Day in the Office: Before & After Generative AI

Well, for many of us, our morning routine

in the office begins with the tried and true tradition

of checking our emails. Nothing remarkable about that. But as some of you might

have seen already, this practice is changing, for the better,

with generative AI. With AI copilots directly integrated into your

favorite email platform, our one hour routine of sorting

our inbox, prioritizing, and crafting responses could

be done in a matter of minutes with the help of

these copilot assistants. These tools are based on

large language models that absorb the emails that

you've written in the past to learn

how you communicate. After learning from these data, the AI copilot can

sort your emails, create drafts for

your responses, and even suggest the right

tone and style according to the audience all based on

your own writing style. All you need to do is

to provide it with a simple description of

what you want. Pretty cool. After your morning meeting, maybe it's time to do

some data analytics for the project that

you'll be working on. While you can have the intern in your data science team to do all the number

crunching for you, generative AI is rapidly

entering the workflow as well. For example, even tools like ChatGPT now possess sophisticated data

analytics capabilities. Instead of you having to code

in a programming language, you can now simply upload

your data into the AI model, tell the tool what you want to do with the data

in plain language, and you'll have the results. Just like in a

programming language, you can generate reports, create compelling data

visualizations and trend analysis, and even invoke very

sophisticated statistical models, all without needing a

technical background. The AI can convert your command into code and run it

for you directly. In other words, right now, gen AI is rapidly democratizing access to sophisticated

analytics. Therefore, allowing

more people to make data-driven informed

decisions faster, with greater confidence,

and potentially with no need for any

sophisticated programming. This could essentially

make any business professional a data

scientist at the same time. As the day progresses, let's say your analysis revealed a new consumer segment that

your company should target. Let's get that focus group

ready and start designing a new marketing

campaign. Wait a minute. Why not first simulating these campaigns using

generative AI so that you can try out your

marketing strategies before incurring any cost in

recruiting human subjects? This is going to be

a reality very soon. Say your company has done

marketing campaigns, focus groups,

surveys in the past. Then you can let a generative

AI model learn from these data and simulate the likely responses from

your target customers. With that, you can create a whole bunch of marketing

contents in one go, test them on your

simulated customers, predict their responses, segment them into priority list, and even design keywords for

search engine optimization. This could help you

target and retain specific audiences

more effectively with much lower cost, thereby making your

marketing campaigns and strategies much

more efficient. Of course, when it's time to actually deal with

these customers, generative AI can be a very effective companion to your traditional team

of human agents. Did you save all

these chat logs from your customer

service center? Now these conversation

histories can be put to use. Generative AI-based

customer service assistant tools can absorb these data and learn from them the best practices your team has built in dealing

with your customers. Armed with these insights

learned from your own data, these assistant

agents can help with your human agents process a larger volume of

customer inquiries. For example, before

your human agent even sees a chat

from a customer, the AI would have already prepared all the information

about their orders, preferences, and

past interaction histories for your agent. Then you can predict

their needs and provide personalized responses

for your human agent to use or edit. By taking over the

lower level processing, this frees up more time for your human agents to deal

with the more complex issues, ultimately enhancing

customer satisfaction. Finally, you can even leverage

generative AI to help you and your workforce with high level, even

strategic tasks. Everything that we've

talked about so far leaves a data trail, so don't let that go to waste. Generative AI can learn from these data and synthesize them into comprehensive reports, highlighting key insights and trends that might have

been missed otherwise. Many of the new tools can now search the Internet

directly as well to actively seek new

information and then package them into new insights on everything from

weather patterns, market conditions,

the macroeconomy, to supply chain risks. The end result is

that all of a sudden, your team now have a much

bigger repository of on-demand insights that you

can use to further make your business more

efficient and resilient. Hopefully, you saw that

generative AI is not just a mere chatbot that you use to tell stories

and search recipes. It can be a transformative force that touches every

part of a business. From strategy and operations to marketing, finance,

compliance, R&D, and customer service, generative AI can be used to

further drive efficiency, personalization, and innovation

on all of these fronts. It's even used to create

this presentation. However, to actually get

here is not a easy task. There's a very big wedge

between ChatGPT the chatbot, to a powerful business

asset that you can trust to be a productive

part of your organization. So, how do we get from here to here? Pause and think about

that for a moment. What is the common ingredient underneath all these new possibilities that

we talked about? What are the

additional ingredients that we need to inject into the chatbot to give it all these awesome

additional capabilities? Keep that in mind

and stay tuned.