Category Archives: Technology

4 Key Aspects of Robots Taking Our Jobs

GUEST POST from Greg Satell

A 2019 study by the Brookings Institution found that over 61% of jobs will be affected by automation. That comes on the heels of a 2017 report from the McKinsey Global Institute that found that 51% of total working hours and $2.7 trillion dollars in wages are highly susceptible to automation and a 2013 Oxford study that found 47% of jobs will be replaced.

The future looks pretty grim indeed until you start looking at jobs that have already been automated. Fly-by-wire was introduced in 1968, but today we’re facing a massive pilot shortage. The number of bank tellers has doubled since ATMs were introduced. Overall, the US is facing a massive labor shortage.

In fact, although the workforce has doubled since 1970, labor participation rates have risen by more than 10% since then. Everywhere you look, as automation increases, so does the demand for skilled humans. So the challenge ahead isn’t so much finding work for humans, but to prepare humans to do the types of work that will be in demand in the years to come.

1. Automation Doesn’t Replace Jobs, It Replaces Tasks

To understand the disconnect between all the studies that seem to be predicting the elimination of jobs and the increasingly dire labor shortage, it helps to look a little deeper at what those studies are actually measuring. The truth is that they don’t actually look at the rate of jobs being created or lost, but tasks that are being automated. That’s something very different.

To understand why, consider the legal industry, which is rapidly being automated. Basic activities like legal discovery are now largely done by algorithms. Services like LegalZoom automate basic filings. There are even artificial intelligence systems that can predict the outcome of a court case better than a human can.

So, it shouldn’t be surprising that many experts predict gloomy days ahead for lawyers. Yet the number of lawyers in the US has increased by 15% since 2008 and it’s not hard to see why. People don’t hire lawyers for their ability to hire cheap associates to do discovery, file basic documents or even, for the most part, to go to trial. In large part, they want someone they can trust to advise them.

In a similar way we don’t expect bank tellers to process transactions anymore, but to help us with things that we can’t do at an ATM. As the retail sector becomes more automated, demand for e-commerce workers is booming. Go to a highly automated Apple Store and you’ll find far more workers than at a traditional store, but we expect them to do more than just ring us up.

2. When Tasks Become Automated, The Become Commoditized

Let’s think back to what a traditional bank looked like before ATMs or the Internet. In a typical branch, you would see a long row of tellers there to process deposits and withdrawals. Often, especially on Fridays when workers typically got paid, you would expect to see long lines of people waiting to be served.

In those days, tellers needed to process transactions quickly or the people waiting in line would get annoyed. Good service was fast service. If a bank had slow tellers, people would leave and go to one where the lines moved faster. So training tellers to process transactions efficiently was a key competitive trait.

Today, however, nobody waits in line at the bank because processing transactions is highly automated. Our paychecks are usually sent electronically. We can pay bills online and get cash from an ATM. What’s more, these aren’t considered competitive traits, but commodity services. We expect them as a basic requisite of doing business.

In the same way, we don’t expect real estate agents to find us a house or travel agents to book us a flight or find us a hotel room. These are things that we used to happily pay for, but today we expect something more.

3. When Things Become Commodities, Value Shifts Elsewhere

In 1900, 30 million people in the United States were farmers, but by 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a manner of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Still, the twentieth century became an era of unprecedented prosperity.

We’re in the midst of a similar transformation today. Just as our ancestors toiled in the fields, many of us today spend much of our time doing rote, routine tasks. However, as two economists from MIT explain in a paper, the jobs of the future are not white collar or blue collar, but those focused on non-routine tasks, especially those that involve other humans.

Consider the case of bookstores. Clearly, by automating the book buying process, Amazon disrupted superstore book retailers like Barnes & Noble and Borders. Borders filed for bankruptcy in 2011 and was liquidated later that same year. Barnes & Noble managed to survive but has been declining for years.

Yet a study at Harvard Business School found that small independent bookstores are thriving by adding value elsewhere, such as providing community events, curating titles and offering personal recommendations to customers. These are things that are hard to do well at a big box retailer and virtually impossible to do online.

4. Value Is Shifting from Cognitive Skills to Social Skills

20 or 30 years ago, the world was very different. High value work generally involved retaining information and manipulating numbers. Perhaps not surprisingly, education and corporate training programs were focused on teaching those skills and people would build their careers on performing well on knowledge and quantitative tasks.

Today, however, an average teenager has more access to information and computing power than a typical large enterprise had a generation ago, so knowledge retention and quantitative ability have largely been automated and devalued. High value work has shifted from cognitive skills to social skills.

Consider that the journal Nature has found that the average scientific paper today has four times as many authors as one did in 1950, and the work they are doing is far more interdisciplinary and done at greater distances than in the past. So even in highly technical areas, the ability to communicate and collaborate effectively is becoming an important skill.

There are some things that a machine will never do. Machines will never strike out at a Little League game, have their hearts broken or see their children born. That makes it difficult, if not impossible, for machines to relate to humans as well as a human can. The future of work is humans collaborating with other humans to design work for machines.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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3 Steps to Find the Horse’s A** In Your Company (and Create Space for Innovation)

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3 Steps to Find the Horse's A** In Your Company (and Create Space for Innovation)

GUEST POST from Robyn Bolton

Innovation thrives within constraints.

Constraints create the need for questions, creative thinking, and experiments.

But as real as constraints are and as helpful as they can be, don’t simply accept them. Instead, question them, push on them, and explore around them.

But first, find the horse’s a**

How Ancient Rome influenced the design of the Space Shuttle

In 1974, Thiokol, an aerospace and chemical manufacturing company, won the contract to build the solid rocket boosters (SRBs) for the Space Shuttle. The SRBs were to be built in a factory in Utah and transported to the launch site via train.

The train route ran through a mountain tunnel that was just barely wider than the tracks.

