Generative AI implementation guide

Introduction

Our world is becoming increasingly tech-driven. The hype surrounding AI, machine learning, and other new technologies is a testament to that. These days, AI is way more than just a buzzword. It’s a total game-changer, redefining how we work, have fun, communicate with others, and many other things. In 2024, it seems like no sphere of our lives will be left untouched by the mighty march of AI. That’s why it’s time to start thinking about how to implement generative artificial intelligence in your business.

Not long ago, I would have said, ‘Imagine having a creative assistant that can generate content, draft personalized emails, and even design product prototypes, freeing you to focus on the bigger picture.’ But that’s not a dream anymore. These capabilities are already here, and the world is abuzz with excitement about the potential they hold for businesses and products.

Artificial intelligence (AI) is no longer a dream but a reality, so the time has come to find ways to implement it in your business or product to use its immense power to improve efficiency through innovation.

Foundation models, which are large-scale, self-supervised models trained on vast quantities of unlabeled data, serve as a base for AI systems that can perform multiple tasks such as generating text, images, and audio content.

Now that we have briefly covered what artificial intelligence (AI) and its cooler brother, generative artificial intelligence (GenAI), are, we have to explore further what all the buzz is about. GenAI has been rapidly gaining traction amongst businesses, professionals, and consumers. If you want to stay ahead of the game, you have no right to miss the myriad opportunities this marvel of technology offers. With your success in mind, we created this guide explaining in detail how best to implement generative artificial intelligence in your business.

No matter if you are a seasoned AI enthusiast who has implemented various advanced AI solutions in countless products or just a newcomer thinking about creating a chatbot, our guide will be helpful to anyone willing to turn things around and make the creative power of machines serve you and the goals of your business. Read on to learn not only the secrets of AI implementation in an enterprise environment and what steps you’ll have to take to make it happen but also how many other companies have already succeeded.

Generative artificial intelligence implementation is not an easy task. That’s why we are here to help you make this process not only easy, but exciting as well. Hopefully, as exciting as using AI tools like DALL-E for the first time and creating the weirdest and funniest art imaginable. Read on to learn how to implement generative artificial intelligence.

What is Generative AI?

As we explained in our previous article, “The New Era of Mobility: Local SLMs as the Key to Security and Reliability”, unlike traditional AI which is for analyzing data and making predictions, GenAI is for generating new content, ideas, or solutions based on the data it’s been trained on.

Unlike traditional AI models, which mostly classify and predict, generative AI can create entirely new outputs. Think of it as a digital artist or a super-smart intern that learns from the best and then produces original work. Generative adversarial networks (GANs) consist of two neural networks that work together to create convincingly authentic images, videos, and audio.

Generative AI is not just limited to visual content; it also plays a significant role in natural language processing. Generative models, including techniques like GANs and transformer networks, are used to develop and better these applications.

what is artificial intelligence

Business Applications of Generative AI

For now it sounds all fun and games. I’m sure you wonder what a serious business may gain from using GenAI. What tasks can it assist with? How can it make you make more money or reduce costs? How can it help you make your customers more satisfied? Finding out how to implement generative artificial intelligence is important, but what for?

These are all valid questions. So let’s try to answer them!

Even if we simply scratch the surface, we’ll see that GenAI’s applications are truly diverse and exciting. Take marketing, for example. Although still far from being able to surpass human creative capabilities, it can already immensely speed up and ease the process of crafting personalized ad copy. Or create highly personalized social media posts that use your tone of voice and resonate best with your audience.

Product development teams can use it to brainstorm new concepts or even create 3D models. Generative AI can also generate code, automating coding processes and software development tasks. Customer service departments can deploy AI-driven chatbots to handle routine inquiries, freeing up human agents for more complex issues, and dealing with tough customers. Large language models like GPT-3 and PaLM 2 play a crucial role in enabling these generative AI applications.

