The Industrial Revolution was all about simulating human and animal bodily activities — like lifting, harvesting, moving, and building — using machines.
Artificial intelligence (AI), on the other hand, is the simulation of cognitive tasks, using computers and math to simulate what human beings do with their brains. And the recent unprecedented advances in AI technology are causing a lot of uncertainty.
The question on everyone’s minds during the 2023 Innovation Summit boiled down to how recent developments in AI will affect our teams, organizations, and work lives in the coming months and years.
Peter Coffee, VP for Strategic Research at Salesforce, spoke to these topics in his kickoff presentation: “Outcomes, Not Artifacts – Tools, Multi-Tools, and Meta-Tools: Mental and Cultural Means of Innovation.”
Peter Mulford, Chief Innovation Officer at BTS, expanded on them in his keynote, “AI and the Future of Work.”
We can start by first understanding AI and its capabilities. Next, as leaders, we can learn to seize the opportunities, embrace the coming changes, and meet the inevitable challenges.
Understand What AI Does (and What it Can’t Do)
Peter Mulford started with an elegant overview of AI:
Artificial General intelligence (AGI) are machines that can do anything a human being can, including build other machines.
Artificial Narrow Intelligence (ANI) are machines that have been programmed to do a specific task, like prediction. Examples include shopping recommendations, self-driving cars, or ships that find optimal routes.
Machine Learning is a subset of AI. It involves training your algorithms to do certain activities and to find insights and data. One example is computerized chess — If you make a certain move, the program will respond — if this, then that.
Deep Learning is another subset of AI, encompassing artificial neural networks. It’s a machine learning method based on a mathematical approximation of your brain, allowing high-speed data clustering and classification.
Finally, Generative AI is a subset of all this. This is AI that can produce next word predictions, music, and art. (In the graphic, we’re mostly concerned with the areas in pink.)
Learn how AI actually works
The good news is all the different types of AI do essentially the same things — computerized prediction. That includes self-driving cars with large language models that talk to you. Artificial intelligence and machine learning are prediction machines, just like us. Your brain is always making predictions: If I do this, will my boss get angry? Will I get promoted?
Know the correct approaches to using AI
Mulford recommends you ask 3 questions before starting any AI project:
- What’s the problem you’re trying to solve? You don’t need a technician to get to this answer, you need a business person. Is your use case about supply chain, marketing, HR, creating competitive advantage, or something else?
- How can you solve this problem through computerized prediction? Because if you can’t, don’t use AI for it.
- What data do you need to make that prediction? What do you need to know about your problem in order to make the right adjustments?
How to Seize the Opportunities of AI
More than ever, the future is now. Here’s how we can best take advantage of it.
Embrace true innovation
The nature of innovation changes over time. In 2023, it needs to be an intentionally crafted strategy. Don’t make the common mistake of viewing innovation in terms of the outcome produced or the service performed. Make sure that you’re being genuinely innovative and not merely inventive or creative.
As Peter Mulford says, “Be intentional about putting yourself into the mind of your customer to see the need that is being met and not the object that is being delivered.”
Focus on the experience. Will you emerge a technology leader? Will you innovate in customer experience?
Efficiency is not revolution. Jeanie Ross, author of Designed for Digital: How to Architect your Business for Sustained Success notes that the point of innovation is not to do the same old things that have always been done, just with digital technology.
Instead, look at the capabilities of connection, collaboration, automation, and machine intelligence and ask what kinds of value can now become part of the product that you couldn’t offer before.
Start with what you know, list desired capabilities, and determine what’s needed to move from current to future state.
You could be living in the best of times or the worst of times…
If you’re genuinely excited about change, disruption, exploration and sailing into the unknown, then today really is the best of times.
But if you’re not — if you don’t like change or uncertainty — or if you wish you could run your business tomorrow the way you did in the past, then these are the worst of times. And unfortunately, it will only get more difficult in the future.
… but “best” or “worst” is largely up to you
If you’re in an organization, team, or even a country where your rate of learning is faster than the rate of change, you’re likely thriving.
If you’re not, if the rate of change is faster than you’re able or willing to deal with, you’ll be facing disruption.
Disruption happens when what you do to create value isn’t valued anymore because something or someone else does it better.
It can affect us at any level — from whole countries or companies down to the individual.
Accept that the pace of change will only accelerate
Change will happen sooner than we’re going to find easy, Peter Coffee warns.
Mathematically, exponential change means continuous acceleration, represented by the hockey stick curve. You’re going to need continuous investment to maintain your pace of change.
As Pat Gelsinger, CEO of Intel, has said, the remarkable, anxiety-inducing pace of change we’re experiencing now is the slowest it’s going to be for the rest of our lives.
Cultivating agility — the ability to recognize changes and move quickly — and the ability to fix one thing without breaking everything else will be key.
How to Embrace AI-Driven Changes
How you anticipate and prepare for change will determine how well you and your organizations are able to meet it.
Values matter more than ever
Customers and clients are increasingly asking where their products come from. Were they produced sustainably and ethically?
Being able to trace the sources of your products, like Patagonia does, is an important innovation. It alters people’s sense of the experience they’re having and their relationship with you.
According to Pew Research, the percentage of people who believe that AI will improve their lives is less now than it was a year ago and considerably less than it was 10 years ago.
That’s because of the awareness of the risks of what Shoshana Zuboff has called surveillance capitalism — the idea that everything you do is collected and used to take advantage of you rather than to offer you new value.
“Honesty in using data with respect and to create value rather than merely to create revenue is maybe the most important value we can embrace,” says Peter Coffee.