The standard width of railroad tracks (distance between the rails or the railroad gauge) in the US is 4 feet, 8.5 inches which means that Thiokol’s engineers needed to design SRBs that could fit through a tunnel that was slightly wider than 4 feet 8.5 inches.

4 feet 8.5 inches wide is a constraint. But where did such an oddly specific constraint come from?

The designers and builders of America’s first railroads were the same people and companies that built England’s tramways. Using the existing tramways tools and equipment to build railroads was more efficient and cost-effective, so railroads ended up with the same gauge as tramways – 4 feet 8.5 inches.

The designers and builders of England’s tramways were the same businesses that, for centuries, built wagons. Wanting to use their existing tools and equipment (it was more efficient and cost-effective, after all), the wagon builders built tramways with the exact distance between the rails as wagons had between wheels – 4 feet 8.5 inches.

Wagon wheels were 4 feet 8.5 inches apart to fit into the well-worn grooves in most old European roads. The Romans built those roads, and Roman chariots made those grooves, and a horses pulled those chariots, and the width of a horses was, you guessed it, 4 feet 8.5 inches.

To recap – the width of a horses’ a** (approximately 4 feet 8.5 inches) determined the distance between wheels on the Roman chariots that wore grooves into ancient roads. Those grooves ultimately dictated the width of wagon wheels, tramways, railroad ties, a mountain tunnel, and the Space Shuttle’s SRBs.

How to find the horse’s a**

When you understand the origin of a constraint, aka find the horse’s a**, it’s easier to find ways around it or to accept and work with it. You can also suddenly understand and even anticipate people’s reactions when you challenge the constraints.

Here’s how you do it – when someone offers a constraint:

Thank them for being honest with you and for helping you work more efficiently
Find the horse’s a** by asking questions to understand the constraint – why it exists, what it protects, the risk of ignoring it, who enforces it, and what happened to the last person who challenged it.
Find your degrees of freedom by paying attention to their answers and how they give them. Do they roll their eyes in knowing exasperation? Shrug their shoulders in resignation? Become animated and dogmatic, agitated that someone would question something so obvious?

How to use the horse’s a** to innovate

You must do all three steps because stopping short of step 3 stops creativity in its tracks.

If you stop after Step 1 (which most people do), you only know the constraint, and you’ll probably be tempted to take it as fixed. But maybe it’s not. Perhaps it’s just a habit or heuristic waiting to be challenged.

If you do all three steps, however, you learn tons of information about the constraint, how people feel about it, and the data and evidence that could nudge or even eliminate it.

At the very least, you’ll understand the horse’s a** driving your company’s decisions.

Image credit: Pixabay

Endnotes:

To be very clear, the origin of the constraint is the horse’s a**. The person telling you about the constraint is NOT the horse’s a**.
The truth is never as simple as the story and railroads used to come in different gauges. For a deeper dive into this “more true than not” story (and an alternative theory that it was the North’s triumph in the Civil War that influenced the design of the SRBs, click here

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Top 5 Tech Trends Artificial Intelligence is Monitoring

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Top 5 Tech Trends Artificial Intelligence is Monitoring

GUEST POST from Art Inteligencia

Artificial Intelligence is constantly scanning the Internet to identify the technology trends that are the most interesting and potentially the most impactful. At present, according to artificial intelligence, the Top Five Technology Trends being tracked for futurology are:

1. Artificial Intelligence (AI): Artificial Intelligence is the development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.

2. Autonomous Vehicles: Autonomous vehicles are vehicles that can navigate without human input, relying instead on sensors, GPS, and computer technology to determine their location and trajectory. Autonomous vehicles are used in a variety of applications, from consumer transportation to military drones.

3. Virtual Reality (VR): Virtual reality is a computer-generated simulation of a three-dimensional environment that can be interacted with in a seemingly real or physical way by a person using special electronic equipment. VR uses technologies such as gesture control and stereoscopic displays to create immersive experiences for the user.

4. Augmented Reality (AR): Augmented reality is a technology that superimposes computer-generated content onto the real world to enhance or supplement a user’s physical experience. AR is used in a variety of contexts, from gaming to industrial design.

5. Internet of Things (IoT): The Internet of Things is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, and connectivity that enable these objects to connect and exchange data. The IoT has the potential to revolutionize many aspects of our lives, from manufacturing and transportation to healthcare and energy management.

It’s obviously amusing that artificial intelligence considers artificial intelligence to be the number one technology trend at present in its futurology work. I would personally rank it number one, but I would rank autonomous vehicles and virtual reality lower. I would put augmented reality and IoT number two and number three respectively, but what do I know …

Image credit: Pixabay

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What Artificial Intelligence Predicts for 2023

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What Artificial Intelligence Predicts for 2023

GUEST POST from Art Inteligencia

As we move into 2023 and beyond, the technology industry is making predictions about what the future of innovation holds for us. With the global pandemic accelerating the rate of digital transformation, it’s safe to say that the next few years will bring some major changes to the way we work and live. Here are some of the top innovation predictions generated by artificial intelligence for 2023:

1. Autonomous Delivery: Autonomous delivery systems are becoming more commonplace, and by 2023, we expect to see them become even more advanced. Autonomous delivery systems use advanced robotics and artificial intelligence to deliver packages to customers without the need for human involvement. This could significantly reduce costs and create greater efficiency in delivery services.

2. Augmented Reality: Augmented reality (AR) is rapidly growing in popularity and it’s expected to become even more pervasive by 2023. AR will be used in many industries, including education, healthcare and retail, to create interactive experiences. For example, in healthcare, AR can be used to provide surgeons with enhanced visuals during operations. In retail, AR can be used to give customers a more immersive shopping experience.