Any company can significantly accelerate the proces of generating documentation, as GenAI tools can churn out manuals, guides, reports, and other types of documents. It is capable of creating product descriptions

No need to think of drug discovery or anything similarly advanced. GenAI may help with search engine optimization. It can take any original input data, raw data, even unlabeled data and with the help of massive compute power and using generative AI algorithm, find underlying patterns invisible to the naked eye.

The possibilities are endless, but the key is identifying where generative AI can truly add value to your business processes.

Assessing Where Generative AI Fits in Your Business

Adding AI capabilities for the sake of adding them and mindlessly jumping on the bandwagon is not smart. Before diving headfirst into AI, you should evaluate your business needs. Look at your current operations and identify areas that could benefit from generative AI.

Neural networks utilize different learning approaches, including unsupervised or semi-supervised learning, to expand generative AI capabilities such as generating realistic human faces, synthetic data, and facsimiles of particular humans.

Are there repetitive tasks that eat up valuable time? Are there creative processes in your company that could use a fresh perspective?

Synthetic data generated by models like GANs and VAEs can be used for AI training, content improvement, and data privacy augmentation.

Is there any process in your app or website where your customers leave vast troves of data that sit there untapped into? Can any part of your app be more personalized without necessarily harassing your users for more information?

It is here where generative AI can make a real difference.

Identifying Suitable Tasks

Start by listing tasks that are repetitive, time-consuming, or require a high degree of creativity. For example, content creation, customer support, and data analysis are prime candidates for AI integration. The benefits of generative AI include faster product development, improved customer experience, and improved employee productivity. Next, assess the feasibility of AI adoption by examining the quality and availability of your training data and your existing technical infrastructure.

Evaluating AI Use Cases

Once you’ve pinpointed potential areas for AI integration, it’s time to drill down into specific use cases. Prioritize these based on their potential impact, feasibility, and alignment with your business goals. Ask yourself the following questions:

  • What particular problem or opportunity does this use case address?
  • What technical requirements and data quality considerations are involved?
  • What are the ethical and legal implications?
  • What is each use case’s projected return on investment (ROI)?

This evaluation will help you focus on use cases that offer the most significant potential for business value and align with your strategic objectives.

Practical Steps to Implement Generative AI in Your Business

Developing a Roadmap for Successful AI Integration

Ok, you have that list of potential use cases. You have a rough idea of where you can employ generative AI technology. You are finally ready to use generative AI’s ability to automate and create anew. How do you get from concept to functioning generative AI systems? Now is the time to focus on how to implement generative artificial intelligence the right way.

You’ll undoubtedly need technical knowledge, an appetite for risk, and readiness to embrace the uncertain. Just as is the case with software development in general, you should be ready to experiment a lot, iterate incessantly, and—fairly often—see our assumptions fail.

Is the game worth the candle? Absolutely! When you consider the transformative capabilities of generative AI, its potential to revolutionize business operations, automate repetitive tasks, and unlock significant cost savings, the answer is a resounding yes!

The thorn in our side? GenAI implementation is a complex task, plain and simple. Therefore, we can’t stress enough the importance of having a well-thought-out plan. You should have well-defined milestones, intermediate metrics, and reviews after each step of the process.

Implementing generative AI is a multifaceted endeavor. It requires a comprehensive roadmap, guiding you from the initial concept to full-scale deployment. A structured step-by-step approach is a must. So here’s how we see it:

Step 1: Define Clear Business Objectives

First, set clear and specific business objectives. These will serve as the foundation for your AI implementation strategy. Prioritize objectives based on their potential impact, feasibility, and alignment with your business goals. This clarity will help narrow down the areas where generative AI should be adopted.

This should be done way earlier than choosing the right tech stack, ensuring data safety, identifying the best use cases or anything else. Many teams have experienced a situation where the whole process of implementing a new feature crumbled due to the focus being in the wrong place.

Step 2: Identify and Evaluate AI Use Cases

Based on the business objectives you defined in Step 1, you will identify and evaluate specific use cases where generative AI models can be applied.

Natural language processing (NLP) plays a crucial role in enhancing chatbot performance and generating content for automated custom software engineering.

