The message is crystal clear. The paradigm is shifting again and is becoming human-centric. It is not about technology replacing humans but how to enhance each other. I know quite a few people who would love “Radically Human: How New Technology Is Transforming Business and Shaping Our Future”. Particularly if you are interested in citizen development.
Intelligent technologies have unfolded in three stages. The first stage was machine-centric. The second stage of human-machine interaction was collaborative. The third stage, underway now, is the human-centric. Machines adapt to humans. Citizen development (ish) on steroids. Towards a more human—and humane—future.
Every business is a technology business
To say that the pandemic accelerated the pace of tech adoption is, to put it mildly. Compared to before the pandemic, the pace picked up by 70%. First-time adoption of digital, AI, cloud and related technologies averaged 63%. IT changes planned for over 12 to 18 months occurred in a matter of days. The bottom line: every business is now a technology business.
A large majority of companies used technology as a lifeline, not as an engine of innovation. Laggards adopt technologies unsystematically, isolate them in unconnected silos, and fail to harness their innovative potential. By contrast, leaders adopt a wide range of cutting-edge information technologies and weave them into “living systems” that blur boundaries, afford agile adaptability, and create seamless human-machine integration.
Leading organisations are not only out-innovating their competitors but taking an even more decisive turn toward human-centred technology. A radically human turn that is upending the very nature of innovation as it was practised over the previous decade. Revolutionary in that it is rewriting the terms of competition. Rooted in the deepest attributes of humans—how we understand, feel, and think.
The authors introduce an innovation framework they call IDEAS, Intelligence, Data, Expertise, Architecture, Strategy. It offers a new innovation framework for companies large and small that they can use to chart a new course to the future, turbocharge revenue growth, and prepare to compete in a world where the human—and the humane—will be the means by which companies will succeed and the measure by which they will be judged.
In the future, we will have top-down systems that don’t require as much data and are faster, more flexible, and more affordable. Rather than training systems with bottom-up machine intelligence, people are guiding them with top-down human knowledge, imparting natural intelligence to what was previously artificial. Innovative companies are creating “living systems”—boundaryless, adaptable, and radically human architectures that bring an elegant simplicity to human-machine interaction. AI has enabled humans and machines to complement each other, transforming mechanistic processes into highly adaptive, organic, and human-centred activities. Some 80% of respondents of the author’s research believe that systems of the future will provide seamless interaction with humans, and 78% believe these systems will adapt to suit human styles of work. Companies can now reimagine systems to empower new human + machine relationships with natural conversation, simple touches, and abundant personalisation.
The problems with AI
There are a few problems with AI at the moment.
- No machine powered by AI can yet match the ease and efficiency with which even the youngest humans learn, comprehend, and contextualise.
- AI is a black box (we don’t know how they work).
- Context is very hard.
- Genuine comprehension is hard.
- Knowledge frameworks are hard.
- Common sense is hard.
- Emotion and intuition are even harder.
- Correlation is not causation (machines find that hard to understand)
- Every decision needs a huge data set (the first computer program to defeat a professional player in the ancient board game Go was trained on 30 million games).
- There is a lack of existing big datasets for AI. For most business problems or opportunities that exist, there are no big datasets “cleansed” and ready for use by AI systems. In fact, in most organisations, the biggest obstacle to comprehensive AI solutions remains noisy, sparse, or incomplete data, much of it semi- or unstructured. For example, Walmart collects 2.5 petabytes of unstructured data (2.5 million gigabytes) from 1 million customers every hour—equivalent to 167 times the number of books in the Library of Congress. By 2025, each human being will create an estimated 3.4 exabytes of data per day (1 billion gigabytes), mostly through social media, video sharing, and communications.
- The computing power needed growing exponentially. Since 2013, the amount of computational power required to train a deep learning model has increased 600,000-fold.
- Costs are rising astronomically.
- Deep learning requires resources that lie beyond the reach of many organisations. Only a limited number—Alibaba, Amazon, Apple, Google, Microsoft, and some Global 1000 companies—can keep up.
Less artificial and more intelligent
The solution in the core is straightforward. You can (and need to) give an AI huge data sets to learn (an almost impossible task), or you can teach the AI to ask a human for feedback or learning. In other words, AIs learning from humans (and vice versa). Becoming less artificial and more intelligent,
Doing more with less data. This is where I got lost. The book introduces terms such as few-shot learning, one-shot learning, edge computing, generative adversarial networks, data echoing, dynamic filtering, simultaneous training, federated learning, data lakes, digital decoupling, adaptable cloud-to-edge architecture, edge analytics, edge intelligence, holistic stacks,
Combine that with data engineering, batch processing, synthetic data, small data, digital twinning, brain interfaces, VR, edge computing, microservices, RPA, cloud, technology debt, IoT, GPT-3, 5G, single-digit millisecond latency
Finally, the book arrives at machine teaching. Humans tutoring AI. Machine teaching includes three distinct areas of human expertise that AI has long struggled to incorporate: professional experience, collective social experience, and personal experience. The overlay of human expertise creates a significant multiplier effect. A no brainer. From explicit to tacit. Creating immediate emotional context.
But also machines augmenting humans by
- amplifying our powers, as in providing otherwise unattainable data-driven insights
- interacting with us through intelligent agents
- embodying us, as with robots that extend our physical capabilities.
This next generation of AI will do at least three things for workers:
- It will enhance creative work through smarter apps that can, for instance, anticipate the needs of designers, salespeople, and others.
- It will augment language-based tasks.
