In an era of rapid technological advancements, generative artificial intelligence stands out as a transformative force that has already reshaped industries and sparked innovation. Whether you’re a seasoned AI enthusiast or a newcomer eager to harness the power of creative machines, our comprehensive guide is your gateway to understanding and implementing generative AI in an enterprise environment.
Table of Contents
Introduction to Generative AI in Business
It’s easy to play with ChatGPT. The numbers only prove it. It crossed 1 million users in just 5 days from launch and gained 100 million active users by January 2023. After a meteoric rise in popularity at the beginning of 2023, it seems that OpenAI’s chatbot is now experiencing a decline in its momentum. According to Reuters,
Worldwide desktop and mobile website visits to the ChatGPT website decreased by 3.2% to 1.43 billion in August, following approximately 10% drops from each of the previous two months. The amount of time visitors spent on the website has also been declining monthly since March, from an average of 8.7 minutes on site to 7 minutes on site in August.
Is the AI and Machine Learning Bubble Going to Burst?
Despite ChatGPT’s traffic decrease, the popularity of generative AI solutions is still growing. And it doesn’t seem that the trend should end anytime soon. As reported by McKinsey, organizations include rapid deployment of efficient generative AI tools in their AI strategy, highlighting their potential to transform organizations:
- 60 percent of organizations with reported AI adoption leverage generative AI,
- 40 percent of those reporting Artificial Intelligence adoption at their organizations say their companies expect to invest more in AI technology overall thanks to generative artificial intelligence,
- and 28 percent say generative AI is already on their board’s agenda.
Moreover, if we take a closer look at the biggest players, it will turn out that ~80% of these firms either own or invest in large language models— setting the trends for the rest of the business world.
Source: AI Multiple
So, how to follow them and succeed with generative AI implementation? Building a successful AI strategy and implementing generative artificial intelligence in an organization requires taking care of different elements: data, security, compliance, culture, etc. — and because of that, it needs a smart approach.
In this guide, we will walk you through the generative AI implementation process — step by step, starting from idea to full roll-out. Let’s go!
Watch the webinar with Avi Arnon — a venture investor from Citi Ventures.
Step-by-step Guide to Successful Generative AI Implementation
The path from concept to functioning generative AI systems involves a blend of technical prowess, creative daring, and a mindset that embraces the uncertain. This is the process that requires us to be ready to experiment, iterate, and—sometimes—see our assumptions fail.
Is the game worth the candle? Considering generative AI capabilities, their impact on business operations, automating repetitive tasks, and potential cost savings, the answer is definitely yes!
However, because of the complexity of generative artificial intelligence adoption, it is important to approach such projects with a clear plan of hot to adopt AI with well-defined milestones that can serve as decision gates.
Step 1: Identify Business Goals
The first step in the process involves defining clear and specific business objectives. This initial phase is crucial for your project’s long-term success. It should precede any other steps you may think of, such as choosing the right tech stack, ensuring data safety, or even identifying the best use cases to start with.
Once you have a list of potential goals, prioritize them based on their potential impact on your business, feasibility, and data availability. Not all objectives may be achievable immediately, so it’s essential to determine which ones to tackle first.
As defining business objectives will narrow down the list of areas where generative artificial intelligence should be adopted, it sets the foundation for the entire generative AI development process.
Step 2: Choosing the Right AI Use Cases
The most commonly reported business functions using generative AI tools are marketing and sales, product and service development, and service operations, such as customer care and back-office support. However, it doesn’t need to be your case! And that’s why it is important to assess each idea against your business goals and the specific setup of your organization.
In this phase, you will identify and evaluate the potential of generative AI applications where generative artificial intelligence can be applied to address the business objectives defined in Step 1. Consider the ease of implementation vs. the potential impact on the organization and the projected ROI. Try to find answers to the following questions:
- How many departments would the implementation affect?
- Should the designed solution integrate with any existing system?
- Do you need to build custom generative AI models, or if pre-existing AI solutions can be adapted to your needs?
- Is it necessary to train the generative model with specific sets of data? If so, do you have this training data?
- What are the ethical and legal implications of the analyzed case? Does the use of generative AI align with industry regulations, data privacy laws, and your organization’s ethical guidelines?
If you don’t have experience with generative AI, you lack technical expertise, and you don’t feel confident about choosing the right case for the first generative AI project on your own, this is where an AI consulting agency may step in and help.
