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Previous Post｜As AI Advances, Do Employers Rejoice? Is It True? [AI Ready] #2
In this edition, we'll explore what changes are needed to make your organization more compelling in the age of AI, specifically the first key element of becoming an employer of choice for AI talent. We'll talk about the changes that adopting AI technology will bring to your organization, especially the new innovations that AI will create.
The word “innovation” has been used to describe the act of creating new value, often in terms of corporate vision or product development. But efficient execution of such innovation? It's hard to get your head around. To really understand what this question means, we need to imagine how AI technology will change the concept of innovation.
“We'll never know until we try"
may no longer be the mantra of innovative leaders
If you look back at the way innovation used to work, it was characterized by gradual improvements to a new product or service based on market research and user feedback - what we might call 'incremental innovation'.
The tech startup innovation process is a prime example. From prototypes and minimum viable products (MVPs) to mature products, we see incremental innovation, with many hypotheses being tested and refined through research and feedback on a daily basis.
The reason we're used to innovating in such small steps is, of course, to reduce the likelihood of failure. We can't afford to put all our money into an outcome that no one can predict.
On the other hand, magical innovations which are not incremental are rare and difficult to guarantee success, so you could say that traditional innovations are trapped by human limitations: we can't know what we haven't tried.
Naturally, the decision to create an existing innovation was largely driven by this rationale:
-External reference-based: “This is successful in the U.S.” or “That company does this, so we should try it.”
-Customer feedback: “Users want this feature” or “They want us to make this product”
-Based on past experience: “We've done this before and it worked well”
These approaches often rely on subjective judgment and limited data, which is why so many people spend so much time on research and planning because ideas only come from the information humans can gather.
And what about the execution phase? After all, as the saying goes, “you only know when you try”. Even if you break the process down into small steps and get a lot of feedback to improve it, the results are only known when you actually do it. That's why our innovation process has been filled with a lot of trial and error.
But innovation in the AI era feels a little different. It's what experts call “efficient innovation,” because we can objectively identify the most likely path to success without having to try it. This is because AI technology enables data-driven and predictive innovation.
“This is a hidden trend that hasn't been discovered yet. People haven't seen it yet, but the data says so”
-AI analyzes massive amounts of data to uncover invisible trends and opportunities.
-It quickly and accurately sets the direction of innovation and minimizes risk to increase the probability of success.
“If we go with this strategy, we have an 80% chance of success. Let's try this.”
-AI simulates different scenarios and predicts outcomes.
-Forecasting the future enables faster response to market changes and strategic planning.
This new look at innovation brought about by AI technology significantly reduces risk, enhances the probability of success, and makes the innovation process itself more efficient than the “incremental innovation” we're used to. The amazing thing about AI is that it can even automate innovation if you want.
According to PwC's <Sizing the prize> study published in June 2017, AI technology has the potential to contribute up to $15.7 trillion to the global economy by 2030, with $6.6 trillion of that coming from productivity gains. This shows the economic impact that AI-powered “efficient innovation” can have on the entire market.
Here's an example of how this AI-driven innovation has maximized efficiency. It's the story of Anheuser-Busch InBev (ABInBev), a global brewing company with world-renowned beer brands like Budweiser, Corona, and Stella Artois.
Last year, AB InBev launched a new beer to celebrate the 150th anniversary of the brand's Beck's. While most people think of a brewery as a place where craftsmanship is alive and well with human touch, the Belgian company did something completely different: it created Autonomous, a beer brewed by AI.
The name Autonomous is a play on words, and the website reads.
'Meet the Beer that made itself. From Recipe to Ads, A Beer Made with A.I.’.
Surprisingly, the homepage shows how the entire process was created with AI, even the prompts. From the beer name to recipe development, logo, homepage, design, marketing strategy, and execution, the AI made its own decisions and actions. It's clear that the entire process was driven by AI.
According to an article by AI technology/business expert Bernard Marr and a Google Cloud case study, the “efficient innovation” that Autonomous has delivered with AI is actually quite remarkable.
-New product development: AI creates and evolves the beer itself
-Quality control: real-time analysis of data from the brewing process to predict final product quality.
-Production optimization: improving the filtration process to increase yield per barrel by 60%.
-Customer assessment and management: AI assesses the credit levels of distributors to manage supply inventory, etc.
-Marketing efficiency: AI automatically generates and analyzes digital ad content to optimize it.
With a process and outcomes like these, ABInBev is confident to say on the Autonomous site.
“We didn't make Autonomous, Autonomous made itself”
This isn't just a marketing phrase, because in reality, a human only said, “I want to brew a 150th anniversary beer”, and everything that happened after that was evolved by AI making data-driven decisions about the most optimized course of action.
In other words, we're at a point where a human can say, “I want to make something”, and AI can do everything else. Do you see how different this is from the incremental innovation we're used to?
On the organizational side, for employees, a shift in the definition of innovation implies that the way you perform, the infrastructure, and the way you're evaluated can be very disruptive. So wouldn't AI talent obviously want a workplace where they can experience and achieve this kind of AI innovation? Organizations like ABinBev, where they can practice and be recognized for “efficient innovation” - data-driven, predictive, automated, and not the innovation of the past.
So, in order for companies to embrace and operate with AI talent in the future, we need to rethink what we think of as enterprise-wide innovation.
What is our existing definition of 'innovation'? What does “innovation” look like that we recognize and practice in our culture?
Innovation in the age of AI is already here. As we've seen with ABInBev, innovation is no longer a “try it and see” kind of thing, as data-driven predictions and efficient execution are now possible. What companies need now is not to be afraid of change, but to be willing to understand and try new innovations.
The adoption of AI technology is not only transforming the way we innovate but also fundamentally reshaping the concepts of trust and empowerment within organizations. In the next edition, I'll explore how to build trust and empowerment in the AI era.
From younger talent saying, “The elders don't know AI,” to a new environment where you have to prove everything with data, I'll share some fascinating stories of corporate culture change in the AI era to give you a final 'resolution'(vivid view).
How do you earn true trust and authority in the AI era? How will traditional hierarchies and experience-based decision-making transform?
I'll be back soon with the second set of questions you need to ask yourself to become the workplace of choice for AI talent. Stay tuned :)
1. Have you ever felt inherent limitations or difficulties with the traditional innovation method of “incremental innovation” that you are familiar with? How do you think the limitations could be addressed if “efficient innovation” as described by The Asker were to become more advanced in the AI age?
2. Has your company ever tried to innovate with AI beyond your personal work, and if so, how did that experience compare to the traditional innovation process?
3. What did you find most interesting about ABInBev's Autonomous Beer example? If AI can optimize across the board, from ideas to decisions, there will come a time when we won't even need idea meetings. What role will humans play in the enterprise?
4. What are some of the practices that are called and defined as “innovation” in our team? What are the common attributes of those practices?
Written by : The Asker (Link)
Writer : Dana Jeong | CEO of nutilde
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