- It will lower the barriers for scaling digital innovations, making it even easier for non-technical staff to use everyday natural language to program,
Enabling in-house experts
Finding new ways to build human professional experience into value-creating systems unique to their businesses. Thus, companies can use in-house experts to rapidly build customised solutions using machine teaching. Amazon and Google are working on machine teaching techniques that enable engineers without AI expertise to program complicated AI models. Instead of extracting knowledge from data alone, putting the specialised knowledge of human experts to full use. That knowledge includes their functional and domain expertise and their fine-grained understanding of the business itself: how it makes money, how it competes, and where it could be improved.
Amazon’s SageMaker Autopilot, for example, can be used by people without machine learning experience to easily produce a model. Teaching a machine what a human expert would do in the face of high uncertainty and little data can beat data-hungry approaches for designing and controlling many varieties of factory equipment.
Nowhere is the human—and human agency—more central than in strategy. That is where the capacity to make a difference in performance is limited only by the imagination of humans. The challenge lies in the fact that technology, business strategy, and execution are becoming so closely intertwined as to be nearly indistinguishable (magic?).
Three core strategies
Three strategies stand out: Forever Beta, Minimum Viable IDEA (VMI), and Co-lab.
- Forever Beta strategies are seen in products like the Tesla, digitally undateable through the cloud,
- VMI strategies use one or more elements of the IDEAS framework to precisely target weak links in a traditional industry, provide a superior customer experience, and make immediate inroads in the market.
- Co-lab strategies produce superior results in the sciences or other knowledge-intensive environments through human-guided, machine-driven discovery.
I don’t understand much about the technology, but I understand how it impacts business models by being able to implement remote diagnostics and repair, remove friction, deliver consistent customer experiences, making companies a utility and/or a platform, become more efficient, more anticipatory and fully data-enabled across the value chain. It is also good for the climate (there is a circular element). Basically, marrying strategy and execution.
Four critical areas
According to the book, there are four key areas that will be critical for companies to compete successfully in the radically human future: talent, trust, experiences, and sustainability.
Technology democratisation ensures that as many of your people as possible are empowered to become drivers of change, igniting grassroots innovation by equipping every employee with the tools and skills to build technology solutions at the point of need. Natural language processing, low-code platforms, and robotic process automation (RPA) are just a few capabilities and services that make technology more accessible. Technology democratisation could not come at a more critical time for businesses. When access to powerful technology spreads throughout an organisation, every employee can be an active and vital part of digital transformation. Other requirements:
- A superior employee brand
- Safety research shows that when employees feel psychologically safe and can act fearlessly at work, productivity increases by 50%, turnover drops 27%, workers are 40% less likely to experience burnout, and companies become 11 times more innovative compared to their peers.
- Moving from digital literacy to digital fluency (a higher TQ, Technology Quotient)
Never have people been more alert—in a more deeply personal way—to matters of trust. And in the commercial world, a rising tide of mistrust had been building for years, driven by a succession of corporate scandals and malfeasance, including the worldwide financial meltdown of 2008. What’s more, we judge companies just as we do human beings. Brands are now judged so strongly along the lines of warmth and competence dimensions that these judgments explained nearly 50% of all purchase intent, loyalty, and likelihood to recommend a brand or product.”
The difference between the scent-free pouch and the aroma-enriched cup is the difference between efficiency-first engineering and radically human-centred design. Radically human-centred design gives us the ability to “stop and smell the coffee” (literally, in NASA’s case), providing richly human experiences. More and more companies are pushing past traditional notions of customer experience to a more expansive notion of experiences that includes employees and sharply differentiates the company from the competition.
The journey begins with three R’s: re-platforming, reframing, and reach. Replatforming means fully committing to the cloud— Reframing requires shifting your organisation’s mindset about technology. Technology should not be seen as a mere facilitator of more efficient transactions and smooth touchpoints but as the driver of innovation. Reach means moving beyond traditional business priorities to exceptional customer and employee experiences and new value propositions.
The radically human turns represented by IDEAS come together in the most radically human turn of all: sustainability, the existential struggle to save our planet and those who inhabit Remote sensors and other edge technologies make it possible to track water pollution, deforestation, and “dark fleets” of vessels whose fishing practices breach environmental regulations. A few examples
- The Porsche, Audi, and Volkswagen brands are using an AI-powered early warning system to identify sustainability risks such as environmental pollution, human rights abuses, and corruption among direct business partners and at the lower levels of the supply chain.
- Dendra drones, using swarm technology, map every square inch of the area to be restored and gather data, including erosion trends, that the company’s ecologists and AI use to determine what seeding plan to pursue.
- Using FarmGrow, suppliers’ field teams—the humans in the loop—can provide customised coaching on farming practices, help farmers prioritise investments, share information about crop management, and monitor adoption methods to improve farmers’ yields.
- Intelligence is a Netherlands-based company whose technology combines remote sensors, satellite imagery, and cloud-based AI.
- In the United Kingdom, the “Spatial Finance Initiative” was established in 2019 to find ways to analyse geospatial data and translate it into financial decision making for impact investors.
- Tiny satellites, augmented by drones, will be used to take high-resolution images of every point on the globe daily. The resulting data will be scanned and interpreted by AI.
- Digital twins are used to model complex systems—from cars to cities to human hearts—and simulate their functioning accurately. Singapore has created Virtual Singapore. Meanwhile, the European Union is creating a digital twin of the Earth.
- AI is being widely employed to speed up the development of batteries across a wide variety of areas. A team of researchers from Stanford, MIT, and the Toyota Research Institute has developed a machine-learning approach that cuts the time required to evaluate batteries by 98%.
An AI that is human-centric and environmentally aware. How bad…….? Also read:
- Small Data (Anthropology as the last mile of data)
- Scary Smart (Interesting perspective on AI)
- Unchartered (Decision making and data)