3. Quantum Computing: Quantum computing is a form of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform calculations. This form of computing has the potential to revolutionize the way we process and store data, and it’s expected to become more mainstream by 2023.

4. 5G Networks: The fifth generation of cellular networks, also known as 5G, is expected to become even more widespread by 2023. 5G networks have faster connection speeds, lower latency and greater reliability than their predecessors, which makes them ideal for a variety of applications, including autonomous vehicles, virtual reality and the Internet of Things.

5. Artificial Intelligence: Artificial intelligence (AI) is becoming increasingly prevalent in our lives. By 2023, we expect to see AI being used in a variety of applications, including automated customer service, natural language processing and personal assistants. AI has the potential to revolutionize the way we interact with technology and the world around us.

These are just a few of the many predictions for 2023 and beyond. As digital transformation continues to accelerate, we can expect to see even more innovation over the next few years. It’s an exciting time to be in the technology industry and we can’t wait to see what the future holds.

Image credit: Pixabay

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Shark Tanks are the Pumpkin Spice of Innovation

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Shark Tanks are the Pumpkin Spice of Innovation

GUEST POST from Robyn Bolton

On August 27, Pumpkin Spice season began. It was the earliest ever launch of Starbucks’ Pumpkin Spice Latte and it kicked off a season in which everything from Cheerios to protein powder to dog shampoo promises the nostalgia of Grandma’s pumpkin pie.

Since its introduction in 2003, the Pumpkin Spice Latte has attracted its share of lovers and haters but, because it’s a seasonal offering, the hype fades almost as soon as it appears.

Sadly, the same cannot be said for its counterpart in corporate innovation — The Shark Tank/Hackathon/Lab Week.

It may seem unfair to declare Shark Tanks the Pumpkin Spice of corporate innovation, but consider the following:

They are events. There’s nothing wrong with seasonal flavors and events. After all, they create a sense of scarcity that spurs people to action and drives companies’ revenues. However, there IS a great deal wrong with believing that innovation is an event. Real innovation is not an event. It is a way of thinking and problem-solving, a habit of asking questions and seeking to do things better, and of doing the hard and unglamorous work of creating, learning, iterating, and testing required to bring innovation — something different that creates value — to life.
They appeal to our sense of nostalgia and connection. The smell and taste of Pumpkin Spice bring us back to simpler times, holidays with family, pie fresh and hot from the oven. Shark Tanks do the same. They remind us of the days when we believed that we could change the world (or at least fix our employers) and when we collaborated instead of competed. We feel warm fuzzies as we consume (or participate in) them, but the feelings are fleeting, and we return quickly to the real world.
They pretend to be something they’re not. Starbucks’ original Pumpkin Spice Latte was flavored by cinnamon, nutmeg, and clove. There was no pumpkin in the Pumpkin Spice. Similarly, Shark Tanks are innovation theater — events that give people an outlet for their ideas and an opportunity to feel innovation-y for a period of time before returning to their day-to-day work. The value that is created is a temporary blip, not lasting change that delivers real business value.

But it doesn’t have to be this way.

If you’re serious about walking the innovation talk, Shark Tanks can be a great way to initiate and accelerate building a culture and practice of innovation. But they must be developed and deployed in a thoughtful way that is consistent with your organization’s strategy and priorities.

Make Shark Tanks the START of an innovation effort, not a standalone event. Clearly establish the problems or organizational priorities you want participants to solve and the on-going investment (including dedicated time) that the company will make in the winners. Allocate an Executive Sponsor who meets with the team monthly and distribute quarterly updates to the company to share winners’ progress and learnings
Act with courage and commitment. Go beyond the innovation warm fuzzies and encourage people to push the boundaries of “what we usually do.” Reward and highlight participants that make courageous (i.e. risky) recommendations. Pursue ideas that feel a little uncomfortable because the best way to do something new that creates value (i.e. innovate) is to actually DO something NEW.
Develop a portfolio of innovation structures: Just as most companies use a portfolio of tools to grow their core businesses, they need a portfolio of tools to create new businesses. Use Shark Tanks to the surface and develop core or adjacent innovation AND establish incubators and accelerators to create and test radical innovations and business models AND fund a corporate VC to scout for new technologies and start-ups that can provide instant access to new markets.

Conclusion

Whether you love or hate Pumpkin Spice Lattes you can’t deny their impact. They are, after all, Starbucks’ highest-selling seasonal offering. But it’s hard to deny that they are increasingly the subject of mocking memes and eye rolls, a sign that their days, and value, maybe limited.

(Most) innovation events, like Pumpkin Spice, have a temporary effect. But not on the bottom-line. During these events, morale, and team energy spike. But, as the excitement fades and people realize that nothing happened once the event was over, innovation becomes a meaningless buzzword, evoking eye rolls and Dilbert cartoons.

Avoid this fate by making Shark Tanks a lasting part of your innovation menu — a portfolio of tools and structures that build and sustain a culture and practice of innovation, one that creates real financial and organizational value.

Image credit: Unsplash

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Crabby Innovation Opportunity

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Crabby Innovation Opportunity

There are many foods that we no longer eat, but because we choose to, not because they have disappeared from nature. In fact, here is a list of 21 Once-Popular Foods That We All Stopped Eating, including:

Kool-Aid
Margarine
Pudding Pops
Candy Cigarettes
etc.

But today, we’re going to talk about a food that I personally love, but that I’ve always viewed as a bit of luxury – crab legs – that is in danger of disappearing off the face of the planet due to climate change and human effects. And we’re not just talking about King Crab, but we’re also talking about Snow Crab, and we’re talking about Dungeness Crab too. And this is a catastrophe not just for diners, but to an entire industry and the livelihood of too many families to count:

2022-2023 King Crab fishing season will officially be closed in the Bering Sea for the second year in a row

Alaskan Snow Crab Season Canceled After 90% Of Population Disappears

Dungeness crab die-off underway along US West Coast

That’s more than a BILLION CRABS that none of us have had the pleasure of their deliciousness.

