Accelerating Enterprise Automation with Generative AI

The advent of Generative AI is set to redefine the landscape of enterprise automation. According to Markets and Markets, the global enterprise automation market is projected to grow from $72.6 billion in 2021 to over $122.6 billion by 2026, mainly driven by the adoption of advanced AI technologies. Generative AI could be the key to unlocking next-level efficiency, cost savings, and innovation.

Goldman Sachs estimates that as many as 300 million jobs could be affected by Generative AI, while at the same time, the productivity gains could drive a 7% (~ 7 trillion USD) increase in the global GDP.

These numbers are insane for a given technology. In this article, I will guide you through understanding Generative AI's transformative potential in enterprise automation, its successful real-world applications, and how your organization can harness its power. Most of my views are based on Open AI's GPT and ChatGPT-based capabilities. However, other LLMs will add more power to the recommendation in this article.

Understanding Generative AI

Assuming that you are familiar with AI/ML, you can consider Generative AI as a subject of Deep Learning.

Generative AI is a branch of artificial intelligence that, as the name suggests, generates new, previously unseen outputs from the data it's been trained on. In traditional AI, you feed your models thousands of data records (e.g. cat photos), and then it predicts some outcome for a given input (e.g. whether an image is/contains a cat or not). The generative AI goes many steps further, and it can create a brand new picture of a cat that doesn't even exist! That is what makes it so exciting!

The below image indicates how generative AI differs from the discriminative AI (the traditional AI that we are used to seeing) models:

Generative AI (GAI) is built upon Artificial Neural Networks (ANN) designed to mimic the human brain's operations. It learns patterns from the data it's fed, much like a child learns by observing the world around them. Over time, and with a lot of data and training, Generative AI can produce new outputs, be it text, images, or speech. Essentially that is what the poster boy of Generative AI, the Generative Pre-Trained Transformer?(GPT), is.

GPT uses machine learning techniques to generate human-like text by predicting the likelihood of a word given the previous words used in the text. This makes it incredibly powerful for a wide range of tasks, from translation and summarization to content creation, making it a versatile tool in the AI field.

I see models like ChatGPT as a creative, ingenious team member who never rests. It's ready to innovate and accelerate automation in your enterprise, making your business processes smarter and your life easier.

While GAI offers great capabilities, you still need to understand these opportunities and associated challenges and implement them very well to realize the true benefits of GAI. Before we go there, let's first understand the current challenges of enterprise automation.

Current Challenges of Enterprise Automation

While AI-driven automation is becoming somewhat acceptable, the general acceptance of AI is still low in enterprises. I have listed down some of the key challenges enterprise automation are facing.

1. Complexity of Processes

Many enterprise processes are complex and involve various tasks that must be coordinated. Traditional rule-based automation struggles with handling these complexities, and data silos often discourage them from taking bold steps. Naturally, businesses feel more comfortable with a human expert who at least assures them that the job is done.

2. Limitations of Rule-Based Systems

Many current automation systems are rule-based, requiring explicit programming for every scenario. This makes them inflexible and incapable of handling unexpected situations. The solutions like RPA did try to give some painkiller-like solutions. However, it never looked like the most efficient solution.

3. Data Availability and Quality

Enterprises handle a massive amount of data that often varies in structure and format. Further, many AI models require vast amounts of labelled data for training. This become a significant challenge, as getting such data often involves time-consuming and costly manual labor.

4. Scalability

As businesses grow, so does the complexity of their automation needs. Scaling traditional automation solutions to handle larger volumes or more complex tasks can be difficult. Traditional rule-based systems or discriminative AI models-based automation require regular updates and maintenance as processes evolve or data change. This becomes resource-intensive and often leads to inefficiencies.

This is where Generative AI promises to solve these challenges reasonably well.