Sustainability is crucial
Don’t forget about sustainability. The world is already burning a large single-digit percentage of selectable power running servers. That number shouldn’t be allowed to continue to grow. Stein’s Law states that if something can’t go on like this forever, then at some point it must stop. Peter Mulford stresses that we as individual practitioners need to be voices in the room talking about this.
Different skills will be needed
The near future of work will value what The Institute for the Future calls “transdisciplinary.” Teams with radically different points of view, made up of musicians, artists, athletes, and programmers, for example, will introduce perspectives you might miss otherwise. In addition, look for:
- Computational thinking. Ginni Rometty at IBM defines this as being able to look at a problem and visualize the data structures that might help to represent it. Visualize how someone might code the solution and what resources might be needed to make sense of it all.
- Sense-making. A term coined by Steve Jobs, sense-making means making sure that you reinforce the right outcomes. Don’t merely reward AI because it wrote an email, reward that it wrote an email that resulted in deals closing more quickly. Use outcome measurement as the training reinforcement mechanism for the AI.
- Social intelligence. Build collaborative teams of people who speak different jargons, who come at problems with different points of view, and who have different priorities in areas like environmental sustainability versus pure economic activity, for example.
How Leaders Can Meet the Challenges of AI
Your teams will likely fall into two camps. The first will be excited about AI, talking about how amazing it’s going to be. The other will be more hesitant, fearing what’s going to happen — to society, to jobs, to the company, and to themselves.
At the same time, you as a leader will want to make sure you don’t chase a shiny object on the one hand or miss the moment on the other.
Motivating people to be the first to disrupt themselves instead of waiting until it’s overwhelmingly obvious that they’ve already been disrupted will always be a challenge — one that each of us needs to assume. The only way is for someone to stand up and say, “We’re going to take the necessary risk here.”
Keys to successful change management
Getting the most out of artificial intelligence is going to require you, your coworkers, and your leaders to do something unnatural — to balance scaling the benefits of the technology as it exists today while creating the culture in which you can explore tomorrow’s version of AI at the same time. This isn’t natural; we’ll have to learn how to best accomplish it.
AI’s impacts may surprise you
People always ask whether there will be fewer customer support representatives in an era of Generative AI. In fact, there will be more people doing customer support — just not in the way it’s delivered today. They’re going to orchestrate experience, not just answer questions.
Historically, when a job can create more value, you wind up with more people instead of fewer. Huntington Bank unified data from 16 different lines of business to see their customers as a whole. This was a key step in making genuine innovation possible in customer service.
Trust in people is paramount
We’re now in the trust business. Everything we do with AI must be done in a trust-first way.
BTS has created guidelines for responsible AI development and their Office of Ethical and Humane Use of Technology is in the room when key product decisions are being made. All innovation needs to take place within the guardrails they set up in the beginning. Trying to add guardrails later on is much more difficult, and it can be brand-destroying if you fail to do it.
Focus on accuracy, safety, honesty, and consent to use the data that you’re collecting. Also, curate the right data. Train your AI on the 10% of customers you want more of instead of the 90% you don’t. Be selective and pay attention to what you’re feeding the beast.
Trust in data integrity will allow new innovations
Blockchains aren’t tamper-proof, but they are tamper-evident. The data structure itself guarantees shared truth and the inability to alter data without evidence of tampering. Now that we can actually start trusting the integrity and unity of the data, it enables new innovations.
The platforms we build today will be scrutinized more heavily and will be expected to include things like automated decision system impact assessments, cryptographic authentication, and non-repudiation. Companies like DocuSign make this their business.
Learn to Build and Use Meta-tools
Rather than creating huge, cumbersome Swiss Army knife solutions to tackle problems, think meta-tools, or platforms, that allow you to make any tool you need.
Most software in workplaces today suffers from low usability, low access, and low empowerment. All they do is automate the existing paper processes.
We need active platforms that help us discover what other people know, enable agility and collaboration, and focus automation near the point of knowledge of the problem.
Crucially, people want this, notes Coffee. 83% of users say that automation solutions have given them the time to take on new projects, learn new skills, and deepen relationships with customers and stakeholders.
Use the Four Ps to determine your AI strategy
As a non-technical leader setting strategy, there are the four questions you need to ask and answer:
- Problems — Which ones do you want to solve for? Be open to the idea that AI might be the solution, but it might not. Identify and prioritize high-value use cases.
- Platform — Which one should you use? You’ll have to decide between build, buy, and rent. Should you use OpenAI or Microsoft? Anthropic or GCP? Or should you buy a Lenovo Hightower with some NVIDIA GPUs and build it yourself?
- People — How do you inspire and prepare them? How do you use AI in a world where people have FOBO — the fear of being made obsolete? Ironically, the people who have the highest FOBO are the ones who understand the technology the best. That has implications for all of us who want to use it.
- Policies — What guardrails do you put in place on the technology? You can put technologies in the business to do one thing, but your employees can potentially use them for 50 to 60 others because they’re so versatile. So be really thoughtful around your policies, how they’re used, and how they’re protected, etc.
Welcome the New Future of Work
Peter Drucker is famous for saying there’s nothing quite so useless as doing with great efficiency something that shouldn’t be done at all.
Peter Coffee encourages us all to take on that burden of being revolutionaries. Be a communicator who can paint pictures to show people the new world they can have. Give them the confidence — call it a reality distortion field if you want — but assure them we can do this. The changes won’t be easy, but they will be worth the effort.
And remember, we’re just getting started. Whether these are the best or worst of times is up to us.