GenAI
Everyone’s got access to Gen AI. So, how can CIOs create business value with it?
Jun 11, 2024
5 min read
Since its launch in November 2022, ChatGPT has had 180 million users, 100 million weekly users, and two million developers building on its API. Between Google’s Gemini, Meta’s Llama, Microsoft’s Orca, and dozens of other open-source large language models (LLMs), everyone has access to generative AI.
Every CIO we’ve met has played with generative AI in some form or another. Gartner, too, finds that 70% of executives are exploring the technology for their needs.
Exploring technology with pilot projects or for personal use is one thing. Creating substantial business value out of it is a different ballgame altogether. From our experience working with billion-dollar enterprises, here is a quick guide on things to keep in mind while adopting enterprise conversational AI platforms.
#1 Think business first
Not everyone needs generative AI. There, we said it. Like automation and productivity tools, generative AI certainly offers the benefits of efficiency, speed, cost savings, etc. However, failed Gen AI projects often lose as much opportunity cost, belief in tech, and adoption as a successful one gains.
So, the question is not do we try gen AI or not?
The question is: What can enterprise gen AI solutions do faster, better, or cheaper?
For instance, we’re working with a publicly traded analytics and publishing company that is using enterprise Gen AI for data indexing - i.e., processing their content, tagging and organizing it based on citations, URLs, etc.
This enables them to create incremental value from their existing assets. Their end-consumers are able to find what they want faster and more accurately than the manual tagging/segregation process allowed them to. This better customer experience results in higher revenue.
#2 Be customer-centric
The real ROI on Gen AI for CIOs is either on your top line or bottom line — in other words, it must expand revenue or reduce costs. In the previous example, we saw Gen AI increase revenue.
By serving internal customers, you can use Gen AI to reduce costs. LLMs today can generate code, write content and create images/videos at scale. For instance, you can write the first draft of an entire press release in minutes.
With Gen AI for business, enterprises can build a robust and scalable content engine, publishing on multiple platforms every single day with a lean team of 1-2 individuals. Feed your style guide, tone, voice, past articles, etc., and it can generate drafts that get better over time.
You can also create gen AI solutions for internal communications, HR management, remote support and more. Imagine a chatbot that answers any question about the organization: When is the next public holiday? How many hours have I filled out the timesheet for this month? Who is the best person in the organization to discuss Python programming?
Especially in hybrid workplaces, Gen AI can capture organizational knowledge and support team members unlike ever before.
#3 Leverage your data
What an LLM like ChatGPT or Gemini lacks is your proprietary data, such as purchase trends, user behavior, employee profiles, customer relationship management (CRM) data, financial records, learning management systems, etc. This is your gold mine—your competitive advantage.
While devising your Generative AI strategy, consider all the data you have and custom-train your models with them. Then, keep training your models as and when you gather more data.
For instance, your customer success teams can use the enterprise Gen AI platform to:
Transcribe video calls with customers
Parse emails and Slack conversations with these customers
Build customer intelligence based on all this, your CRM, and publicly available information
Based on that, create customized summary emails after every meeting
Generate automated, personalized, and effective follow up emails
#4 Seriously consider open source
Of the 140+ foundation models released in 2023, 65.7% were open source, finds the Stanford AI Index. Over the last three years, the contribution of open-source LLMs has grown dramatically. We believe that enterprise Gen AI will go the way server operating systems went, i.e., become >90% open source.
Before locking in with some closed-source model, CIOs must consider open-source enterprise Gen AI solutions for the following reasons.
Customizability: You can combine multiple open-source models and customize them to suit your needs.
Performance: Open-source models offer performance on par with proprietary models, some even better. Combine this with your own infrastructure, you’ll make no compromises on performance or security.
Transparency: Open-source models typically enable higher transparency, allowing users to see how inferences are made. Thorough auditability also helps prevent hallucinations.
Flexibility: You can deploy customized, fine-tuned open-source models on public and private clouds, even on-prem, the options of which are limited with closed-source offerings.
#5 Keep an eye on AI governance
Like any emerging technology, enterprise conversational AI platforms Generative AI come with its set of risks, from regulatory compliance, data security, input bias, and lack of diversity to intellectual property theft.
While designing your AI strategy, also set up the right safeguards for governance. Use human-in-the-loop models to ensure output accuracy and reliability. Establish individual accountability. Focus retraining efforts on eliminating inherent biases.
If you’re just starting out with generative AI projects, even the basics can seem like a handful—especially given your existing resources and the talent scarcity in AI in general.
We’ve built the Tune enterprise Gen AI platformsuite of products to solve precisely this problem.
Tune Chat is your fully customizable AI assistant, empowering your teams or customers with all the answers at their fingertips. Try now!
Tune Studio is the ultimate AI playground for fine-tuning your models. Would you rather we do your customization? Sure!
Our AI experts can create business value with Gen AI for CIOs by designing, building, fine-tuning, and deploying your custom models for you to use. Speak to us now.