Desired outcomes:
At the end of this step, you should have a list of prioritized use cases that clearly define:
- the specific problem or opportunity each case addresses,
- feasibility assessments for each case,
- a value proposition outlining the potential benefits,
- technical requirements and data quality considerations,
- ethical and legal considerations,
- ROI projections for each case,
- and prioritization of use cases based on their impact on business processes and their strategic importance.
Gathering this information ensures that your efforts are focused on the cases that offer the greatest potential for business value and align with your organization’s goals.
Check out the advice from AI experts: Alain Bindels, Ranjan Roy, and Matt Kurleto
Step 3: Project discovery & planning
Once you know which use case you want to focus on, it’s time to plan this undertaking thoroughly.
Such a project plan serves as a roadmap for the subsequent phases of implementation, ensuring that even if some assumptions or technical details change, you have a clear direction for the technical aspects of the generative AI project and that it aligns with your organization’s goals and priorities.
At this stage, you should think about:
- identifying the AI problem to be solved (mind that it is not the same as the business problem you are approaching; the AI problem refers to technical issues),
- technical solution selection (what AI model, how to use it, whether to fine-tune it, whether to connect it to an external knowledge base, etc.),
- technology stack identification (cloud services, frameworks, libraries, vector databases),
- the architecture of the designed solution (how generative artificial intelligence will integrate with external databases, libraries, and tools),
- identifying success metrics and key performance indicators — both technical and non-technical,
- cost assessment.
You should also review the existing data and verify its volumes and data quality. Based on that, you will be able to decide whether you will use 0-shot, 1-shot, or few-shot learning or if you need to include fine-tuning in your estimates.
Note that, unlike predictive AI, generative AI does not require you to have large volumes of data. It does not require much data cleaning. Even with a small amount of data, you can build a benchmark to facilitate the prompt engineering process.
On the other hand, lack of high-quality data requires engineers to define success metrics or design an evaluation process.
Start Your Gen AI Project Smart
To support organizations at this stage and make sure that all key areas are covered, we designed a Generative AI Exploratory workshop, where we:
- analyze your business goals,
- discuss the potential use cases, analyze their strengths and weaknesses,
- identify the AI problem to be solved,
- define success metrics — both business and technical (if possible),
- set priorities (cost, time, security).
After the workshop, the technical team gathers to build a report. At this stage, we define the technical solution needed to achieve the goals (what’s the right AI model, how it should be used, whether we should train it or connect it with an external knowledge base, etc.). We also try to roughly estimate the cost of using the AI model, specify the technologies, and plan the architecture of the designed system
Step 4: AI Proof of Concept
A proof of concept (PoC) is a small-scale test or “experiment” that helps you check if your idea for using generative AI will work — from a technical standpoint. It is relatively cheap to build (on average, it falls between $15k and $20k) and carries a very low risk.
While the opponents of PoCs say that it makes the whole project a bit longer, it’s worth noting that it also helps to minimize the risk related to generative AI implementation and lets you drop the project earlier if the PoC fails. In other words, if the hypothesis of using generative artificial intelligence is right, a proof of concept stage is redundant. If it’s not — it will help you save time and money, increase confidence, and avoid the sense of failure. The more complex implementation you have in mind, the more valid it gets to build a proof of concept.
A proof of concept may include:
- data collection for AI model training and testing (if necessary),
- exploring and selecting appropriate generative AI algorithms,
- setting up the development environment,
- building the prototype AI model and testing it,
- gathering feedback from stakeholders and users,
- hypothesis verification (the assessment of the results).
Based on the results of the PoC phase, you will be able to decide whether to continue the project, drop it, or iterate.
Learn from the hands-on experience!
Step 5: AI Pilot / MVP
When you are confident to start the project — you validated your hypothesis about using generative AI with a proof of concept — it is time to start the actual implementation.
While PoC focuses on technical viability, the primary purpose of an MVP is to provide a functional version of a product that can perform its core functions, provide a positive customer experience, and deliver value to users. This step helps move from the experimental phase to a more practical and usable product.
The MVP stage may include:
- refining the AI model, making improvements to enhance its performance and capabilities,
- expanding data collection (if needed),
- UI development,
- integrating AI with existing systems (if applicable),
- ensuring the designed solution complies with relevant industry regulations and data privacy laws,
- gathering user feedback,
- fine-tuning and performance optimization.
