Introduction
Adopting generative AI can be a transformative journey for any company. However, the process of GenAI implementation can often be cumbersome and confusing. Rajendra Singh Pawar, chairman and co-founder of NIIT Limited, joined us at the DataHack Summit 2024, to share some valuable insights into how enterprises can implement GenAI. He has formulated a 100-day GenAI implementation plan for enterprises which I will be explaining in this article. We will also discuss some of the common challenges faced by enterprises during GenAI implementation, and how the plan helps to solve them.
Overview
- Understand the difference between AI and GenAI in the context of enterprises.
- Explore some of the common use cases of AI and GenAI in workplaces.
- Get to know the various challenges companies face during GenAI implementation.
- Learn how your company can implement GenAI into the workforce in just 100 days.
Table of Contents
AI vs GenAI for Enterprises
AI and GenAI are two terms that are often used interchangeably. Most people do not understand the clear difference and hence find it difficult to implement the right tools at work.
Although AI and GenAI share the same foundation of machine learning, they serve different purposes at the enterprise level. It has therefore become increasingly important for enterprises to know the difference between them, to harness artificial intelligence to its best potential.
AI for Enterprises
Artificial Intelligence is a broad term used to describe machines that can think like humans or mimic human intelligence. These machines or AI models can understand language, recognize patterns, or even make decisions, just like humans.
So how does AI help companies? Well, here are some of the most common use cases of AI in enterprises:
- Predictive Analytics: AI helps businesses analyze historical data to predict future trends, customer behaviors, and potential risks. Industries like retail, finance, and healthcare, use AI-driven models for forecasting demand, stock levels, and patient outcomes, respectively.
- Personalization: AI enables personalized customer experiences, which is especially useful in customer service and targeted marketing. By analyzing user data, AI can tailor marketing campaigns, recommend products, or optimize the customer journey in real time. This increases user engagement and client conversion rates.
- Decision-Making: AI systems assist leaders in making informed decisions by analyzing vast datasets and providing actionable insights. Banking, logistics, and manufacturing sectors use AI algorithms to improve decision accuracy and drive cost savings.
GenAI for Enterprises
Generative AI or GenAI is a more specific subset of AI that focuses on models that are capable of creating new content. They learn from their training data and generate human-like text, images, code, music, etc. based on natural language prompts. With the rise of GenAI models like ChatGPT, DALL-E, and Sora, the possibilities for AI-powered content creation are endless.
Here’s how GenAI can help enterprises:
- Content Creation: GenAI models, such as GPT-4, can automatically generate blog posts, social media content, product descriptions, and more. Marketing, advertising, and media companies can use them to save time and reduce reliance on human writers.
- Code Generation: In software development, GenAI can assist with generating code, debugging, and even offering suggestions to developers. This accelerates development cycles and reduces the burden on engineering teams.
- Design and Creativity: Enterprises in fashion, architecture, or gaming can use generative AI to develop creative concepts, design prototypes, or create virtual environments. This would significantly cut down design timelines.
- Customer Interaction: GenAI-powered chatbots or conversational agents like ChatGPT can hold human-like conversations with customers. They can also handle complex customer queries and resolve grievances, improving the customer service of enterprises.
- Data Synthesis: GenAI can synthesize new data based on existing datasets. This helps enterprises in research, testing, or training machine learning models. This is most useful in industries like pharmaceuticals, where data limitations can slow innovation.
Key Differences Between AI and GenAI for Enterprises
| Artificial Intelligence (AI) | Generative AI (GenAI) | |
| Purpose | AI is generally used for automation, prediction, optimization, and decision-making support. | GenAI focuses on generating new, creative outputs (text, images, etc.) based on prompts. |
| Applications | AI is better suited for predictive analytics, fraud detection, and personalized recommendations. | GenAI is ideal for content creation, creative design, code generation, and conversational interactions. |
| Impact on Workforce | AI enhances employee efficiency by automating tasks, allowing employees to focus more on strategic activities. | GenAI enables teams to scale creative and developmental processes, reducing manual content generation workloads. |
How Enterprises Adapt New Technology?
Back in the 1980s when Information Technology (IT) emerged, people were wondering what this new technology was. The next 2 decades went into trying to understand how businesses could harness it. During this process, new teams were formed in companies that focussed on data and insights. The IT department became a norm across industries. Even organizational hierarchy changed – where Electronic Data Processing (EDP) Managers became IT managers, and later turned into Information Systems Managers. A decade ago new essential roles such as Chief Technical Officers (CTOs) and Chief Data Officers (CDOs) became common to leverage technology in an organization
Today, the same transition is happening with generative AI – but at a much faster pace. As more and more companies are exploring and adopting GenAI, tech-savvy functional managers are stepping up to be GenAI managers, Heads of AI, Director of AI, etc. Small teams exploring generative AI tools for product or service-based use cases are transforming into larger, full-fledged departments.
