive transformation. In McKinsey’s latest survey, 78% of respondents said their organizations use AI in at least one business function.
     
    In my role as a futurist and managing partner of HP Tech Ventures, I’ve witnessed firsthand how artificial intelligence is not just changing individual processes or products, but fundamentally rewiring entire industries in order to succeed. And I’m not alone. 88% of tech leaders believe AI adoption will create a competitive edge.
     
    The businesses that will thrive in this new landscape are those that build adaptive capacity today. The good news? Your business doesn’t necessarily need to invest large sums of money to succeed.

    The AI Megatrend: beyond the hype

    Unlike previous technological shifts that evolved over decades, the impact of AI is compressed into years, sometimes even months.
     
    The startups we work with through HP Tech Ventures are helping to transform established industries who are grappling with a fundamental reality: AI isn’t coming to transform their industry — it’s already here.
     
    The question becomes how to build resilience and adaptability into your organization when the rate of change itself is accelerating.
     
    Four pillars of AI-ready business architecture

    1. Cultivate an experimentation mindset
       
      Future-proof businesses don’t wait for perfect AI solutions; they experiment with imperfect ones. The most successful companies I’ve observed are running small-scale AI pilots across multiple business functions simultaneously. They’re building organizational muscle memory for rapid adoption and iteration.
       
      Start with low-risk, high-learning opportunities. Use AI tools to enhance customer service interactions, optimize scheduling, or improve content creation workflows. The goal isn’t immediate ROI. It’s developing institutional knowledge about how AI integrates with your specific business context.

    2. Invest in human-ai collaboration, not replacement
       
      The companies that thrive will be those that utilize AI to augment human capabilities. This requires rethinking job roles, not eliminating them. Customer service representatives become orchestrators of the customer experience. Financial analysts become strategic advisors. Marketing professionals become campaign architects.
       
      This shift demands significant investment in reskilling and upskilling your workforce. However, it also creates a competitive advantage: while your competitors focus on cost reduction through automation, you’re building enhanced capabilities through augmentation.
       
      It’s a win-win, too. 94% of employees would stay longer at a company that invests in their career development.

    3. Build data infrastructure as a strategic asset
       
      AI is only as good as the data that feeds it, yet most businesses treat data as a byproduct rather than a primary asset. Future-proofing requires viewing data infrastructure as critically important as financial systems or supply chain logistics. Improved data and security, as well as reduced compliance breaches, are among the top benefits of having data governance in place. 
       
      How do you accomplish this? Establish clear data governance protocols, invest in data quality systems, and create mechanisms for data sharing across organizational silos. It also means being strategic about what data you collect and how you structure it for future AI applications you haven’t even imagined yet.

    4. Develop ethical AI frameworks before you need them
       
      As AI becomes more central to business operations, the ethical implications become more complex. Businesses that establish clear ethical guidelines for AI use — covering everything from bias prevention to privacy protection and transparent decision-making — will have a significant advantage over those scrambling to address these issues reactively.
       
      Recent studies indicate a high level of public concern about AI’s negative impacts, with 86% of people supporting the regulation of AI companies. 
       
      But this isn’t just about compliance or public relations; it’s about ensuring the well-being of our employees. Ethical AI frameworks enable businesses to make informed decisions about which AI applications to pursue, how to implement them responsibly, and how to establish trust with customers and employees throughout the transformation process.

    The network effects of future-proofing

     
    One of the most interesting patterns I’ve observed is that AI-ready businesses create ripple effects throughout their ecosystems. Suppliers adapt their processes to integrate better with AI-enhanced workflows. Customers develop new expectations for service and customization. Partners begin exploring collaborative AI applications.
     
    This network effect creates a virtuous cycle: businesses that move early and thoughtfully in adopting AI help shape the standards and expectations for their entire industry. They become the gravitational center around which ecosystem innovation occurs.

