Estimated reading time: 12 minutes
One question in my chats with Enterprise CIOs on the Generative AI adoption keeps popping up: “Where do we start? How do we pinpoint those initial use cases?” In this blog, I’ll share what I’ve learned from these conversations. We’ll talk about how we’ve been steering our enterprise clients toward picking the right use cases, the perfect tech stack, and the most efficient processes. Plus, I’ll touch on how to empower teams for quicker development in the world of Generative AI.
But first, let’s chew over why Generative AI is the talk of the town.
A recent Goldman Sachs report dropped staggering data: a whopping 67% of enterprises they surveyed are either crafting something with Generative AI or have grand plans to implement it in their org.
This stat is a game-changer – 67% in just a year since GPT-3.5 hit the scene? That’s monumental! Cloud and big data adoption never hit these heights. This level of uptake? It’s reminiscent of the early internet days. We’re talking about Generative AI being as monumental as the dawn of the internet.
Now, stir in the mix of high-interest rates, which means tighter budgets and companies striving to slash expenses for a more sustainable future. We’re witnessing layoffs left, right, and center, and CFOs and CEOs are under tremendous pressure to manage cash flow. All these factors are turbocharging the adoption of Generative AI in enterprises and mid-market companies. And startups? For them, it’s a no-brainer.
Here’s the kicker: contrary to the gloomy predictions of data privacy and security concerns slowing down adoption, we’re seeing the exact opposite. Adoption is surging at a breakneck pace. In this rapidly changing landscape, no enterprise wants to be the one lagging behind its competitors or losing touch with customers who might switch to niftier, AI-powered alternatives.
So, if you’re a CIO at an enterprise or a mid-market company, you might be wondering, “What can I do right now to get the ball rolling with Generative AI?” Let’s dive into that and get you set up for success.
Getting Started With the First Set of GenAI Use Cases
Alright, let’s talk about diving into your first Generative AI use cases. It’s a lot like hitting an all-you-can-eat buffet when you’re super hungry. You know the drill: you walk in, your stomach’s growling, and you’re tempted to pile your plate with the first few dishes you see. But hey, that’s not the wisest move, right? There are probably some amazing dishes further down the line you don’t want to miss out on. The smart approach? Scope out the entire spread first, then decide what you fancy and in what order. This way, you get the most bang for your buck and a truly satisfying meal.
Similarly, when stepping into Generative AI adoption, it’s crucial to check out the full spread of use cases. Let’s explore some of the popular and proven ones.
1. Chat Agents: The big names in this arena are ChatGPT and Claude. Businesses of all sizes are asking us to build Private ChatGPTs – these are chat agents tailored for internal employees, customers, and functional teams. But they’re not just any chat agents. They take on various roles – customer support agents for clients, IT and HR helpdesks for employees, procurement co-pilots, legal co-pilots for compliance and legal teams, lead generation agents for sales and marketing teams, and customer interaction agents on websites.
These are the go-to use cases, but there are niche ones too. Think of e-commerce product recommendation agents that simulate a salesperson or claims processing co-pilots assisting human agents in real-time decision-making. The key ingredient? An LLM-powered chat engine that can converse intelligently, reason to an extent, and focus on specific outcomes.
2. Search Agents: Next up, we have search agents powered by Retrieval Augmented Generation (RAG). This is a game-changer for quickly deploying enterprise search solutions. It’s a boon for teams like procurement, legal, compliance, and HR, allowing them to sift through mountains of documents and easily extract pertinent information. RAG’s strength lies in its ability to fetch the right data, which is essential for multitasking agents handling a variety of tasks.
Document search is another hot area in this category, especially among vast PDF repositories. So, who’s the MVP in the search agent game? It’s Perplexity. Customers often come to us saying, “We need a Private Perplexity.” What they’re really looking for is a savvy search agent, akin to Perplexity’s Copilot, known for delivering spot-on answers with references to the original sources. That’s the essence of what people are after in a private Perplexity – the second big use case we see.
