With the burgeoning growth of Artificial Intelligence and Machine Learning, today’s startups are faced with more challenges than ever before. According to a new report from the World Economic Forum, by 2022 AI is expected to create 133 million new jobs and also eliminate 75 million, a projection that poses big concerns for some and/or bigger opportunities for others. It all depends on how you look at it. But what is not up for debate is that ready or not, machines are here to stay and the sooner startups embrace that idea, the better chances they’ll have to survive and ultimately thrive.
But how can a startup get ahead of the curve? How can they “beat the machine” at its own game… or better yet, how can they leverage an “intelligent machine” to turbocharge their business growth and outsmart their competitors? Maybe it’s time for the Lean AI approach. It’s the next big step in the evolution of the Lean Startup and it’s proving to be quite an asset for companies like IMVU, which has successfully implemented it since 2018.
Everyone knows that the Lean Startup has been one of the most successful systematic approaches to date. It has been widely adopted across the globe, changing the way startups are built and new products are launched. But now, the stakes are higher. Competing on the rate of learning will become the key difference between the startups that succeed and those that fail. And that’s where Lean AI comes in. Companies that embrace Lean AI and Machine Learning will be able to test, learn and iterate radically faster, raising the competitive bar for learning.
How does a Lean AI work for a Lean Startup?
Lean AI allows innovative startups to use artificial intelligence and machine learning with automation to scale up growth… using a lean startup team. The lean AI approach to modern artificial intelligence, machine learning, and automation combine to offer companies large and small the ability to conduct far more experiments simultaneously. Conducting experiments at scale improves the likelihood of finding successful experiments, some of which you’d never have taken the time to test in a pre-AI and machine learning world. Incremental experiments that otherwise would have been sidelined for cost or complexity are now valid for observation in the world of autonomous marketing.
We already know that machines are better than humans at processing large volumes of data within a short amount of time. AI intelligent machines can learn to make smarter and faster decisions based on the successes or failures of their previous tasks, and over time would produce better results to hit your success metrics like CAC, LTV, etc. That means AI is much better than humans for replacing menial repetitive marketing tasks and calculations that would take humans a lot of time to complete. In addition, those tasks, when done manually, are prone to errors which can be costly. But when it comes to strategic thinking, or any task that exceeds the platform’s capacity for learning or statistical analysis, AI and any form of Machine Learning in their current state are woefully inadequate.
The most powerful uses for AI and Machine Learning must be guided by skilled humans with the broad domain expertise to ensure all the right levers and dials are being optimized for optimal performance. This empowers more human creative minds and strategic thinkers to focus on the work they love rather than the boring data-rich tasks that drive them crazy. Being flexible and adaptable is critical to working well with AI and Machine Learning, and it’s important for anybody who wants to stay relevant on the growth team.
The end goal for any Lean Startup growth team is to continue acquiring new customers cost-effectively and keep achieving more growth with fewer resources. The best way for startup growth teams to stay lean is to find a way to figure out the right roles for your different human team members, so they can support the AI intelligent machine with data, creative, budgets, goals, new ideas and experiments to ensure that their investment in AI and Machine Learning is set up for success.
So at the end of the day, it’s probably safe to say that the choice shouldn’t be between humans and machines, but rather, it’s about how can you best leverage AI, Machine Learning and human intelligence to work well together by complementing each other’s strengths and weaknesses.