GenAI Adoption in Enterprises
From e-commerce and education to healthcare and architecture, GenAI is growing to be a part of every industry. A study by IBM shows that the largest impact of GenAI is seen in customer engagement and software. The finance sector, which is usually the last to adapt to new technology due to security considerations has also jumped on the bandwagon.
According to a 2023 survey by EY, 75% of senior executives globally agree that GenAI would enhance their employees’ capabilities and productivity. Meanwhile, 64% of companies that had already experienced a significant impact from GenAI, expect that it will redefine their entire business and operating model iby 2025.
While a few companies have already implemented GenAI and started getting their ROI, a vast majority of enterprises have just begun researching and learning about it. However, despite its diverse applications, we see that the widespread implementation of GenAI across industries is hindered.
Let’s try to understand why that is.
Challenges of Generative AI Adoption
As with the hype cycle for any new technology, generative AI too has reached the disillusionment phase. We are now at a point where although everyone has tried using GenAI, only a small percentage of users have found it useful or worth investing in at the enterprise level.
Mr. Pawar spoke to a number of CEOs and top executives across industries in India to understand the hindrances in GenAI adoption in enterprises. According to Mr. Pawar, most of the leaders mentioned challenges in four major aspects:
- Skill Gap: The biggest challenge in GenAI adoption is the lack of skilled professionals in the field. Only a small percentage of the workforce understands data culture or has the required knowledge. This makes it difficult to hire people, especially line managers.
- Unclear Use Cases: The next problem is the inability to identify where and how to use this new technology. Companies that have the resources and skilled staff, can’t seem to find the right use cases for GenAI. Most of them are still learning about the diverse applications of Generative AI.
- Lack of GenAI Initiatives: Despite knowing how GenAI can help them, a lot of companies don’t know where to start or how to go about it. There is still a lot of confusion on who to train and what exactly to train them on. Not having an implementation framework to follow is thus a big hurdle. A related challenge lies in convincing stakeholders and upper management to fund the GenAI adoption plan through to the end.
- Associated Risks: Another major concern that companies have is regarding the risks associated with GenAI adoption. This includes data breaches, jailbreaking, prompt injection to gain confidential information, etc. Although the Indian government is working on setting up laws and guardrails to ensure the safe and responsible use of AI, until they are in place, this will remain a hurdle in GenAI adoption.
A 100-Day Implementation Plan for Generative AI
In order to tackle these challenges and make the GenAI transition easy, Mr. Pawar has formulated a 100-day GenAI Implementation plan for enterprises. The plan, deployed in three stages, starts from scratch and ends with onboarding the entire company into a practically achievable GenAI implementation strategy. It includes market studies, stakeholder discussions, awareness-building programs, use case exploration, and learner-centered, outcome-driven training workshops.
The plan focuses on engagement rather than completion, acknowledging the long-term nature of generative AI integration. It also emphasizes the need to establish guardrails to tackle privacy concerns and mitigate risks.
The 3 Stages of the 100-Day GenAI Plan
The strategic 100-day GenAI implementation plan takes place in three stages:
- Stage 1: Alignment and onboarding
- Stage 2: Use case discovery
- Stage 3: Project-based training
Let’s now find out what happens in each of these stages.
Stage 1: Alignment and Onboarding
The first 35 days focus on educating the leadership teams about generative AI, its possible applications, and impact. This phase includes:
- Pre-training surveys
- Market research for business impact
- Discussions with higher management
- Workshops for leaders
- Organization-wide GenAI awareness sessions
Goal: To understand the importance of building talents with GenAI skills and identify pivotal business functions that can be impacted through GenAI.
How to Achieve It?
This phase begins by conducting market research and surveys to gain knowledge of the possibilities and expected outcomes of incorporating GenAI into the enterprise. The results of these studies will help in onboarding higher management and key stakeholders onto the potential GenAI adoption plan.
Once they are on board, the next step is to identify the main functions and teams that will be using GenAI. This will be followed by educating the leaders within the organization of the transition and future plans. Lastly, there have to be awareness sessions conducted within every team to understand the plan and individual roles in the process.
By the end of this phase, all key stakeholders must clearly understand why to invest in GenAI, and the workforce must be aware of the upcoming changes.