So, there you have it – chat and search agents are your buffet’s star dishes in the world of Generative AI. Each serves a distinct purpose and can be tailored to suit various business needs, making them a solid starting point for any enterprise looking to harness the power of AI. Stay tuned as we dive deeper into other tantalizing options in the Generative AI feast!
Beyond Chat & Search Agents
Now, the third big hitter is data analysis. Sure, LLMs might not be data whizzes, but they’re pretty slick when it comes to programming. LLMs can write Python scripts to interact with Pandas data frames in a somewhat roundabout but effective way. Within this realm, conversation analytics is now made possible. Imagine leadership teams casually chatting with their data – asking about sales trends or performance metrics – and getting instant, informed responses.
It’s also a boon for regional business leaders and even field agents, who can get data-driven answers and recommendations on the fly. At Lyzr, for instance, our data agents don’t just analyze data; they offer actionable steps and build to-do lists. It’s like having a private Julius.ai or Power BI Copilot but turbocharged with advanced data science capabilities.
Fourth in line are the generators and summarizers. This is all about creating documents – think product requirements, contracts, and proposals – and condensing information, like summarizing lengthy documents or turning meeting recordings into actionable items. It’s like having your own private Jasper.ai or Copy.ai on hand.
Last but not least, we’re seeing a rise in automation agents. These are the all-rounders, capable of handling end-to-end tasks and processes, even mimicking entire roles. It’s an emerging field, with platforms like Autogen and our Lyzr Automata (in the developmental stages). By the end of 2024, I’m betting we’ll see some solid contenders in this space, giving established RPA platforms a run for their money.
These are the big five use cases in the Generative AI scene – chat agents, search agents, data analysis, content generators/summarizers, and automation agents. Each offers a unique flavor and can bring transformative changes to businesses looking to leverage the power of AI.
Focus on Process & Enablement
While zeroing in on the right use cases is crucial, it’s equally important to consider your organization’s structure, process, and goals. A practice we’ve observed making waves in enterprise companies is the formation of a Generative AI task force. This is not your average team – Generative AI spans across various specialties. Unlike cloud technologies, primarily the domain of IT teams, Generative AI has seeped into every nook and cranny of the corporate world, thanks to ChatGPT’s widespread accessibility.
So, when assembling your Generative AI task force, think inclusively. Bring together minds from sales, marketing, IT, procurement, HR, business, support, and beyond. Diverse perspectives can fuel innovation. These thought leaders from each department form the backbone of an effective task force. What’s their mission? Meeting monthly to chart out the company’s Generative AI adoption journey, setting achievable yet challenging targets, and being accountable for reaching these milestones. These goals should be audacious enough to push boundaries – think launching prototypes and pushing pilots into production.
Choosing the right people for this task force is key. It’s not just about their expertise in their respective fields. Ensure that they, along with the broader leadership team, undergo basic training in Generative AI – covering the dos and don’ts, possibilities, limitations, and essential security protocols. A solid grasp of LLMs and their workings is a must for all members.
Now, about those task force members. They should be more than just subject matter experts. Intermediate to advanced prompt engineering skills are a non-negotiable, even if they aren’t Python wizards. The ideal candidate? A subject matter expert who’s also a prompt engineering pro. We’ve seen such individuals drive the most value in enterprise task forces.
Besides the task force and training, let’s talk about enablement. Aim to get as much of your tech team as possible comfortable building apps using Generative AI stacks. Options range from private agent platforms like what we offer at Lyzr to more foundational tools like OpenAI’s direct function calls or Langchain. This step is about equipping your team with the tools and skills to harness Generative AI effectively.
Choosing the Right Generative AI Stack
After setting the foundation with your task force and choosing your initial use cases, the next big question looms: What Generative AI framework should your organization bet on? It’s like picking the right set of tools for a complex job. Below, I’ll outline various layers you might find in a Generative AI stack and offer some pointers on selecting the right one for your needs.
Choosing the Framework: Consider starting with two to three, or maybe even four, agent frameworks. Your Generative AI task force can play a pivotal role here, picking out suitable tools for each layer in phase one. Another approach could involve consulting with advisory firms like Gartner and Forrester. They can offer tailored advice based on your industry, use cases, and goals. Alternatively, consider collaborating directly with the founders of some of these platforms for a more hands-on, early-stage project.
