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.
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.
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:
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:
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. |
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.
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.
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:
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 strategic 100-day GenAI implementation plan takes place in three stages:
Let’s now find out what happens in each of these stages.
The first 35 days focus on educating the leadership teams about generative AI, its possible applications, and impact. This phase includes:
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.
The second stage explores how GenAI can be implemented across the various departments in the organization. This includes:
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.
The final stage focuses on the practicality of GenAI implementation through project-based training. This happens by:
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.
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.
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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.
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.
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.
A. AI helps enterprises mainly in predictive analysis, personalization, and decision-making support.
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.