How to Get Started with Generative AI for Your Business

Nvidia reported a 265% increase in revenue as a direct result of selling chips to train models such as GPT-4o. About 83% of global enterprises today are either actively testing generative AI pilot programs or have already implemented AI for one or more manufacturing use cases. The GenAI business is booming.

Despite this, generative AI implementation often fails even with successful pilot programs.

The main three generative AI challenges CIOs cite include:

• a need for more clarity around success metrics (73%)
• budget/cost concerns (68%)
• a rapidly changing technology landscape (64%)

55% cited data security and privacy concerns, while 41% listed staffing shortages as key challenges. So, how do you determine where best to apply generative AI in your business? And when you do, how should you take the first steps? We have prepared a guide to help you get started with generative AI for your business.

It Starts with a GenAI Adoption Strategy

1. Discover the potential of generative AI
2. Identify use cases for your company
3. Build a technology strategy
4. Get started with high-ROI use cases

Step 1. Discover the potential of generative AI

It may seem obvious, but the journey starts with learning what generative AI is, how it’s used in business, and its benefits and challenges.

AI has very real limitations.

Companies like OpenAI gather information and train AI models. Most of the information is gathered online, but can’t always be legally used for training. Some of it has to be purchased as seen with Google’s recent deal with Reddit.

Airbnb first tested some use cases for generative AI, such as checking for inaccuracies in property listings, before deciding to use it to create a more personalized user experience. Financial institutions now use generative AI for banking to automate routine tasks like data entry, report generation, and compliance checks.

Today, our customers actively use OpenAI or similar services to test their business ideas. When business leaders realize they can apply generative AI for their business needs, they look for a tech company to help them implement it successfully.

Step 2. Identify use cases for your company

Look for areas that require a lot of creative output. Then, assess your current technological capabilities and resources to see where you can implement generative AI solutions. For example, you could automate content creation with generative AI (writing social media posts and providing AI-generated prototypes and models).

The following tools can help you identify generative AI use cases for your company:

Our customers usually know their pain points, but they need help finding an appropriate solution and evaluating the scope of work before starting with generative AI.

Step 3. Build a technology strategy

A systematic approach will help you to develop the right strategy for your needs.

• Assess Needs and Set Objectives: Identify business problems AI can solve, evaluate ROI, and define measurable goals aligned with your strategy.
• Feasibility Study: Assess the technical requirements and data availability and consider financial, operational, and ethical implications.
• Build and Train the Team: Recruit or train staff in AI technologies and foster a culture of continuous learning.
• Choose Technology and Implement Governance: Select appropriate AI tools and ensure secure, high-quality data management.
• Pilot and Test: Start with manageable pilot projects to test AI applications in real-world scenarios.
• Monitor and Improve: Track performance, use feedback to enhance solutions, and ensure ethical AI use.
• Scale and Optimize: Expand successful projects, stay updated with AI advancements, and regularly review your strategy.

4. Get started with high-ROI use cases

There are countless use cases for GenAI; however, not all of them are worth it. If the first three steps are done thoroughly, it should be very clear where to place your bets.

One of our clients, a leading AI, IoT, and digital workflow solutions provider for insurers, automakers, and repairers, had a challenge identifying subrogation opportunities. They followed a manual process, which was far too labor-intensive. The client wanted us to simplify the process, reduce human error, and improve claims processing efficiency.

As a solution, we developed an intelligent document processing application that uses natural language processing and machine learning algorithms. This application automates document validation and prepares them for further analysis. To improve the analysis capabilities, the client has implemented BERT and DistilBERT, large language models that evaluate claims data to identify subrogation opportunities.

We also utilized generative AI computer vision models to add the ability to analyze visual data and detect potential subrogation processes.

Results:

• 2700+ work-hours saved per month
• 9K+ documents processed monthly
• 0,84 F1 Score achieved

The GenAI Project Lifecycle

Implementing a GenAI project is a little different from the regular software development lifecycle as it emphasizes iterative development and continuous learning from data, involving extensive training, fine-tuning, and evaluation of AI models. Additionally, GenAI projects require ongoing model maintenance and updates based on new data and user feedback.

Data Collection

Every generative AI project starts with data collection. Usually, the data we use is not structured. For example, we need to generate a report from documents in different formats (pdf, doc, ppt, etc).

Create a Vector Store

A vector store is a database that stores and manages data in vectors, mathematical representations of information. It’s used in applications like search engines, recommendation systems, and AI models to find and compare similar items based on their features quickly.

Retrieval

Retrieval involves searching a database for relevant data to enhance content creation by integrating specific, stored information. In text generation, AI can extract articles or facts to
provide detailed and accurate responses to user queries. For images, AI retrieves examples with specific attributes to guide the creation of new images that match user requests.

Prompt Engineering

Prompt engineering significantly improves generative AI models’ performance, as the output quality relies on the prompt’s structure. When providing information to ChatGPT, it is important to include only the most relevant details due to the limited context, ensuring optimal results.

Train & Fine-tune The Model

Fine-tuning is used to change writing styles, by taking a pre-trained model like GPT-3 and adjusting its parameters to better match a required writing style or domain. Hence, it’s more suitable for specific applications. This approach leverages pre-existing knowledge while tailoring a model to a particular task.

The end model will be pretty costly (default GPT-3.5-turbo pricing: $0.50 / 1M tokens for input, $1.50 / 1M tokens for output; fine-tuned GPT-3.5-turbo pricing: $3.00 / 1M tokens for input, $6.00 / 1M tokens for output).

This begs the question: Is it worth the cost of achieving the required writing style?

Integration

Integration involves deploying the generative AI model into existing systems, applications, or workflows. During this stage, the project moves from development to practical use, which requires careful planning to ensure compatibility and performance. For example, an e-commerce platform can integrate a generative AI model to create product descriptions and simplify the process for sellers.

Continuous Improvement

The lifecycle of a generative AI project does not end with its deployment. Continuous improvement is necessary to keep the system relevant and effective. It includes monitoring performance, collecting feedback, and retraining the model with new data.

Open-source vs. Commercial Solutions

Developers often face a dilemma when choosing between open-source and commercial software solutions. Commercial solutions such as OpenAI offer the advantage of quick application development, but this comes with a price tag. Since charges are typically based on the number of requests, costs can skyrocket with increased usage.

Open-source alternatives are more cost-effective and offer greater control over data security. It allows users to verify security and functionality directly through accessible source code. There are other solutions, too, each with distinct behaviors and pricing structures.

Vector Store vs Fine-Tune: Choose the Right Approach

You want to optimize your AI applications’ performance, scalability, and cost-effectiveness. These two approaches offer advantages better suited to specific use cases. Here are some factors to consider:

Security, Privacy, and Compliance

Consider data protection and privacy before implementing generative AI in your business. Anonymize personal data and use encryption to protect data in transit and at rest. Incorporate privacy-enhancing technologies into the initial system design to effectively maintain user privacy.

Secure the development process: integrate security features and maintain access controls to only data and system access to authorized personnel. Develop a plan to address potential AI security threats.

Stay compliant with relevant regulations, such as GDPR, and ensure your AI systems are transparent and understandable. Regularly test your AI for bias and train your models using safe, up-to-date methodologies.

A Word of Caution

Don’t wait.

According to McKinsey, in retail alone, generative AI may increase profits by as much as $400 billion to $600 billion – so we know it can be extremely lucrative.

That said, you can’t just ‘apply GenAI’ because the technology still produces misleading or wholly fabricated results and can ignore privacy and copyright issues.

It makes sense to get the right plan in place before you start with GenAI to increase the chances that your company will be able to reap the massive rewards that this new technology promises.