• Unlike rule-based systems or traditional AI models, generative AI models can adapt to new scenarios and generate appropriate responses or actions, even for situations not seen during training.
• Generative AI can generate synthetic data that resembles real data, helping to overcome the challenge of data availability and quality.
• APIs of generative models like GPT-3, GPT-4 and tools like Langchain and LlamaIndex can be easily integrated with existing systems to provide AI capabilities and scale horizontally and vertically.

Generative AI's Role in Automation

Imagine a world where an intelligent system efficiently manages your routine tasks, leaving you free to focus on strategic, creative, and critical thinking tasks. Generative AI doesn't just automate tasks; it automates intelligent decision-making. Its potential applications in enterprise automation are vast and exciting, promising to bring a new level of efficiency and innovation to businesses.

Typically we are more tuned to think of workflow automation in terms of digitalization. However, Generative AI may even change the definition of work and workflows/processes, changing the playground itself. This may make a certain role irrelevant and thus it may require restructuring of the team itself.

At the high level, I see the following as a broader role of GAI in accelerating automation:

1. Accelerating Automation Processes

By learning and mimicking complex patterns, Generative AI can automate content creation - from writing emails to designing graphics, making processes faster and freeing human resources. It takes data inputs, learns from them, and generates outputs without requiring explicit programming for each task.

For example - companies can use GPT-3 to generate product descriptions or promotional content at scale, significantly reducing the time and resources required for these tasks.

2. Boosting Efficiency - Augmenting Human

Generative AI significantly reduces the time and effort involved in routine tasks. Its ability to generate high-quality outputs in a fraction of the time and work round-the-clock can significantly enhance overall operational efficiency.

For example - WalkingTree has built an AI-coded test automation framework that generates BDD, and Cucumber test scripts directly from the user stories, leaving it to human experts to review and fix the placeholders. Usually an engineer will take about 200 man days to write about 1000 test scripts. However, with the assistance of generative AI, this can be done in about 40 man days.

3. Automating 'Human-exclusive' Tasks

Traditionally, tasks requiring creativity or contextual understanding were thought to be the exclusive domain of humans. Generative AI, however, is challenging this notion. From creating human-like text to designing unique visuals, it's proving capable of executing the tasks we once thought only humans could do.

For example, the Associated Press, a renowned news agency, has employed AI to automate the production of certain types of news stories, such as financial earnings reports. Here's how it works:

• The system uses Natural Language Generation (NLG), a generative AI subfield that converts structured data into human-like text.
• When a company releases its quarterly earnings report, the AI system takes the key financial data—such as revenue, net income, and earnings per share—and generates a news article that provides a summary of the company's financial performance.

The system can produce these reports within seconds of the data being available, significantly faster than a human writer. Notably, this doesn't replace human journalists but allows them to focus on reviews and more complex stories.

This use case is a striking example of how generative AI is encroaching on tasks previously thought to be exclusive to humans, demonstrating the potential of AI in various industries.

4. Personalization at Scale

Generative AI can also automate personalized content creation, tailoring messages for individual customers on a mass scale. Personalization can greatly enhance the customer experience.

Businesses can make their customers feel valued and understood by tailoring products, services, and communications to individual customer preferences. This, in turn, can lead to increased customer satisfaction and loyalty.

In a market crowded with similar products and services, personalization can help businesses stand out from their competitors. By offering a unique, tailored experience, businesses can differentiate themselves and attract more customers.

5. Data Augmentation and Acceleration of AI-model development

Generative AI can create synthetic data that resembles real data, which can be used to augment existing datasets and improve the performance of other AI models. This can be particularly useful when real data is scarce or expensive.

Specially the applications which doesn't have huge data, for example, Loan Applications of NBFCs, can benefit tremendously using this method.

In summary, Generative AI offers many benefits for enterprises, from efficiency and cost savings to improved accuracy, scalability, personalization, and innovation. It promises to play a key role, especially by overcoming the current challenges and providing new possibilities.

The best way to understand the role of GAI in enterprise automation is to look at how enterprises are currently using it.