    Weak signals to watch

    While most attention focuses on obvious AI applications like chatbots and process automation, the businesses that will truly dominate are paying attention to weak signals that indicate where AI is heading next:

    • The convergence of AI with other emerging technologies like quantum computing and advanced materials science
    • The development of AI systems that can reason about physical world constraints, not just digital information
    • The emergence of AI that can collaborate with other AI systems to solve complex, multi-step problems
    • The evolution of AI from task-specific tools to general-purpose reasoning systems

    How to take action today

    Future-proofing isn’t about predicting the future perfectly. It’s about building the organizational capabilities to adapt quickly when the future becomes clear. The businesses that will thrive are already taking concrete steps:

    • This Quarter: Identify three business processes where AI could provide immediate value and launch pilot programs. Establish cross-functional teams to evaluate AI tools and develop initial implementation strategies.
    • This Year: Invest in data infrastructure improvements and staff training programs focused on AI collaboration. Develop ethical guidelines for the use of AI and establish mechanisms for monitoring the performance and impact of AI systems.
    • Ongoing: Build relationships with AI technology providers, participate in industry groups exploring AI applications, and maintain awareness of emerging AI capabilities that could disrupt your business model.

    The Age of AI is today’s reality. The businesses that recognize this and act accordingly won’t just survive the transformation; they’ll lead it. The question isn’t whether your business can afford to invest in AI readiness.

    The question is whether it can afford not to.

    Blog Innovation Leadership
    Second Brain AI
    — Comments Off on Your Digital Brain Partner: How AI Will Transform How We Think and Work

    Your Digital Brain Partner: How AI Will Transform How We Think and Work

    Imagine having a co-worker who never forgets anything, can instantly recall every conversation you’ve ever had, and helps you connect ideas you never would have linked on your own. This is the reality of Second Brain AI, and it can fundamentally change how we work, learn, and think.
     
    We’re moving beyond simple AI tools that answer questions or automate tasks. The next wave of artificial intelligence will act as genuine thinking partners, extending our cognitive abilities in ways that feel almost magical. These aren’t replacements for human intelligence. They’re amplifiers that make us dramatically more capable.
     
    Tools are now emerging that demonstrate this power. Google’s NotebookLM, launched in 2024 and continuously updated through 2025, serves as an AI research assistant, transforming uploaded documents into interactive conversations and even podcast-style audio overviews. Meanwhile, platforms like Elict help researchers identify valuable research seeds and explore topics through conversational AI. Granola focuses on bringing your team’s conversations into one place and enhancing them with AI through summarizing, finding connections through scattered ideas, and surfacing relevant information.

    From Information Overload to Intelligent Insight

    We live in an age of information abundance that often feels more like information overwhelm. Every day, we’re bombarded with alerts, emails, articles, videos, podcasts, and conversations. Our natural response is to try to consume more, faster, but that’s a losing battle.
     
    Second Brain AI takes a completely different approach. Instead of helping you process more information, it helps you understand the information you already have. It identifies patterns you may have missed, connects ideas across different contexts, and surfaces exactly what you need when you need it.
     
    Think of it as having a personal librarian who has read everything you’ve ever encountered and can instantly provide the perfect piece of information for whatever you’re working on. However, unlike a human librarian, this one learns your thinking patterns and improves at helping you over time.
     
    ClickUp Brain exemplifies this approach, automatically summarizing lengthy conversation threads, drafting documents, and transcribing voice clips directly within tasks — eliminating the need for teams to switch between multiple tools and contexts.

    The End of Forgetting

    How many great ideas have you lost because you forgot to write them down? How many important details from meetings have slipped through the cracks? How often do you find yourself thinking, “I know I read something about this, but I can’t remember where”?
     
    Second Brain AI solves the fundamental human problem of forgetting. It creates a permanent, searchable record of your thoughts, experiences, and learning that grows more valuable over time. More importantly, it doesn’t just store this information — it actively helps you use it.
     
    Your digital brain partner remembers the context around every piece of information. It knows not just what you learned, but when you learned it, what you were working on at the time, and how it connects to other ideas in your mental landscape.
     
