Hi guys.
Welcome to this session.
So in this session we wanted to talk about the generative AI business case studies, some successes
and failures we have seen in this particular field.
So the first one we are going to look at is the Khan Academy's Conmigo which they created.
So this is an AI tutor which they built out.
The Khan Academy built out after ChatGPT was launched, which helps students through critical thinking.
It helps teachers with lesson planning and for creating curriculum.
And how it worked is it worked because of the deep integration with the Khan Academy platform they had.
It involved 10,000 plus students and 8000 plus tutors before releasing.
So the key learnings from this particular integration was that the aligning of the AI capabilities with
real user needs, and it worked out really well.
It had a rigorous amount of testings done so that we could understand the success rates were much more
higher with this.
Another example can be of Coca-Cola's Create Real Magic campaign.
So this was a campaign which they ran, where it was a bold mix of AI tools and Coca-Cola's iconic brand
elements.
So the users created unique artwork using logos, bottles and established guidelines.
Why it worked majorly was because of the creative freedom people got paired with clear brand boundaries.
It scaled globally across using ChatGPT and Dall-E across 40 plus markets.
Now the key learnings from this campaign was the generative AI thrives with freedom and structure specifically,
and the strategic guardrails maximizes impact while protecting the brand.
Now, if you look at another use case which was of Air Canada's chatbot incident, which happened.
So basically what happened was Air Canada's chatbot gave some incorrect information about bereavement
fares.
Now, because of this misinformation, it led to financial losses and legal trouble for a customer.
The fallout of that was that Air Canada deflected responsibility and calling the chatbot or third party,
a separate legal entity.
And because of which a civil resolution tribunal ruled against the company, setting a legal precedent.
Now, the key learnings from this incident was that the there was insufficient testing and lack of human
oversight, which was seen in this particular case.
And there was poor crisis management, which made things worse.
Lastly, what we want to look at another scenario which is Google's Gemini image generation feature.
So the goal of this particular feature, it was launched.
The Gemini tool was launched by Google in February 2024, wherein the AI model was to reflect the company's
values around diversity and inclusion.
Now, what went wrong was it produced historically inaccurate images, which sparked a lot of backlash.
It criticized.
It was criticized for potentially trying to reshape the whole societal narratives.
And the key learnings which they had from this was that there was no not enough robust data, and human
oversight was, uh, was used and which was critical for accuracy.
Uh, the balance includes inclusivity with historical context through rigorous training.
Testing was not done.
The positive and responsible, responsible decision to remove the feature.
So what the brand did was they went ahead and they took the right decision to remove the feature eventually.
So these are some case studies scenarios in real life which you can see how AI integrated with different
brands and what impact it brought into the world.