Case Studies

Hundreds (if not thousands) of Generative AI case studies are pouring in daily. Here are a few examples that should encourage you to also apply generative AI for your organization.

1. OpenAI and GitHub's Copilot: Copilot is an AI-powered assistant that helps write code. It's based on Codex, a model trained by OpenAI on a range of public code repositories. Copilot generates suggestions for whole lines or blocks of code, helping developers code faster and with fewer errors.
2. ChatGPT in Customer Support: Companies like Zendesk use AI like ChatGPT to automate customer support. This reduces response time and frees human agents to handle more complex queries.
3. Jukin Media: Jukin Media used GPT-3 to automate writing descriptions for thousands of videos in their library, saving significant time and effort.
4. Automated Content Generation: Companies like Jasper (formerly Jarvis) use GPT-3 to generate marketing copy, blog posts, and other content, making content creation faster and more efficient.
5. Language Learning: Companies like Duolingo are incorporating GPT-4 into their language learning app to create two new AI-backed features, ‘Role Play’ and ‘Explain my Answer’, in its new subscription tier, Duolingo Max. With GPT-4, Duolingo aims to allow learners to converse freely about various topics in niche contexts.
6. Enterprise Search: Wealth management firm?Morgan Stanley?has a GPT-4-enabled internal chatbot that searches through extensive PDF format, making it easier for advisors to find answers to specific questions. The company started exploring how to harness its intellectual capital with GPT-3 and now GPT-4.?
7. Teaching assistance and mentoring: Online learning platform?Khan Academy?has started using GPT-4 to operate an AI assistant named Khanmigo, which serves both as a virtual mentor for students and an assistant for teachers in classrooms.

When you see these companies using GPT to ensure a better experience and improved automation, it encourages you to explore how it might help you.

Steps to Implement Generative AI for Automation

Implementing Generative AI for automation involves steps that extend from understanding the business needs to deploying and maintaining the solution. Here are some of the considerations:

1. Define Business Objectives and Identify Use Cases

This is the most important part of the implementation. You'll need to start by identifying the business problem that needs to be solved, which can be solved primarily using GAI. It could be automated customer service, generating the content, designing products, summarizing text, finding the closest possible case studies, etc. The solution must align with the business objectives and provide tangible value.

At all the point, you must remember that Generative AI is just another promising technology. It has to help business rather than business trying to use it because it sounds sexy.

2. Evaluate Existing Infrastructure

Please look at your current technical infrastructure and determine what needs to be upgraded or added. This could involve server capabilities, storage, cloud platforms, and other hardware or software resources.

There are aprehensions regarding data leaving your premise. If that is your case as well then you would like to consider using Azure OpenAI kind of solutions or even consider using Small but open LLMs and deploy using LLMops tools on your preferred cloud or even OnPrim.

3. Collect and Prepare Data

Generative AI models need a lot of high-quality data for training or fine-tuning. Identify relevant data sources and prepare the data by cleaning, normalizing, and labelling it appropriately.

Ensure that you have the necessary permissions to use the data and that its use complies with all relevant privacy regulations.

4. Choose the Right Model

Different generative models may be more suitable depending on the problem you're trying to solve. For instance, GPT-3 is excellent for text generation, while VAEs or GANs are more suited for image-based tasks.

Even within GPT there are different versions of the models that give you different accuracy and cost you differently. So choose them carefully by looking at the viability of your use cases.

5. Train/Finetune and Validate the Model

Using your prepared data, train your chosen generative model. This will require significant computational resources. During training, the model learns to generate data similar to the training data. You may need to tweak various parameters during this stage to optimize performance.

The closed source models (e.g. GPT3 or GPT4) don't allow you to train your models. In that case you can augment the model knowledge with your domain data using in-context learning or fine-tune the models on your domain specific requirements and achieve the desired business goals.

After training, you must validate the model using separate datasets. This helps you gauge how well the model has learned and if it can generalize its learning to new, unseen data. Make any necessary adjustments and retrain the model as needed.