    Notion with AI integration and Obsidian’s interconnected note system are making this vision a reality. The Second Brain AI platform at thesecondbrain.io now allows users to chat with their saved notes from Notion, Evernote, and other platforms, while Elephas enables users to create topic-specific “brains” that can be shared via URLs for collaborative learning.

    Predictive Thinking: Knowing What You Need Before You Ask

    The most exciting aspect of Second Brain AI is its ability to anticipate your needs. By learning your patterns of thinking and working, it begins to suggest relevant information and insights before you even realize you need them.
     
    Working on a presentation? Your AI partner might surface research from six months ago that perfectly supports your argument. Facing a difficult decision? It could remind you of a similar situation you handled successfully and suggest applying the same approach.
     
    This predictive capability transforms how we approach complex problems. Instead of starting from scratch each time, you build on the accumulated wisdom of your past experiences, guided by an AI that sees patterns you might miss.

    Everyone Becomes an Expert

    One of the most democratizing aspects of Second Brain AI is how it levels the playing field between experts and beginners. Traditionally, expertise comes from years of accumulated knowledge and experience. But what if you could instantly access the insights and patterns that experts have developed over the course of decades?
     
    Second Brain AI doesn’t replace the need for deep thinking or creativity, but it dramatically accelerates the learning curve. A junior employee can make decisions informed by organizational wisdom that previously took years to acquire. Students can engage with complex topics by building on the collective knowledge of their field.

    The Creative Amplifier

    Creativity often comes from combining existing ideas in new ways. Second Brain AI excels at this kind of creative synthesis. It can identify unexpected connections between concepts, suggest novel combinations of ideas, and help you explore creative directions you might never have considered.

    This isn’t about AI generating creative work for you. It’s about AI helping you be more creative by expanding the pool of ideas and connections you can draw from. It’s like having a creative partner who has perfect recall of everything you’ve ever been interested in and can suggest fascinating combinations at just the right moment.
     
    Tools like MyMind and Bear App are pioneering this creative synthesis, using AI to help users discover unexpected connections between saved content, images, and ideas across different projects and time periods.

    Privacy and Control in the Age of AI

    A common concern about AI thinking partners is the issue of privacy and control. The most effective Second Brain AI systems are designed to be personal and private, learning from your information without sharing it or using it to benefit others.
     
    It’s like the difference between a personal diary and a public social media post. Your Second Brain AI is your private thinking space, designed to serve your goals and protect your information. You maintain complete control over what information it has access to and how it uses that information.
     
    For example, HP AI PCs are designed to streamline tasks, speed up workflows with AI data analysis, copy editing, and image creation, all while ensuring the security of on-device AI.

    The Learning Revolution

    Traditional learning is linear. If you read a book, take a course, or attend a lecture, then you try to remember and apply what you learned. Second Brain AI enables dynamic, contextual learning that adapts to your needs in real-time.
     
    Instead of trying to remember everything, you can focus on understanding concepts and making connections, knowing that your AI partner will help you recall specific details when needed. This shift from memorization to comprehension fundamentally changes how we approach learning and skill development.

    Building Your Second Brain

     The transition to working with AI thinking partners isn’t about adopting new technology — it’s about developing a new relationship with information and learning. It requires shifting from trying to remember everything to trusting that the right information will be available when needed.
     
    This transformation is already beginning. Early adopters are discovering that the most effective approach is gradual integration, starting with simple information capture and organization, then gradually expanding into more sophisticated AI-assisted thinking and decision-making.
     
    The current landscape offers multiple entry points that offer increasingly sophisticated ways to build interconnected knowledge networks that grow more valuable over time.

    The Future of Human Potential

    We’re entering an era where the limiting factor in human achievement won’t be our ability to access information or remember details — it will be our creativity, judgment, and ability to ask the right questions. Second Brain AI handles information processing, allowing us to focus on the uniquely human aspects of thinking and problem-solving.
     
    This partnership between human and artificial intelligence promises to unlock human potential in ways we’re only beginning to understand. We’ll be able to tackle more complex problems, make better decisions, and achieve goals that would have been impossible to work on alone.
     
    Will you be among the early adopters who shape this transformation or among those who struggle to adapt later?

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