And given the magnitude of the die off, it is possible they might disappear completely, meaning we can’t enjoy and salivate at the thought of this popular commercial from the 80’s:

Climate change and global warming are real. If you don’t believe humans are the cause, that it’s naturally occurring, fine, it’s still happening.

There can be no debate other than surrounding the actions we take from this point forward.

And while the magnitude of the devastation of other animal species that humans are responsible for is debatable, we are failing in our duties as caretakers of the earth.

This brings me back to the title of the post and the missions of this blog – to promote human-centered change and innovation.

Because we have killed off one of our very tastiest treats (King, Snow and Dungeness Crabs), at least in the short-term (and possibly forever), there is a huge opportunity to do better than krab sticks or the Krabby Patties of SpongeBob SquarePants fame.

If crab legs are going to disappear from the menus of seafood restaurants across the United States, and possibly the world, can someone invent a tasty treat that equals or exceeds the satisfaction of wielding a crab cracker and a crab fork and extracting the white gold within to dip into some sweet and slippery lemon butter?

Who is going to be first to crack this problem?

Or who will be the first to find a way to bring the crabs back from extinction?

We’re not just talking about a food to fill our bellies with, we’re talking about a pleasurable dining experience that is going away – that I know someone can save!

And no Air Protein marketing gimmicks please!

Image credit: Northsea.sg

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Corporate Venturing as a Catalyst for Innovation

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Venture Beyond

Corporate Venturing as a Catalyst for Innovation

GUEST POST from Art Inteligencia

In today’s rapidly evolving business landscape, the pursuit of innovation is no longer optional; it’s existential. Yet, many large, established corporations struggle to innovate at the pace of the market. Internal bureaucracy, risk aversion, and a focus on incremental improvements can stifle the disruptive thinking required for true transformation. As a human-centered change and innovation thought leader, I am here to argue that one of the most powerful and underutilized strategies for overcoming this inertia is corporate venturing. This isn’t just about investing money; it’s about strategically engaging with the startup ecosystem to ignite new growth, access frontier technologies, and inject a vital dose of entrepreneurial DNA into the heart of your organization. Corporate venturing is a deliberate act of looking beyond your walls to find the future.

Corporate venturing encompasses a range of activities, from direct venture capital investments (Corporate Venture Capital or CVC) to incubation programs, accelerators, and strategic partnerships with startups. Its core purpose is to bridge the innovation gap between the agile, disruptive startup world and the established, resource-rich corporate entity. This symbiotic relationship offers startups access to capital, market reach, and mentorship, while providing corporations with a window into emerging technologies, new business models, and fresh talent. More importantly, it acts as an external nervous system for innovation, allowing the corporation to sense, adapt, and respond to market shifts with a speed that internal R&D often cannot match. It’s a human-centered approach to expanding your innovation capacity, leveraging the entrepreneurial spirit that often flourishes outside traditional corporate structures.

The Strategic Imperatives of Corporate Venturing

To truly leverage corporate venturing as a catalyst for innovation, it must be approached with strategic intent, not just as a financial play. Here are four key imperatives:

1. Strategic Alignment, Not Just Financial Return: While financial returns are welcome, the primary driver for corporate venturing should be strategic. How does this investment or partnership align with your long-term vision? Does it open up new markets, provide access to critical technologies, or deepen your understanding of future customer needs?
2. Active Engagement, Beyond Capital: Successful corporate venturing is not passive. It requires active mentorship, resource sharing, and a genuine effort to integrate lessons learned from startups back into the core business. It’s a two-way street of learning and collaboration.
3. Build Bridges, Not Walls: The biggest challenge is often integrating the fast-paced startup mentality with the established corporate culture. Dedicated venturing units should act as translators, bridging the gap between the two worlds and fostering mutual understanding and respect.
4. Portfolio Thinking and Experimentation: Treat your venture portfolio like an experimental lab. Not every investment will succeed, but each provides valuable learning. Diversify your bets across different technologies, markets, and business models to hedge against uncertainty and maximize discovery.

“Don’t just acquire the future; invest in building it. Corporate venturing is your strategic lens into tomorrow’s disruption and market expansion.” — Braden Kelley

Case Study 1: Google Ventures (GV) – Investing in the Adjacent Future

The Challenge:

Google, despite its massive internal R&D capabilities, recognized that innovation often happens at the edges of an industry, driven by small, agile teams. The challenge was to systematically identify and invest in groundbreaking startups that could either complement Google’s core business or open up entirely new growth areas, without stifling their entrepreneurial spirit with corporate bureaucracy.

The Corporate Venturing Solution:

Google established Google Ventures (GV) as its venture capital arm. Unlike traditional corporate VCs, GV operates with a high degree of autonomy, investing in a broad range of technology companies, many of which are not directly related to Google’s immediate product lines. However, the strategic alignment is clear: GV invests in areas that represent the adjacent future of technology—AI, life sciences, consumer tech, enterprise software—giving Google an early window into the next wave of disruption. GV provides more than just capital; it offers startups access to Google’s unparalleled expertise in engineering, design, and marketing through its “GV Experts” program.

Strategic Alignment: GV’s investments provide Google with intelligence on emerging technologies and market shifts that could impact its long-term strategy.
Active Engagement: The “GV Experts” program offers invaluable operational support, helping startups scale and overcome technical challenges.
Autonomy and Agility: By operating somewhat independently, GV avoids many of the bureaucratic pitfalls that can slow down corporate innovation efforts.

The Result:

GV has been incredibly successful, with a portfolio that includes major companies like Uber, Slack, and Nest (which Google later acquired). These investments provide significant financial returns, but more importantly, they offer Google a strategic vantage point. It allows them to understand and even influence future technological trajectories, keeping the parent company at the forefront of innovation. GV demonstrates how a well-structured CVC can act as a crucial early warning system and growth engine for a tech giant.