Since its launch in November 2022, ChatGPT has had 180 million users, 100 million weekly users, and two million developers building on its API. Between Google’s Gemini, Meta’s Llama, Microsoft’s Orca, and dozens of other open-source large language models (LLMs), everyone has access to generative AI.
Every CIO we’ve met has played with generative AI in some form or another. Gartner, too, finds that 70% of executives are exploring the technology for their needs.
Exploring technology with pilot projects or for personal use is one thing. Creating substantial business value out of it is a different ballgame altogether. From our experience working with billion-dollar enterprises, here is a quick guide on things to keep in mind while adopting enterprise conversational AI platforms.
#1 Think business first
Not everyone needs generative AI. There, we said it. Like automation and productivity tools, generative AI certainly offers the benefits of efficiency, speed, cost savings, etc. However, failed Gen AI projects often lose as much opportunity cost, belief in tech, and adoption as a successful one gains.
So, the question is not do we try gen AI or not?
The question is: What can enterprise gen AI solutions do faster, better, or cheaper?
For instance, we’re working with a publicly traded analytics and publishing company that is using enterprise Gen AI for data indexing - i.e., processing their content, tagging and organizing it based on citations, URLs, etc.
This enables them to create incremental value from their existing assets. Their end-consumers are able to find what they want faster and more accurately than the manual tagging/segregation process allowed them to. This better customer experience results in higher revenue.
#2 Be customer-centric
The real ROI on Gen AI for CIOs is either on your top line or bottom line — in other words, it must expand revenue or reduce costs. In the previous example, we saw Gen AI increase revenue.
By serving internal customers, you can use Gen AI to reduce costs. LLMs today can generate code, write content and create images/videos at scale. For instance, you can write the first draft of an entire press release in minutes.
With Gen AI for business, enterprises can build a robust and scalable content engine, publishing on multiple platforms every single day with a lean team of 1-2 individuals. Feed your style guide, tone, voice, past articles, etc., and it can generate drafts that get better over time.
You can also create gen AI solutions for internal communications, HR management, remote support and more. Imagine a chatbot that answers any question about the organization: When is the next public holiday? How many hours have I filled out the timesheet for this month? Who is the best person in the organization to discuss Python programming?
Especially in hybrid workplaces, Gen AI can capture organizational knowledge and support team members unlike ever before.
#3 Leverage your data
What an LLM like ChatGPT or Gemini lacks is your proprietary data, such as purchase trends, user behavior, employee profiles, customer relationship management (CRM) data, financial records, learning management systems, etc. This is your gold mine—your competitive advantage.
While devising your Generative AI strategy, consider all the data you have and custom-train your models with them. Then, keep training your models as and when you gather more data.
For instance, your customer success teams can use the enterprise Gen AI platform to:
Transcribe video calls with customers
Parse emails and Slack conversations with these customers
Build customer intelligence based on all this, your CRM, and publicly available information
Based on that, create customized summary emails after every meeting
Generate automated, personalized, and effective follow up emails
#4 Seriously consider open source
Of the 140+ foundation models released in 2023, 65.7% were open source, finds the Stanford AI Index. Over the last three years, the contribution of open-source LLMs has grown dramatically. We believe that enterprise Gen AI will go the way server operating systems went, i.e., become >90% open source.
Before locking in with some closed-source model, CIOs must consider open-source enterprise Gen AI solutions for the following reasons.
Customizability: You can combine multiple open-source models and customize them to suit your needs.
Performance: Open-source models offer performance on par with proprietary models, some even better. Combine this with your own infrastructure, you’ll make no compromises on performance or security.
Transparency: Open-source models typically enable higher transparency, allowing users to see how inferences are made. Thorough auditability also helps prevent hallucinations.
Flexibility: You can deploy customized, fine-tuned open-source models on public and private clouds, even on-prem, the options of which are limited with closed-source offerings.
#5 Keep an eye on AI governance
Like any emerging technology, enterprise conversational AI platforms Generative AI come with its set of risks, from regulatory compliance, data security, input bias, and lack of diversity to intellectual property theft.
While designing your AI strategy, also set up the right safeguards for governance. Use human-in-the-loop models to ensure output accuracy and reliability. Establish individual accountability. Focus retraining efforts on eliminating inherent biases.
If you’re just starting out with generative AI projects, even the basics can seem like a handful—especially given your existing resources and the talent scarcity in AI in general.
We’ve built the Tune enterprise Gen AI platformsuite of products to solve precisely this problem.
Tune Chat is your fully customizable AI assistant, empowering your teams or customers with all the answers at their fingertips. Try now!
Tune Studio is the ultimate AI playground for fine-tuning your models. Would you rather we do your customization? Sure!
Our AI experts can create business value with Gen AI for CIOs by designing, building, fine-tuning, and deploying your custom models for you to use. Speak to us now.
Since its launch in November 2022, ChatGPT has had 180 million users, 100 million weekly users, and two million developers building on its API. Between Google’s Gemini, Meta’s Llama, Microsoft’s Orca, and dozens of other open-source large language models (LLMs), everyone has access to generative AI.