Stage 2: Use Case Discovery
The second stage explores how GenAI can be implemented across the various departments in the organization. This includes:
- Market research on specific use cases
- Workshops with business leaders
- Team-wise brainstorming sessions
- Department-wise use case testing
- Understanding the process of identifying GenAI use cases for the future
Goal: To discover specialist tracks for GenAI implementation within the organization and prepare the workforce for future exploration of use cases.
How to Achieve It?
The second stage is more of a research and development phase. The first step of the second stage is again market research – this time, to find out existing use cases of GenAI within the industry. This will help understand which of these applications can be implemented within the enterprise and how. It will also give an idea as to what new use cases can be explored or tested.
The second step involves having discussions with industry leaders or attending their workshops to understand how exactly to incorporate GenAI into various functions. This gives a more practical understanding of the ground reality and possible challenges in implementing GenAI.
Once the use cases are listed, the next step is to conduct team-wise brainstorming sessions to develop a detailed implementation plan. The plan will include timeframes for the initial testing of all use cases to find out what works and what doesn’t. This will be followed by department-wise use case testing and documentation of the outcome.
Through this process, the workforce will be able to comprehend the process of researching, identifying, testing, and implementing GenAI. This will help in building a system for exploring future use cases.
By the end of this phase, the stakeholders must get clarity on where exactly to implement GenAI tools and services to best benefit the organization.
Stage 3: Project-based Training
The final stage focuses on the practicality of GenAI implementation through project-based training. This happens by:
- Listing out activities that can be compressed and streamlined using GenAI.
- Developing an implementation plan with timelines for each department.
- Designing and developing role-specific programs on GenAI usage.
- Prototyping and deploying MVP (Minimum Viable Product) versions.
- Monitoring and evaluating the systems based on feedback.
Goal: To get the GenAI implementation up and running throughout the enterprise and track the outcome.
How to Achieve It?
The final stage of the implementation plan answers “how to implement GenAI into the enterprise”. By the end of the second stage, there will be clarity on what tasks can be optimized using generative AI. The third stage begins with developing a detailed plan as to how and when each of these tasks will be GenAI-powered.
Each department will then design and develop role-specific programs to train team members on how to use GenAI tools. Parallelly, they will also start prototyping and deploying MVPs wherever new tools need to be developed. This process will also address challenges like cybersecurity, capacity, cost, risk, and privacy while testing out the use cases.
Both these actions need to be continuously monitored, evaluated, and perfected based on feedback in order to meet the goals set in Stage 1. As the 100-day plan concludes, all members of the organization must know how to responsibly and safely harness the power of GenAI to make their work easier and more impactful.
Conclusion
The world is heading towards AI-powered automation and content generation. Both AI and Generative AI present transformative opportunities for enterprises. While AI is crucial for optimizing and automating processes, GenAI introduces new possibilities for creativity, content generation, and human-like interaction. Enterprises need to assess their unique needs and strategies to integrate both AI and GenAI to unlock maximum value from their AI investments.
While companies worldwide are exploring new ways to use GenAI technology, they still find it difficult to implement it into their workforce. This article was an attempt to guide you on how you can upgrade your organization through GenAI implementation.
Whether you’re looking to enhance customer experiences, automate content creation, or accelerate product development, this plan will help you take a significant step ahead in just 100 days.
Find out how Analytics Vidhya can help you in Building Next-Gen AI Enterprises.
Frequently Asked Questions
Q1. Are AI and generative AI the same?
A. Artificial intelligence (AI) refers to models that can mimic human intelligence. Generative AI (GenAI) is a sub-domain of AI that can generate new information and creative content as humans do.
Q2. What is generative AI for your enterprise?
A. Generative AI helps in tasks like content creation, code generation, designing, customer interaction, and data synthesis. It helps enterprises with these tasks and also ensures security and fixes software issues.
Q3. What are the challenges in implementing generative AI in an organization?
A. Some of the challenges in implementing GenAI in organizations include skill gaps and the lack of clarity in use cases. The lack of GenAI initiatives and overcoming the risks associated with GenAI implementation are also prominent challenges.
Q4. How is AI used in companies?
A. AI helps enterprises mainly in predictive analysis, personalization, and decision-making support.
Q5. How do you structure an AI team?
A. When structuring AI teams, it’s important to consider short-term and long-term goals. Short-term solutions may include shared or managed services from an external partner that has AI teams already in place. For long-term goals such as creating AI products, you would need to hire an in-house AI team. It may consist of AI developers, AI engineers, model testing professionals, data scientists, and data engineers, depending on your projects.
Sabreena
19 Sep, 2024
Sabreena Basheer is an architect-turned-writer who's passioante about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.