Case Study 2: BMW i Ventures – Driving Future Mobility

The Challenge:

The automotive industry is facing unprecedented disruption, driven by trends like electrification, autonomous driving, shared mobility, and connected vehicles. BMW, a legacy automaker, needed to rapidly adapt and innovate beyond its traditional car manufacturing core to secure its position in the future of mobility. Relying solely on internal R&D would be too slow and limited in scope.

The Corporate Venturing Solution:

BMW established BMW i Ventures, a corporate venture capital fund dedicated to investing in early- to mid-stage startups in the mobility, digital, and sustainability sectors. The fund strategically targets companies developing cutting-edge technologies and services that could shape the future of transportation and enhance the overall customer experience. This includes areas like advanced materials, AI for autonomous systems, smart charging solutions, and innovative digital services for car ownership or sharing. BMW i Ventures provides capital, but also offers strategic partnerships, pilot opportunities within BMW’s ecosystem, and valuable market insights.

Strategic Alignment: Every investment is directly tied to BMW’s long-term vision for sustainable, intelligent, and human-centered mobility.
Access to Frontier Tech: The fund provides early access to technologies that might take years or decades to develop internally, accelerating BMW’s innovation timeline.
New Business Models: Investments in areas like shared mobility or digital services help BMW explore and validate entirely new revenue streams beyond traditional car sales.

The Result:

BMW i Ventures has allowed the company to stay ahead of the curve in a rapidly changing industry. It has fostered collaborations with innovative startups, informed BMW’s internal product roadmaps, and positioned the brand as a leader in future mobility solutions. By strategically venturing beyond its core business, BMW has gained agility, expanded its innovation ecosystem, and proactively secured its relevance in the coming decades.

Conclusion: The Future of Innovation is Open

Corporate venturing is more than just a financial vehicle; it is a mindset—an acknowledgment that the most profound innovations often emerge from outside your established walls. It’s a strategic embrace of openness, agility, and the entrepreneurial spirit. For large corporations, it represents a vital pathway to overcome internal inertia, access game-changing technologies, and build a more resilient and future-ready organization.

As leaders, our challenge is to move beyond short-term thinking and embrace a portfolio approach to innovation. By strategically venturing into the unknown, by actively engaging with the disruptors, and by fostering a culture that learns from both successes and failures, we can unlock unprecedented growth and ensure our organizations are not just prepared for the future, but actively shaping it.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pexels

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AI-Powered Foresight

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Predicting Trends and Uncovering New Opportunities

AI-Powered Foresight

GUEST POST from Chateau G Pato

In a world of accelerating change, the ability to see around corners is no longer a luxury; it’s a strategic imperative. For decades, organizations have relied on traditional market research, analyst reports, and expert intuition to predict the future. While these methods provide a solid view of the present and the immediate horizon, they often struggle to detect the faint, yet potent, signals of a more distant future. As a human-centered change and innovation thought leader, I believe that **Artificial Intelligence is the most powerful new tool for foresight**. AI is not here to replace human intuition, but to act as a powerful extension of it, allowing us to process vast amounts of data and uncover patterns that are invisible to the human eye. The future of innovation isn’t about predicting what’s next; it’s about systematically sensing and shaping what’s possible. AI is the engine that makes this possible.

The human brain is a marvel of pattern recognition, but it is limited by its own biases, a finite amount of processing power, and the sheer volume of information available today. AI, however, thrives in this chaos. It can ingest and analyze billions of data points—from consumer sentiment on social media, to patent filings, to macroeconomic indicators—in a fraction of the time. It can identify subtle correlations and weak signals that, when combined, point to a major market shift years before it becomes a mainstream trend. By leveraging AI for foresight, we can move from a reactive position to a proactive one, turning our organizations from followers into first-movers.

The AI Foresight Blueprint

Leveraging AI for foresight isn’t a one-and-done task; it’s a continuous, dynamic process. Here’s a blueprint for how organizations can implement it:

Data-Driven Horizon Scanning: Use AI to continuously monitor a wide range of data sources, from academic papers and startup funding rounds to online forums and cultural movements. An AI can flag anomalies and emerging clusters of activity that fall outside of your industry’s current focus.
Pattern Recognition & Trend Identification: AI models can connect seemingly unrelated data points to identify nascent trends. For example, an AI might link a rise in plant-based food searches to an increase in sustainable packaging patents and a surge in home gardening interest, pointing to a larger “Conscious Consumer” trend.
Scenario Generation: Once a trend is identified, an AI can help generate multiple future scenarios. By varying key variables—e.g., “What if the trend accelerates rapidly?” or “What if a major competitor enters the market?”—an AI can help teams visualize and prepare for a range of possible futures.
Opportunity Mapping: AI can go beyond trend prediction to identify specific market opportunities. It can analyze the intersection of an emerging trend with a known customer pain point, generating a list of potential product or service concepts that address an unmet need.

“AI for foresight isn’t about getting a crystal ball; it’s about building a powerful telescope to see what’s on the horizon and a microscope to see what’s hidden in the data.”

Case Study 1: Stitch Fix – Algorithmic Personal Styling

The Challenge:

In the crowded and highly subjective world of fashion retail, predicting what a single customer will want to wear—let alone an entire market segment—is a monumental challenge. Traditional methods relied on seasonal buying patterns and the intuition of human stylists. This often led to excess inventory and a high rate of returns.