The goals of the MVP may vary depending on the project. They should be defined and specified with success metrics at the beginning of the project. In general, they should help you confirm if your product is able to solve the business problem chosen at the earlier stages of the project. Once you validate this hypothesis with users, you’re ready for the next step.
Step 6: Full Gen AI Implementation
With a successful MVP that has met its objectives and earned positive feedback from users, it’s time for the transition from a prototype to a fully operational solution that will meet your organization’s needs and help you achieve the business goals defined in step 1.
At this stage, you scale the generative AI solution up to accommodate larger datasets, serve more departments (if applicable), add new features, integrate this solution with existing systems and processes, strengthen security measures, implement monitoring tools, and establish maintenance procedures to assess and optimize the AI models’ performance continuously. Speaking of which…
Step 7: Optimization and Maintenance
Generative AI projects are ever-growing & transforming. They change as the AI models learn from interactions with users, as the knowledge base they are connected to grows, and finally — as technologies that power them are being developed or… the other way around, they get dumber. After all, each and every model is only as good as the data it was fed with. Because of that, it is particularly important to monitor your generative AI-powered product, improve it on a constant basis, and ensure that your model continues to provide value over time.
Consistent monitoring of machine learning models in production is crucial to understanding the evolving dynamics of the use case, especially in relation to incoming data. Regularly tracking model performance will highlight if its accuracy deteriorates over time and pinpoint areas where the AI model might underperform. Addressing these issues promptly helps ensure that the model continues to perform effectively and deliver the expected value.
Filip Kaiser, AI Tech Lead
The good news is that maintenance and optimization are far less time-consuming than the earlier stages of the project. Even though the actual workload will depend on the complexity of your solution, staying proactive in monitoring and optimizing your generative AI system will ultimately save time and resources in the long run.
Generative AI Development Case Studies and Real-World Examples
Now, let’s apply the theory to real life and examine how it works in practice.
Client: a global technology company that supplies systems for passenger cars, commercial vehicles, and industrial technology
Project overview:
Generative AI Workshops
First, we analyzed the business goals and listed several applications of GenAI. Then, we evaluated them and prepared a report summarizing our findings and recommendations regarding the next steps and tech stack.
An AI PoC
Once we evaluated the use cases and chose the one to work on, we developed a proof of concept. Its goal was to prove the technical feasibility of the designed solution. It was connected to the client’s internal database to make sure that the AI model was able to handle that specific data sets.
The collected data was indexed and organized in a searchable database, facilitating quick and efficient retrieval and analysis of historical maintenance records. When asked a question, the assistant extracted information from the user inquiry and searched through the database to help users securely access internal documentation and get the insights they needed.
The Pilot Phase
Once we validate the solution’s technical feasibility, we could start the pilot and give the AI assistants to users. Using structured and unstructured text data, such as maintenance notes, the assistant facilitates the rapid identification of the reported issues. It provides the staff with relevant, summarized information, answers their queries, and offers suggestions for resolving common problems.
Full Implementation
Once we gather users’ feedback, it will be possible to expand the solution, add necessary features, and plan the company-wide adoption. In the future, it is also planned to expand the feature’s capabilities, address technical challenges, and refine the user experience based on the collected feedback. Additional challenges will certainly arise as new data sources — in different languages and with different data structures — will be added.
Read the full case study: Generative-AI-powered Maintenance Assistant
Generative AI Enterprise Adoption in 6 Steps
That’s it. These six steps will help you ensure the success of your generative AI project — or avoid a spectacular failure!
What’s important is that the division between these steps lets you step back and cancel the project (or iterate) as soon as you learn it doesn’t go as planned. And let’s face it: a lot of things can go wrong. There may be no product-market fit, it may turn out that your stakeholders have unrealistic expectations, the desired metrics overrun the technical capabilities of existing technologies, or the data you have is inadequate for the problem you are approaching. Sometimes, large language models will work perfectly accurately in laboratory conditions but fail in production (e.g., due to discrepancies in data).
Finally, the challenges, the needs, or even the data itself may change so dynamically that the initial project assumptions outdate faster than it is possible to deliver the solution. However, being aware of these risks, you can prepare and minimize them to an absolute minimum.
Gen AI projects are, in fact, R&D and should be approached as such: iteratively and with clear milestones. If you want to innovate, if you’re ready to experiment, if you’re ready to fail and draw conclusions from failures — then generative artificial intelligence is definitely a technology you should explore. And with the right process of adopting innovations in place, you can increase your chances of successful implementation.
