The term AI-first was made popular by Sundar Pichai in 2017 when he announced a strategic move that Google was going to make from shifting their strategy from mobile-first to AI first.
“We are moving from a company that helps you find answers to a company that helps you get things done,” Pichai said in his keynote address. “We want our products to work harder for you in the context of your job, your home, and your life.”
Immediately following the decision, it became clear that Google was rethinking all of its products from the perspective of how to delight their customers in their unique context. Google would go on with acquiring several AI firms, hiring the best AI talent in the world, and putting massive amounts of data they already had to work to generate actionable insights. This necessitated them to develop new algorithmic capabilities and approaches powered by the most recent advances in artificial intelligence.
It was a smart strategy considering that since then, people, machines, data, and processes have become even more connected. And as this conversion progressed, data made it possible for creating personalized insights. Google engineers have since successfully infused AI into almost every offering; from Nest that connects every sensor and device in your home, to serving you the most relevant advertisement that you may likely click on.
It should be no surprise that AI has first penetrated the software industry and started to transform it fundamentally. Companies like Google, Alibaba, Facebook, Baidu, Pinterest, Microsoft, Netflix, Lyft, and Amazon have invested tens of billions of dollars, and AI became pervasive in their organizations and cultures.
AI not only powers most products and applications such as anti-fraud, personalized search, and customer recommendations but also makes critical business decisions in daily business operations without necessitating much human intervention.
What is AI-first strategy?
The core idea behind AI-first strategy is the aspiration for an organization to use machine intelligence vs. human expertise in making business decisions and creating and sustaining competitive advantage. Companies that have an AI strategy end up completely reconfiguring their operations and changing their ways of working so that they can rely on machine intelligence for making business decisions.
They use data, algorithms, data science, and build AI systems that automate decisions, create and offer personalized products to capture customer value more effectively and efficiently, and learn and improve with feedback. While humans design and maintain these systems, machines and algorithms run them without much human intervention.
This, of course, is a significant transformation and departure of how businesses operate today. It requires the organization to not only change its vision and mindset but transform its operations, infrastructure, core capabilities, workforce, and ways of working.
Companies that have succeeded in an AI-driven transformation treat every one of their customers differently and offer customers a personalized, differentiated product, service, or solution that delights them in their unique context. AI first companies follow a drastically different approach to conducting business vs. how a traditional firm operates.
Traditional companies develop products targeting several segments and tailoring their offerings to deliver segment-specific value propositions. Drug manufacturers create products that work for a large patient group. Retailers manufacture clothing for casual, business-casual or other context, and offer standardized sizes from small to extra large. If you are looking for a drug that is just made for you, you are out of luck. If you are looking for a dress made for you, you can have one tailored by paying a premium. Personalization is not a service that traditional organizations know how to offer because it’s simply not feasible for a large firm to do so. AI is changing that perception.
Personalization, as a customer expectation and firm capability, has been evolving rapidly. Artificial intelligence and large amounts of data make it now possible for firms to treat their customers as a segment of one. Rather than offering large groups of customers a single solution, AI-first organizations focus on using data, and AI systems to design individual experiences that only work for one single customer.
Most are familiar with Netflix and how they personalize their content. “Netflix has done a fantastic job from the very early days of collecting data and understanding what viewers want, and presenting viewers with options that they ultimately decide is the right option for them.” said Dr. Nandhakumar, Senior Vice President in the office of the CTO of LG Electronics Inc., in a recent interview.
Netflix, Spotify, Stitchfix, Amazon, Google thrive because of this mindset and their AI-first approach.
If you’re using Google assistant regularly, after a while, the assistant will know what sports you like, which teams you root for, which restaurants you eat at, what temperature at your home is most comfortable for you, and a host of other preferences and personal information about you.
Similarly, if you’re using Siri, you can ask it to retrieve and show you pictures of your daughter shot in 2018. Your assistant will suggest available parking spots near the destination you’re heading towards, the best Italian restaurant for your dinner meeting, and schedule meetings with your co-workers. All you need to do is ask.
That, of course, requires a trade-off between privacy and convenience. Are you comfortable conversing with an AI-assistant and providing all of this information? If you are, what you will get in-return is convenience and personalization. You will be provided those pictures that you wanted in no time. In return, you will be shown an advertisement that you’ll likely find relevant. You are a known entity, more so every day.
The move to AI-first requires putting AI and data in the center of a firm’s operations. To do so, firms need to build the infrastructure necessary. They need to process structured and unstructured data and develop robust data governance and security layers around it. They also need to bring in machine learning and algorithmic capabilities and build APIs to serve their internal and external customers better.
For Google, this shift worked quite well. Today, the company serves personalized ads optimized for one customer rather than for a larger segment. As a result, advertisers get a better return for their investments, Google differentiates itself and its services and the AI system keeps learning.
Not every company is suited for an AI-first strategy, however. Contact us to discuss, whether this is the right path for you as you adopt AI and how to get started with your journey.