I'm on calls with Heads of Orgs - Tech, Product, Biz deciding on AI roadmaps every day. Here's a quick framework that I share with them as these decision-makers embark on this journey.
3 questions I urge you to ask as you embark on this journey on becoming AI-Native.
3 questions I urge you to ask as you embark on this journey on becoming AI-Native.
1 - What mission-critical problems can you fix with AI? Not 'nice to have' experiments but instant RoI?
Seeing value immediately is the best way to make large-scale cultural changes in your org with A.I. adoption. Build to create a winning mindset with A.I.
Seeing value immediately is the best way to make large-scale cultural changes in your org with A.I. adoption. Build to create a winning mindset with A.I.
Across 200+ companies we've worked with over the years I can tell you that a performance analytics-driven A.I. adoption is the only one that sees the best outcomes.
RoI = saving time. faster process. better tools for your people. revenue growth. better workflows & processes.
RoI = saving time. faster process. better tools for your people. revenue growth. better workflows & processes.
2 - When is a good time to invest in data science/ML teams?
BUILD - Some young startups I see want to hire large data science teams & want to invest in building in-house. This is great if you're with a war chest to experiment. Takes biz 2+ years of investment to see outcomes.
BUILD - Some young startups I see want to hire large data science teams & want to invest in building in-house. This is great if you're with a war chest to experiment. Takes biz 2+ years of investment to see outcomes.
BUY - With engineers who can work towards quick wins with ready-to-use APIs, you put the culture of data-driven orgs ahead of titles. Buy arguments focused on quick value / RoI can be game-changing for companies. You don't need to compete in the ML talent market, just yet.
Build-Buy hybrid - You can be small or big org & create a hybrid buy-build approach, all focused on RoI. We work with companies with >5-10K engineering teams with build & buy approaches, I can tell you 👉 RoI & value are the only questions that really help make the right decision
3 - Intellectual honesty about the why? Sometimes you invest in A.I. to look good, to have a creative outlet of some kind, to experiment, learn vs. drive metrics affecting your biz. Being honest about where you are on this list can decide which way to go for investing in A.I.
From watching multi-billion $$ public companies to small startups just getting started, one thing is true for those that did it right > The journey towards building a truly AI-Native biz is about making investment decisions that answer the Q - where is my RoI today, here & now.
Might seem obvious but you'd be surprised how little this is asked. Great Q! Scalability is absolutely part of definition of 'best possible' way to build/deploy. AI is almost always a game of numbers/scale. Best = scale, RoI, impact on workflow & people
https://twitter.com/guruji93/status/1435448147325571073?s=20
• • •
Missing some Tweet in this thread? You can try to
force a refresh