Think about the ease of implementation, potential impact, and ROI for each use case. Ensure that the chosen use cases align with your business objectives and address specific problems or opportunities.

There are a few questions you may find helpful to find answers to:

  • Will the implementation of GenAI affect only one department or several of them?
  • Will the designed solution be a stand-alone one, or will it be integrated into the existing one?
  • Does your team have to build a custom model from scratch, or can some pre-existing solutions be adapted to meet your needs?
  • Do you need to train your model with some specific data? Do you have this data at hand, or do you need to collect it first? Is it necessary to train the AI model with particular sets of data?
  • Is there any risk that your use case has ethical and legal implications? What is your industry’s regulatory escape? Do you have to be mindful of any data privacy laws? Does your company have any ethical guidelines you have to follow?

Need help with answering these questions? Not sure if you have enough experience with generative AI? Do you feel the lack of technical expertise is holding you back and choosing the right use case for your first AI project is all a bit too much? Feel free to contact Applandeo so we can lend you a helping hand.

Once you found answers to the above questions, you should have the following things ready:

  • what specific problem or opportunity each use case addresses,
  • feasibility assessments for each use case,
  • a value proposition with an outline of the potential benefits you intend to get,
  • technical requirements and data quality considerations,
  • ethical and legal information,
  • ROI projections for different use cases,
  • prioritization of each use case on the basis of their impact on business processes and their strategic importance.

Have you got all this information? Now, you can focus your AI implementation efforts on the use cases offering the most tremendous potential for business value and aligning with your organization’s goals.

Step 3: Go Through Project Discovery & Scoping, Follow with Planning

Now it’s time to get into the nitty-gritty of planning your generative AI project. Identify the AI problem to be solved, select the appropriate technical solution, define your technology stack, and outline the architecture of your solution. Establish success metrics and key performance indicators (KPIs) to measure progress.

Sounds like a lot to do, but once you’ve learnt how to implement generative artificial intelligence, the benefits will start flowing in.

Step 4: Develop a Proof of Concept (PoC)

Create a small-scale PoC to test the technical feasibility of your AI solution. This phase involves data collection, model training, and testing. Gather feedback from stakeholders and users to validate your hypothesis and make necessary adjustments. So far, so good!

how to implement generative artificial intelligence

Step 5: Launch an AI Pilot or Minimum Viable Product (MVP)

Transition from the experimental phase to a functional product with an AI pilot or MVP. In this stage you should focus on refining the AI model, expanding data collection, developing the user interface, and integrating the solution with existing systems. Next gather user feedback and optimize performance based on real-world usage.

Step 6: Execute Full AI Implementation

Once you’re done with creating the MVP, time to get down to finally implementing GenAI.

Start by scaling up the generative AI solution to accommodate larger datasets, serve more departments, and add new features. At this stage, you can also strengthen security measures, implement monitoring tools, and establish maintenance procedures to continuously optimize the AI model’s performance.

Step 7: Ensure Optimization and Maintenance

Generative AI projects require ongoing monitoring and optimization. Regularly track model performance, address issues promptly, and make improvements to ensure the AI system continues to provide value over time. Stay proactive in maintaining and optimizing your generative AI solution.

These are the main seven steps on the way on how to implement generative artificial intelligence and ensure your business grows. Now let’s turn our attention to the potential problems you may encounter.

Overcoming Challenges, Mitigating Risks, and Ensuring Successful Generative AI Adoption

Implementing generative AI isn’t without its challenges. Here’s how to navigate some common pitfalls:

Data Quality and Availability

High-quality data is the bedrock of effective generative AI. Ensure your data is clean, relevant, and comprehensive enough to train AI models effectively. Establish data governance practices to maintain data integrity over time.

Stable Diffusion is a key generative AI model that enables users to generate photorealistic images given a text input, similar to other tools like GPT-3, ChatGPT, Midjourney, and Gemini.

Technical Expertise

Generative AI projects require specialized technical skills. Consider collaborating with AI experts or consulting agencies to bridge any knowledge gaps and ensure the success of your implementation.

