From Autopilot to AI Co-Pilots
The launch of ChatGPT in late 2022 catalyzed both individuals’ and businesses’ interest in AI. Since then, millions of people have poured countless hours into thinking about, experimenting with, and investing in it. And it’s unlikely that this trend will change in the coming years: A recent IDC forecast found that, by 2027, the global AI software market is expected to reach up to $251 billion — and, separately, the generative AI market is expected to grow to $55.7 billion.
With this burgeoning AI market comes the rare opportunity for businesses to transform the way they work — to save time with automation, simplify complex workflows, and drive better outcomes. And, given how many businesses are already investing in AI, now is the time for go-to-market (GTM) leaders to carefully consider how AI can work for them so that they and their teams can stay relevant and thrive in the future.
Dr. Ayesha Khanna was called on as one of the eight brightest AI thought leaders to share their hottest takes, best tips, and recommended roadmaps to help you get on the runway with AI.
AI can boost efficiency in all parts of your business | Dr. Ayesha Khanna, Co-Founder & CEO, ADDO AI
While we throw around the idea that we’re currently living in an “AI revolution,” it’s actually more accurate to say that we’re experiencing three concurrent AI revolutions.
If you’ve so much as dabbled with any sort of GPT technology, then you’ve already experienced the first revolution: AI that makes humans more productive. Whether it’s providing prompt suggestions to break your writer’s block or translating chat conversations in real time, these AI agents help us humans work faster, smarter, and more efficiently.
The second revolution is a little more hidden: AI that makes machines more productive. For example, manufacturers use digital twins — digital reconstructions of real-world equipment like jet engines — to monitor the health of their equipment. By modeling wear and tear using AI, manufacturers can pinpoint when equipment does (and doesn’t) need to be fixed, saving millions of dollars on unnecessary routine maintenance.
But it’s the third revolution that people tend to overlook the most — and that is where AI makes AI more productive.
For the most part, AI tools operate in silos. An imaging system will buzz away in one corner, a chatbot in another, and a copy generator in a third — but these tools can’t communicate with each other. While you can manually piece together Frankenstein-like AI systems, the results are often inefficient, unproductive, and unreliable.
However, recently, it’s become easier to build these coordinated AI systems. By using natural language processing (NLP) to get AI tools to talk to each other, you can now chain AI tasks together so that a human can give a command to one AI assistant and then communicate it to the other AI tools behind the scenes.
What might this look like in practice? Say you’re a conversion specialist on a marketing team. You tell your AI agent, “Angela matches our ideal customer profile (ICP) perfectly. Wherever she goes, I want you to personalize our offerings to her.” From this command, the AI agent will coordinate with your generative AI tool to craft highly personalized copy, the social platform’s AI to optimize ad targeting, and your on-site AI chatbot to personalize the ad landing page.
While this is only a vision of what’s to come, this third AI revolution is not far off in the future. Soon, AI will be able to communicate with, collaborate with, and train each other, which means that humans will be able to execute strategies more easily and efficiently. When combined with AI for humans and AI for machines, these three AI revolutions will empower you to drive better results in every corner of your business.
This was originally published on Drift.