Every CIO we’ve met has played with generative AI in some form or another. Gartner, too, finds that 70% of executives are exploring the technology for their needs.
Exploring technology with pilot projects or for personal use is one thing. Creating substantial business value out of it is a different ballgame altogether. From our experience working with billion-dollar enterprises, here is a quick guide on things to keep in mind while adopting enterprise conversational AI platforms.
#1 Think business first
Not everyone needs generative AI. There, we said it. Like automation and productivity tools, generative AI certainly offers the benefits of efficiency, speed, cost savings, etc. However, failed Gen AI projects often lose as much opportunity cost, belief in tech, and adoption as a successful one gains.
So, the question is not do we try gen AI or not?
The question is: What can enterprise gen AI solutions do faster, better, or cheaper?
For instance, we’re working with a publicly traded analytics and publishing company that is using enterprise Gen AI for data indexing - i.e., processing their content, tagging and organizing it based on citations, URLs, etc.
This enables them to create incremental value from their existing assets. Their end-consumers are able to find what they want faster and more accurately than the manual tagging/segregation process allowed them to. This better customer experience results in higher revenue.
#2 Be customer-centric
The real ROI on Gen AI for CIOs is either on your top line or bottom line — in other words, it must expand revenue or reduce costs. In the previous example, we saw Gen AI increase revenue.
By serving internal customers, you can use Gen AI to reduce costs. LLMs today can generate code, write content and create images/videos at scale. For instance, you can write the first draft of an entire press release in minutes.
With Gen AI for business, enterprises can build a robust and scalable content engine, publishing on multiple platforms every single day with a lean team of 1-2 individuals. Feed your style guide, tone, voice, past articles, etc., and it can generate drafts that get better over time.
You can also create gen AI solutions for internal communications, HR management, remote support and more. Imagine a chatbot that answers any question about the organization: When is the next public holiday? How many hours have I filled out the timesheet for this month? Who is the best person in the organization to discuss Python programming?
Especially in hybrid workplaces, Gen AI can capture organizational knowledge and support team members unlike ever before.
#3 Leverage your data
What an LLM like ChatGPT or Gemini lacks is your proprietary data, such as purchase trends, user behavior, employee profiles, customer relationship management (CRM) data, financial records, learning management systems, etc. This is your gold mine—your competitive advantage.
While devising your Generative AI strategy, consider all the data you have and custom-train your models with them. Then, keep training your models as and when you gather more data.
For instance, your customer success teams can use the enterprise Gen AI platform to:
Transcribe video calls with customers
Parse emails and Slack conversations with these customers
Build customer intelligence based on all this, your CRM, and publicly available information
Based on that, create customized summary emails after every meeting
Generate automated, personalized, and effective follow up emails
#4 Seriously consider open source
Of the 140+ foundation models released in 2023, 65.7% were open source, finds the Stanford AI Index. Over the last three years, the contribution of open-source LLMs has grown dramatically. We believe that enterprise Gen AI will go the way server operating systems went, i.e., become >90% open source.
Before locking in with some closed-source model, CIOs must consider open-source enterprise Gen AI solutions for the following reasons.
Customizability: You can combine multiple open-source models and customize them to suit your needs.
Performance: Open-source models offer performance on par with proprietary models, some even better. Combine this with your own infrastructure, you’ll make no compromises on performance or security.
Transparency: Open-source models typically enable higher transparency, allowing users to see how inferences are made. Thorough auditability also helps prevent hallucinations.
Flexibility: You can deploy customized, fine-tuned open-source models on public and private clouds, even on-prem, the options of which are limited with closed-source offerings.
#5 Keep an eye on AI governance
Like any emerging technology, enterprise conversational AI platforms Generative AI come with its set of risks, from regulatory compliance, data security, input bias, and lack of diversity to intellectual property theft.
While designing your AI strategy, also set up the right safeguards for governance. Use human-in-the-loop models to ensure output accuracy and reliability. Establish individual accountability. Focus retraining efforts on eliminating inherent biases.
If you’re just starting out with generative AI projects, even the basics can seem like a handful—especially given your existing resources and the talent scarcity in AI in general.
We’ve built the Tune enterprise Gen AI platformsuite of products to solve precisely this problem.
Tune Chat is your fully customizable AI assistant, empowering your teams or customers with all the answers at their fingertips. Try now!
Tune Studio is the ultimate AI playground for fine-tuning your models. Would you rather we do your customization? Sure!
Our AI experts can create business value with Gen AI for CIOs by designing, building, fine-tuning, and deploying your custom models for you to use. Speak to us now.
Written by
Anshuman Pandey
Co-founder and CEO
Enterprise GenAI Stack.
LLMs on your cloud & data.
© 2024 NimbleBox, Inc.
Enterprise GenAI Stack.
LLMs on your cloud & data.
© 2024 NimbleBox, Inc.
Enterprise GenAI Stack.
LLMs on your cloud & data.
© 2024 NimbleBox, Inc.