“The starting point must be that AI is built by human intelligence – how do you get a machine to think and act like a human ? “
Lomit and I discuss how AI, the future of work and growth, and how to scale this growth and the necessary people strategy effectively. The challenge for growing startups is how to get more done with less. Lean is about being resourceful and engaging, retaining and monetizing customers; AI is about automating processes and turning data into actionable insights.
The use of data and AI is transforming the workplace, and the future of skills and work. Leaders have to understand data, data driven decision making and have technical acumen to inform good AI. Companies are always under pressure to grow, and a product must unify rather than divide – consensus is required to focus on how to make or save money (growth v. efficiency).
How do we continue to proactively manage the co-dependancy of Humans and technology ? How do we get more done with less ? and how do we scale the up-skilling necessary for tomorrow’s workforce ?
Lomit shares his experience and insights from his work with start ups and organisations and from working on growth platforms across the world.
The main insights you’ll get from this episode are :
– The challenge for growing startups is how to get more done with less. Lean is about being resourceful and engaging, retaining and monetizing customers; AI is about automating processes and turning data into actionable insights.
– To leverage AI, we must train machines to think and act like humans, personalize the experience of a product to attract new customers and have an ideal user journey to enhance the product value.
– Manual personalisation is difficult, but built-in AI can integrate data into one place and populate different platforms to create an asynchronous journey for all customers – a much more efficient way of acquiring customers thanks to real-time data-driven decisions.
– Increasing the lifetime value of customers creates a virtuous cycle to grow business and control growth. An AI-based engine for growth can leverage marketing platforms instead of hiring more people. A great product still needs a great growth marketing engine.
– An aggressive growth curve starts with people (as in any transformation) – building internal alliances; creating a cloud-based customer data platform with cross-company buy-in; over-communicating; sharing best practices; defining the resources you have and need.
– Culture must be nimble and buy/bring in different technologies to support transformation; companies must aim for at-scale onboarding for customers from all over the world that require different approaches.
– Goals must be defined at the outset; successes and/or failures shared transparently; use cases utilised to bring immediate value to the business; marketing budget spent as efficiently as possible; ROI increased as quickly as possible (using AI – aka machine learning).
– Leaders have to understand data and have technical acumen to inform good AI. Companies are always under pressure to grow, and a product must unify rather than divide – consensus is required to focus on how to make or save money (growth v. efficiency).
– Risk audit assessment, scenario planning, controllable levers (e.g. data collection and optimum retention/access), input and technology are required to achieve the desired outcome. All the different layers of a company must be involved and heard.
– We must demystify AI for a co-dependent relationship between AI and humans. A ‘winning together’ mindset addresses the elephant in the room and offers an opportunity to uplevel and upskill, e.g. make menial tasks more efficient.
– The starting point must be that AI is built by human intelligence – we define and control how we leverage it to make jobs better and easier. Ideas are often stifled due to lack of resources and AI facilitates experimentation. A growth mindset – test, learn and iterate – helps.
– The future is all about automation and coding, so these should be taught as early as possible, at school: ‘gamifying’ the experience makes it relatable. Modular teaching of (block) coding gives an understanding of simple algorithms.
– Learning a second language broadens our perspective and coding is another language. It teaches logical, computational and critical thinking, offering lifelong skills, e.g. for future leaders, to understand how technology works.
– Children love to consume technology but also to make and create it too as it builds confidence. Group project-based work brings different children together, builds (creative) resilience and teaches communication, teamwork and problem-solving.
– Top tips for adults to get started are to read around a subject first, ask other people about their experiences, teach yourself and give it a go to become a lifelong learner driven by curiosity.
Find out more about Tynker here : www.tynker.com
Find out more about Lomit Patel here : www.lomitpatel.com