The AI-Powered Foresight Response:

Stitch Fix, the online personal styling service, built its entire business model on AI-powered foresight. The company’s core innovation was not in fashion, but in its algorithm. The AI ingests data from every single customer interaction—what they kept, what they returned, their style feedback, and even their Pinterest boards. This data is then cross-referenced with a vast inventory and emerging fashion trends. The AI can then:

Predict Individual Preference: The algorithm learns each customer’s taste over time, predicting with high accuracy which items they will like. This is a form of micro-foresight.
Uncover Macro-Trends: By analyzing thousands of data points across its customer base, the AI can detect emerging fashion trends long before they hit the mainstream. For example, it might notice a subtle shift in the popularity of a certain color, fabric, or cut among its early adopters.

The Result:

Stitch Fix’s AI-driven foresight has allowed them to operate with a level of efficiency and personalization that is nearly impossible for traditional retailers to replicate. By predicting consumer demand, they can optimize their inventory, reduce waste, and provide a highly-tailored customer experience. The AI doesn’t just help them sell clothes; it gives them a real-time, data-backed view of future consumer behavior, making them a leader in a fast-moving and unpredictable industry.

Case Study 2: Netflix – The Algorithm That Sees the Future of Entertainment

The Challenge:

In the early days of streaming, content production was a highly risky and expensive gamble. Studios would greenlight shows based on the intuition of executives, focus group data, and the past success of a director or actor. This process was slow and often led to costly failures.

The AI-Powered Foresight Response:

Netflix, a pioneer of AI-powered foresight, revolutionized this model. They used their massive trove of user data—what people watched, when they watched it, what they re-watched, and what they skipped—to predict not just what their customers wanted to watch, but what kind of content would be successful to produce. When they decided to create their first original series, House of Cards, they didn’t do so on a hunch. Their AI analyzed that a significant segment of their audience had a high affinity for the original British series, enjoyed films starring Kevin Spacey, and had a preference for political thrillers directed by David Fincher. The AI identified the convergence of these three seemingly unrelated data points as a major opportunity.

Predictive Content Creation: The algorithm predicted that a show with these specific attributes would have a high probability of success, a hypothesis that was proven correct.
Cross-Genre Insight: The AI’s ability to see patterns across genres and user demographics allowed Netflix to move beyond traditional content silos and identify new, commercially viable niches.

The Result:

Netflix’s success with House of Cards was a watershed moment that proved the power of AI-powered foresight. By using data to inform its creative decisions, Netflix was able to move from a content distributor to a powerful content creator. The company now uses AI to inform everything from production budgets to marketing campaigns, transforming the entire entertainment industry and proving that a data-driven approach to creativity is not only possible but incredibly profitable. Their foresight wasn’t a lucky guess; it was a systematic, AI-powered process.

Conclusion: The Augmented Innovator

The era of “gut-feel” innovation is drawing to a close. The most successful organizations of the future will be those that have embraced a new model of augmented foresight, where human intuition and AI’s analytical power work in harmony. AI can provide the objective, data-backed foundation for our predictions, but it is up to us, as human leaders, to provide the empathy, creativity, and ethical judgment to turn those predictions into a better future.

AI is not here to tell you what to do; it’s here to show you what’s possible. Our role is to ask the right questions, to lead with a strong sense of purpose, and to have the courage to act on the opportunities that AI uncovers. By training our teams to listen to the whispers in the data and to trust in this new collaborative process, we can move from simply reacting to the future to actively creating it, one powerful insight at a time.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Microsoft CoPilot

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How AI is Reshaping Brainstorming

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The Future of Ideation

How AI is Reshaping Brainstorming

GUEST POST from Chateau G Pato

For decades, the classic brainstorming session has been the centerpiece of innovation. A whiteboard, a room full of energetic people, and a flow of ideas, from the brilliant to the absurd. The goal was simple: quantity over quality, and to build on each other’s thoughts. However, as a human-centered change and innovation thought leader, I’ve come to believe that this traditional model, while valuable, is fundamentally limited. It’s often hindered by groupthink, a fear of judgment, and the cognitive biases of the participants. Enter Artificial Intelligence. AI is not here to replace human ideation, but to act as the ultimate co-pilot, fundamentally reshaping brainstorming by making it more data-driven, more diverse, and more powerful than ever before. The future of ideation is not human or AI; it’s human-plus-AI.

Generative AI, in particular, has a unique ability to break us out of our mental ruts. It can process vast amounts of data—market trends, scientific research, customer feedback, and design patterns—and instantly synthesize them into novel combinations that a human team might never consider. It can challenge our assumptions, expose our blind spots, and provide a constant, unbiased source of inspiration. By offloading the “heavy lifting” of data synthesis and initial idea generation to an AI, human teams are freed up to focus on what they do best: empathy, intuition, ethical consideration, and the strategic refinement of an idea. This isn’t just a new tool; it’s a new paradigm for creative collaboration.

The AI-Powered Ideation Blueprint

Here’s how AI can revolutionize the traditional brainstorming session, transforming it into a dynamic, data-rich experience:

Pre-Brainstorming Research & Synthesis: Before the team even enters the room, an AI can be tasked with a prompt: “Analyze the top customer complaints for Product X, cross-reference them with emerging technologies in the field, and generate 50 potential solutions.” This provides a rich, data-backed foundation for the session, eliminating the “blank page” syndrome.
Bias-Free Idea Generation: AI doesn’t have a boss to impress or a fear of sounding foolish. It can generate a wide range of ideas, including those that are counterintuitive or seem to come from left field. This helps to overcome groupthink and encourages more divergent thinking from the human participants.
Real-Time Augmentation: During a live session, an AI can act as an instant research assistant. A team member might suggest an idea, and a quick query to the AI can provide immediate data on its feasibility, market precedents, or potential risks. This allows for a more informed and efficient discussion.
Automated Idea Clustering & Analysis: After the session, an AI can quickly analyze all the generated ideas, clustering them by theme, identifying unique concepts, and even flagging potential synergies that humans might have missed. This saves countless hours of manual post-it note organization and analysis.
Prototyping & Visualization: With the right tools, a team can go from a text prompt idea to a basic visual prototype in minutes. An AI can generate mockups, logos, or even simple user interfaces, making abstract ideas tangible and easy to evaluate.