Key Considerations: When selecting a framework, several factors come into play:
- Learning Curve: A steep learning curve can slow down adoption.
- Security: Ensuring robust security is essential to get clearance from InfoSec teams.
- Data Privacy: Different teams may have varying thresholds for data privacy.
- Choice of LLMs: Decide whether to rely on GPT-4’s extensive knowledge base or your internal resources. If your internal knowledge bank is strong, open-source models like Mixtral could be an option.
- Social Proof and Talent Availability: Consider the platform’s popularity, user base, and availability of skilled professionals.
- Flexibility: The platform should adapt to different data types and use cases.
Lyzr AI – Purpose Built for Enterprises Keeping Privacy & Security in Mind
Lyzr’s Approach: At Lyzr, our inspiration stemmed from open-source platforms like LangChain and LlamaIndex. Our goal? To bridge the gap between these platforms and the specific needs of enterprises. With a decade of experience in AI and data with enterprise clients, we aimed to create an agent framework that is:
- Low Code and Fully Integrated: Capable of replicating end-to-end functionalities of sophisticated SaaS platforms.
- Super Flexible: Allowing adjustments for various data types and use cases.
- Fully Private: Enabling deployment on local servers with top-notch security.
- Versatile: Compatible with smaller models like Bert-1B and Phi2.7, as well as larger ones like Mixtral, Llama2, and even GPT-4 and Claude in hybrid scenarios.
Lyzr AI Management System: To manage these SDKs, we developed the Lyzr AI Management System, inspired by the ISO 42001 AI Security Framework. This system allows you to activate and deactivate SDKs with a secret key, monitor usage, develop and deploy production-grade prompts, use our AutoRAG feature for optimal use case pipelines, and much more in the enterprise version.
So, there you have it – a roadmap for selecting the right Generative AI framework. With these guidelines, you can chart a course that aligns with your organization’s needs, ensuring you’re well-equipped to harness the full potential of Generative AI.
10 Use Cases, 10 Pilots, 1 Production Workload
So, what’s next after finalizing the agent framework? Let’s break down the initial stages – the first three months and the first two years. While it’s a bit early to sketch out the entire two-year plan, we can draw some insights from past experiences with cloud and big data adoption.
First Three Months: This phase is all about action. Imagine identifying 10 use cases and launching 10 pilot programs across various functions, all with the goal of pushing at least one into production the following quarter. This proactive start can set a solid foundation, regardless of your company’s size. Crucially, keep the leadership looped in – if your CXOs are on board and involved in monthly progress reviews, you’re more likely to secure the necessary budget for your experiments. And if the board is engaged, even better – this could open up more funding for future projects.
ROI Focus: Each use case should consider the potential ROI. Will it cut existing costs or drive new revenue? This approach can bolster your case within the enterprise and demonstrate the tangible value of your Generative AI initiatives.
Quarter Two and Beyond: Once you’ve got a pilot into production, ramp up your ambitions. Target 10 production launches in the next quarter, then aim for 20 in the following one. This gradual but consistent scaling can accelerate your Generative AI adoption.
Some neat tricks to identify the first set of use cases:
- Cost-Cutting through Internal Development: Review your company’s most expensive SaaS products and assess if your internal team can develop Generative AI alternatives using Agent Frameworks like Lyzr. This could save significant costs by eliminating the need for pricey external apps.
- In-House Solutions vs. SaaS Sign-ups: Instead of subscribing to new platforms like ChatGPT Enterprise or Perplexity, consider building similar tools in-house at a fraction of the cost. At Lyzr, for example, we offer no-throttling pricing – unlimited users, data, and API calls since our SDKs run on your server. This approach can significantly reduce expenses compared to SaaS platforms that charge per seat.
By focusing on these strategies, you can craft compelling use cases for Generative AI adoption in your organization. Not only do these methods foster innovation, but they also align with cost-saving objectives, proving that Generative AI isn’t just about embracing cutting-edge technology – it’s about smart, strategic implementation that benefits your company’s bottom line.
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