6. Deploy the Model

Once the model's performance is satisfactory, integrate it into your existing systems. This might involve creating APIs for the model to interact with other software or setting it up on a cloud platform for easy access.

We always recommend using LLMOps to ensure that you are not facing standard challenge of operationalizing an AI model.

7. Monitor and Maintain

Post-deployment, continually monitor the model's performance and make adjustments as needed. Over time, the model may need retraining or further fine-tuning with fresh data to maintain accuracy.

Ensure that your use of generative AI adheres to ethical guidelines. This includes being transparent about its use, avoiding and auditing for bias, and respecting user privacy. Put these aspects into your monitoring and governance system.

Implementation Challenges and Possible Solutions

The implementation steps mentioned in the previous section may fall short of giving you a perfect Generative AI implementation. However, I am confident that this will act as a good compass. You may still like to think about the following aspects explicitly.

• Availability of High-Quality Data in your enterprise. In fact, for any meaningful analytics, you would need this, and you must build a robust data collection, transformation, cleansing and data governance strategy to drive the quality of the data.
• Lack of enough data. Consider using synthetic data wherever required.
• Decision and use of Infrastructure. Use Cloud platforms like AWS, Azure, and GCP for their scalability, computational capabilities and overall monitoring and security that they provide you. Especially do consider LLMOps from the very beginning.
• Availability of skilled resources and team. Invest in training existing staff, hire new talent, or outsource to AI consulting firms. For example, a company like WalkingTree has invested significantly in the full-stack development around LLM, and you would like to leverage them as much as possible.
• The unpredictability of AI Outputs. This aspect is well documented around Generative AI. However, if you can implement rigorous testing and validation of AI models, including checks with Humans in the loop, you can mitigate the risk of unexpected AI behaviour.
• Resistance to Change within the Org. This could be a big barrier for any automation. Build a culture of AI literacy within the organization. Demonstrate the value and benefits of AI to all stakeholders. At WalkingTree, we run a Strategy and Consulting exercise where we guide you and your team on identifying the opportunities and seeing their benefits.

These aspects are practical challenges of any implementation. However, most of the LLMs we are talking about are relatively new, and it does bring in some of their complexity. A well-designed solution approach and focused execution will help you navigate the AI journey accelerated by Generative AI.

Learning Resources around Generative AI

There are a ton of resources around generative AI. However, I will share the important videos and Links from WalkingTree that will excite you about Generative AI in general and OpenAI in particular.

1. Best strategies to navigate the LLM landscape for building intelligent apps
2. Designing LLM based Apps using LangChain and Llama Index
3. Designing Cutting Edge Industry Applications with ChatGPT
4. Conversational Al using Flutter, DALL?E & GPT 3
5. Finetuning GPT 3 for industries Use cases
6. More

Conclusion

There is no debate that Generative AI stands at the forefront of innovation today, and enterprises looking for transformation must consider this seriously to accelerate their automation. Its unparalleled ability to generate data-driven insights promises to boost efficiency, foster scalability and offer a competitive edge to enterprises. Like any new technology, there will be some teething issues, but enterprises that can mature this faster will be at a definite advantage against their competitors.

In this article, I hope I helped you understand how this can be implemented and what challenges you should anticipate. I am also completely aware that embracing this shift might be daunting. However, remember one thing the journey of a thousand miles begins with a single step. And that first step could be as simple as a conversation with WalkingTree.

As a pioneer in Generative AI, we offer tailored solutions and consultations, helping you navigate the path towards successful implementation. Contact us today at [email protected], and we can help you navigate the Generative AI journey!

References

• https://towardsdatascience.com/drawing-the-transformer-network-from-scratch-part-1-9269ed9a2c5e
• https://www.dhirubhai.net/pulse/data-asset-your-business-embracing-mindset-alok-ranjan/
• https://platform.openai.com/
• https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html