“AI isn’t the brain in the room; it’s the nervous system, connecting every thought to a universe of data and possibility.”

Case Study 1: Adobe’s Sensei & The Future of Creative Ideation

The Challenge:

Creative professionals—designers, marketers, photographers—often face creative blocks or repetitive tasks that slow down their ideation process. Sifting through stock photos, creating design variations, or ensuring brand consistency for thousands of assets can be a time-consuming and manual process, leaving less time for truly creative, breakthrough thinking.

The AI-Powered Solution:

Adobe, a leader in creative software, developed Adobe Sensei, an AI and machine learning framework integrated into its Creative Cloud applications. Sensei is not a tool for generating an entire masterpiece; rather, it’s a co-pilot for ideation and creative execution. For example, a designer can provide a few images and a text prompt to Sensei, and it can generate dozens of logo variations, color palettes, or photo compositions in seconds. In another example, its content-aware fill can instantly remove an object from a photo and seamlessly fill in the background, a task that used to take hours of manual work.

Accelerated Exploration: Sensei’s generative capabilities allow designers to explore a vast “idea space” much faster than they could on their own, finding new and unexpected starting points.
Automation of Repetitive Tasks: By handling the tedious, low-creativity tasks, Sensei frees up the human designer to focus on the higher-level strategic and aesthetic decisions.
Enhanced Personalization: The AI can analyze a user’s style and past work to provide more personalized and relevant suggestions, making the collaboration feel seamless and intuitive.

The Result:

Adobe’s integration of AI hasn’t replaced creative jobs; it has transformed them. By accelerating the ideation and creation process, it has empowered creative professionals to be more prolific, experiment with more ideas, and focus their energy on the truly unique and human-centric aspects of their work. The AI becomes a silent, tireless brainstorming partner, pushing creative teams beyond their comfort zones and into new territories of possibility.

Case Study 2: Generative AI in Drug Discovery (Google’s DeepMind & Isomorphic Labs)

The Challenge:

The ideation process in drug discovery is one of the most complex and time-consuming in the world. Identifying potential drug candidates—novel molecular structures that can bind to a specific protein—is a task that traditionally requires years of laboratory experimentation and millions of dollars. The number of possible molecular combinations is astronomically large, making it impossible for human scientists to explore more than a tiny fraction.

The AI-Powered Solution:

Google’s DeepMind, through its groundbreaking AlphaFold AI model, has fundamentally changed the ideation phase of drug discovery. AlphaFold can accurately predict the 3D structure of proteins, a problem that had stumped scientists for decades. Building on this, Google launched Isomorphic Labs, a company that uses AI to accelerate drug discovery. Their models can now perform “in-silico” (computer-based) ideation, generating and testing millions of potential molecular structures to find those most likely to bind with a target protein.

Exponential Ideation: The AI can explore a chemical idea space that is orders of magnitude larger than what a human team or even a traditional lab could ever hope to.
Rapid Validation: The AI can predict the viability of a molecule almost instantly, saving years of physical lab work on dead-end ideas.
New Hypotheses: The AI can propose novel molecular structures and design principles that are outside the conventional thinking of human chemists, leading to breakthrough hypotheses.

The Result:

By using AI for the ideation phase of drug discovery, companies are drastically reducing the time and cost it takes to find promising drug candidates. The human scientist is not replaced; they are empowered. They can now focus on the higher-level strategy, the ethical implications, and the final verification of a drug, while the AI handles the tireless and rapid-fire brainstorming of molecular possibilities. This is a perfect example of how AI can move an entire industry from incremental innovation to truly transformative, world-changing breakthroughs.

Conclusion: The Human-AI Innovation Symbiosis

The future of ideation is a collaboration, a symbiosis between human creativity and artificial intelligence. The most innovative organizations will be those that view AI not as a threat to human ingenuity, but as a powerful amplifier of it. By leveraging AI to handle the data crunching, the pattern recognition, and the initial idea generation, we free our teams to focus on what truly matters: asking the right questions, applying empathy to solve human problems, and making the final strategic and ethical decisions.

As leaders, our challenge is to move beyond the fear of automation and embrace the promise of augmentation. It’s time to build a new kind of brainstorming room—one with a whiteboard, a team of passionate innovators, and a smart, tireless AI co-pilot ready to turn our greatest challenges into an infinite number of possibilities. The era of the augmented innovator has arrived, and the future of great ideas is here.

Image credit: Pixabay

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Accelerating Innovation Cycles with AI

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From Idea to Impact

Accelerating Innovation Cycles with AI

GUEST POST from Chateau G Pato

The innovation landscape has always been a race against time. Ideas are plentiful, but transforming them into tangible impact—a new product, an optimized process, a groundbreaking service—often involves arduous cycles of research, development, testing, and refinement. In today’s hyper-competitive, human-centered world, this pace is simply no longer sufficient. As a thought leader in change and innovation, I believe the single most powerful accelerator for these cycles is Artificial Intelligence. AI isn’t just a tool; it’s a paradigm shift, enabling us to move from nascent concepts to measurable outcomes with unprecedented speed and precision.

For too long, the innovation journey has been characterized by bottlenecks: manual data analysis, slow prototyping, biased feedback interpretation, and iterative development that could stretch for months or even years. AI offers a compelling antidote to these challenges, supercharging every phase of the innovation process. It’s about augmenting human creativity and insight, not replacing it, allowing our teams to focus on the truly strategic and empathetic aspects of innovation while AI handles the heavy lifting of data crunching, pattern recognition, and rapid iteration.