This is where Applandeo can help you start from scratch or ease the process right in the middle with our team of experienced tech consultants.

Ethical and Legal Considerations

Generative AI raises important ethical and legal questions, from data privacy to bias and transparency. The importance of these can’t be overstated. If you want your business to succeed, make sure your AI solutions comply with any relevant regulations and ethical guidelines. Just to be on the safe side, implement mechanisms to monitor and mitigate biases in AI outputs.

You can take a look at the risks and regulatory challenges of GenAI in this article in Harvard Business Review.

Change Management

Introducing generative AI can disrupt existing workflows and processes. Therefore, effective change management strategies are of crucial importance. These will ensure smooth transitions from one version to another. Here you should engage stakeholders early on, provide necessary training, and communicate the benefits of AI adoption to gain buy-in and support.

Information awareness is key here.

Risk Mitigation

Develop a comprehensive risk management plan to address potential challenges. Conduct thorough risk assessments, define contingency plans, and continuously monitor the AI system’s performance.

Is there a risk of a data leak? Is everything secure enough? Do you run the risk of losing more users than gaining?

It seems like all in all it’s fairly easy how to implement generative artificial intelligence. How about increasing the GenAI’s impact and scale? Find out next!

Maximizing the ROI of Generative AI: Measuring Impact and Scaling Up

To maximize the return on investment (ROI) of generative AI, it’s essential to measure its impact and scale up successful implementations. Here’s how you can do it:

Define Success Metrics and KPIs

Establish clear success metrics and KPIs to measure the impact of generative AI on your business. These metrics should align with your business objectives and provide quantifiable insights into AI performance.

What can these be? Think the typical indicators like latency, accuracy score, adoption rate and frequency of use, abandonment rate, user satisfaction, and so on and so forth.

Monitor and Analyze Performance

Continuously monitor the performance of your generative AI solutions. Analyze the data to identify trends, areas for improvement, and opportunities for optimization. Regular performance reviews ensure that the AI system remains effective and aligned with business goals.

Consider establishing some kind of a frequent GenAI implementation audit where you will check all of the metrics set up previously.

Scale Up Successful Implementations

Once you’ve validated the success of your AI solutions, scale them up to other areas of your business. Expand the scope of AI applications, integrate new features, and leverage AI insights to drive further business growth.

The chatbot was a success? Why not have interactive features on all pages then?

Smart search lead to an increase in user retention? How about adding smart fields elsewhere on your website or in your app?

Foster a Culture of Innovation

Encourage a culture of innovation within your organization. Promote continuous learning, experimentation, and collaboration to keep pace with evolving AI technologies. Empower employees to explore new AI applications and contribute to the ongoing success of AI initiatives.

More often than not, it is employees who drive innovation and come up with valuable input, so giving them the time and platform to share their thoughts won’t hurt.

Conclusion: Embracing Generative AI to Drive Competitive Advantage and Business Growth

Integrating generative AI into your business isn’t just about keeping up with the latest technology trends—it’s about driving real, tangible business growth. Put simply, this way you’ll increase your revenue. By understanding the potential of generative AI, identifying suitable use cases, and following a structured implementation roadmap, you can make AI work for your business. This way you will harness the power of AI to significantly improve creativity, efficiency, and innovation within your organization.

The time has come now to embrace generative AI as a strategic tool to stay ahead of the competition, improve customer experiences, and turn operations up a notch. With careful planning, continuous optimization, and a proactive approach to addressing challenges, your business can harness the full potential of generative AI to achieve sustainable success in the digital age. In 2024 most business are either considering AI implementation or are way down that road.

By following the strategies outlined in this guide, you can better navigate the complexities of generative AI implementation. Take a closer look at the steps we outlined here, and see how you can leverage AI’s capabilities to drive significant value for your business. The future of AI is here, and it’s time to unlock its power to transform your organization. We at Applandeo are here to help you on this journey.

Please fill out the form below to contact us, and our experts will answer any questions you may have at this stage. We can discuss a tailor-made AI project for your needs or help you implement AI in your business at any step.

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