The AI Accelerator: How AI Transforms Each Stage of Innovation

The true power of AI in innovation lies in its ability to enhance and speed up various stages of the innovation cycle:

Discovery & Ideation: AI can rapidly analyze vast datasets—market trends, customer feedback, scientific research, patent databases—to identify emerging white spaces, unmet needs, and potential synergies that human teams might miss. Generative AI can even assist in brainstorming novel concepts, providing diverse starting points for human ingenuity.
Concept Development & Prototyping: AI-powered design tools can generate multiple design variations based on specified parameters, simulate performance, and even create virtual prototypes in a fraction of the time it would take human designers. This allows for faster testing of diverse ideas.
Validation & Testing: Predictive AI models can forecast market reception for new products or features by analyzing historical data and customer behavior, reducing the need for extensive, costly live testing. AI can also analyze user feedback (sentiment analysis) from early tests to quickly identify areas for improvement.
Optimization & Launch: AI can optimize product features, pricing strategies, and marketing campaigns in real-time, learning from live data to maximize impact post-launch. For internal process innovations, AI can identify inefficiencies and suggest optimal workflows.
Learning & Iteration: Post-launch, AI continuously monitors performance, identifies emerging patterns in customer usage, and suggests further improvements or next-gen features, effectively creating a perpetual feedback loop for continuous innovation.

“AI doesn’t just speed up innovation; it fundamentally redefines the possible, turning months into days and guesses into data-driven insights.”

Human-Centered AI for Innovation: A Crucial Distinction

It’s vital to emphasize that integrating AI into innovation must remain human-centered. The goal is not to automate innovation away from people, but to empower people to innovate better, faster, and with greater impact. AI should serve as an invaluable co-pilot, handling the computational burden so that human teams can focus on:

Empathy and Understanding: Interpreting the emotional nuances of customer needs that AI cannot grasp.
Strategic Vision: Setting the direction, defining the ethical guardrails, and making the ultimate strategic decisions.
Creative Problem-Solving: Leveraging AI’s insights to spark truly original, human-relevant solutions.

Case Study 1: Pharma Research Acceleration with AI (BenevolentAI)

The Challenge:

Drug discovery is notoriously slow, expensive, and high-risk. Identifying potential drug candidates for specific diseases often takes years of laborious research, involving sifting through vast amounts of scientific literature and conducting countless lab experiments. The human-driven cycle from initial idea to clinical trial could span a decade or more.

AI as an Accelerator:

BenevolentAI, a leading AI drug discovery company, uses its platform to accelerate this process dramatically. Their AI system can:

Analyze Scientific Literature: Rapidly process and understand millions of scientific papers, clinical trial results, and proprietary datasets to identify relationships between genes, diseases, and potential drug compounds that human scientists might overlook.
Generate Hypotheses: Propose novel hypotheses for drug targets and disease mechanisms, suggesting existing drugs that could be repurposed or identifying entirely new molecular structures for development.
Predict Efficacy and Safety: Use predictive modeling to assess the likelihood of success and potential side effects of drug candidates early in the process, reducing wasted effort on less promising avenues.

The Result:

By leveraging AI, BenevolentAI has significantly reduced the time it takes to identify and validate promising drug candidates. For example, they identified a potential treatment for Parkinson’s disease, successfully repurposing an existing drug, and advancing it to clinical trials in a fraction of the traditional timeframe. This acceleration means getting life-saving treatments to patients faster, transforming the innovation cycle from an agonizing crawl to a rapid, data-driven sprint, all while maintaining strict human oversight and ethical considerations.

Case Study 2: Generative AI in Product Design (Nike)

The Challenge:

Designing high-performance athletic footwear involves a complex interplay of biomechanics, material science, aesthetics, and manufacturing constraints. Iterating on designs to optimize for factors like weight, durability, and shock absorption used to be a time-consuming, manual process involving physical prototypes and extensive testing. The innovation cycle for a new shoe model could take 18-24 months.

AI as an Accelerator:

Companies like Nike have begun integrating generative AI into their product design processes. Generative design algorithms can:

Explore Design Space: Given a set of design parameters (e.g., desired weight, material properties, aesthetic guidelines), the AI can rapidly generate hundreds or thousands of unique sole structures or upper designs. These designs often push the boundaries of human intuition, creating novel geometries optimized for performance.
Simulate Performance: AI-powered simulation tools can instantly analyze the generated designs for factors like stress points, airflow, and energy return, providing immediate feedback on their potential performance without needing to build physical prototypes.
Suggest Material Optimization: The AI can also suggest optimal material combinations or placement to achieve desired characteristics, further speeding up the development process.

The Result:

The integration of generative AI allows Nike’s design teams to explore a vastly larger array of design possibilities and to iterate on ideas at an accelerated pace. What once took weeks or months of manual design and physical prototyping can now be achieved in days. This not only shortens the overall innovation cycle for new footwear (reducing time-to-market) but also leads to more innovative, higher-performing products that better meet the specific needs of athletes. The human designer remains at the helm, guiding the AI and making critical creative choices, but their capabilities are amplified exponentially.

Conclusion: The Future of Innovation is Intelligent

The journey from a raw idea to a market-ready innovation has never been faster, nor more critical. Artificial Intelligence is not merely an optional add-on; it is becoming an essential engine for accelerating innovation cycles across every industry. By intelligently augmenting human capabilities, AI allows organizations to move beyond incremental improvements to truly transformative breakthroughs.

As leaders, our role is to embrace this technological evolution with a human-centered approach. We must leverage AI to free our teams from mundane tasks, empower them with deeper insights, and enable them to focus their unique creativity and empathy where it truly matters. The future of innovation is intelligent, collaborative, and, above all, accelerated. It’s time to harness AI to build a future where every great idea has a fast track to impact.

Image credit: Microsoft CoPilot

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