Generative AI Implementation Guide for Business Leaders

The arrival of generative AI marks a milestone comparable to the advent of the internet or the spread of mobile devices, promising a revolution in individual and business productivity. An impressive 82% of organizations that use or plan to use AI believe it will significantly transform their industries, positioning it as an indispensable tool for the business future.

What sets generative AI apart is its accessibility and ability to tackle everyday challenges in innovative ways. By facilitating interaction through everyday language, this technology allows anyone, with just the ability to ask a question to a search engine, to interact with chatbots or virtual agents to get answers, generate content, create images, summarize documents, and much more.

Furthermore, generative AI excels in four key functions: creation, summarization, detection, and automation. These capabilities translate into transformative applications for businesses, such as improving customer interactions, employee training, marketing content generation, and business process optimization. McKinsey & Company estimates that generative AI could add between $2.6 and $4.4 trillion annually to the global economy, highlighting its potential to accelerate, automate, scale, and significantly enhance business processes.

Some base models that might be more familiar for these generative AIs are LLMs, like Chat GPT or Gemini. However, merely implementing these models does not provide all the benefits to your company. Rather, they act as engines on which your generative AI application is customized, shaped, and propelled through human input and pattern identification, allowing your company's people to focus more on strategy that fosters growth and less on repetitive tasks that take away time and motivation from workers. The other part that makes up generative AI applications is traditional programming, which is more precise, helping to set limits and rules for AI to avoid hallucinations, excessive costs, and ensure that it is used to the company's benefit.

This detailed guide, inspired by Google Cloud's recommendations, will lead you through the crucial steps to implement generative AI in your company, regardless of the sector you operate in.

Step 1: Identify a Specific Domain

The first step is to select a domain within your company that would significantly benefit from generative AI. It is crucial to group all use cases of this domain and execute them sequentially. Focusing on a directed approach ensures better results. Moreover, we can leverage how the model, as it is perfected and receives more data, becomes smarter. This, subsequently, will reflect in other use cases or areas of the domain.

It can be any area facing unique challenges, from customer service to product development and marketing. For example, in the domain of customer service and support, we could start with intelligent chatbots to improve customer service. Then, move on to another use case where AI helps us identify response times and schedule support visits. Finally, implement a use case where AI optimizes routes so that technicians have more efficient displacements and communicate approximate visit times to customers. This would improve customer experience and reduce mobility costs and technicians' time.

Step 2: Choose an Archetype

After selecting a specific domain to implement generative AI, it is crucial to start with a focused and directed approach. Determine which function or position within the chosen domain you wish to improve first with generative AI. Starting with a single use case allows for a more controlled and manageable implementation, facilitating process optimization and productivity increase in a specific area.

This step-by-step method helps you visualize how AI can optimize processes and effectively increase productivity. Once this initial use case is functioning correctly and you have identified tangible improvements and benefits, you can proceed to replicate success in other archetypes or areas of your company, gradually expanding the impact and reach of generative AI in your organization.

Keys to identifying it:

• Positions that are difficult to retain or hire due to repetitive tasks that generate boredom and a lack of employee purpose.

• Opportunities to automate tedious tasks.

• Creating an environment of safety, compliance, and quality, such as supervisory tasks to ensure specifications are met. Using AI can help identify the poor condition of a product, avoid potential future penalties, and improve product quality.

Step 3: Determine Data Sources

Selecting data sources is crucial for the success of any generative AI model, as these data form the foundation for AI learning and development. It is vital to collect data that is not only accurate and relevant to the specific focus area but also clean and well-organized. In the realm of marketing, for example, it would be necessary to compile data on customer interactions, results of previous campaigns, and consumer preferences, coming from various internal sources and carefully prepared to avoid inconsistencies or biases.

It's important to identify and organize relevant data to ensure the model can learn effectively, in addition to mitigating risks of errors and improving AI responses.

Step 4: Create a Tiger Team

To lead the implementation of generative AI, it is crucial to form a tiger team composed of three key actors, each bringing unique skills and perspectives:

Business Area Individual: This member is essential for deeply understanding the work requirements, workflows, challenges, and daily needs of the selected archetype. Their knowledge of the business domain ensures that the generative AI solution aligns with the company's operational and strategic needs.

Prompt Engineer: Responsible for converting the needs, actions, and expected outcomes of the business archetype into clear and effective prompts for generative AI models. This role is vital for designing the interaction between the AI model and end-users, ensuring that the generated responses meet expectations and contribute to business objectives.

ML Operations Chief: Takes care of preparing and managing the application in production. Their task is to ensure that the generative AI model integrates properly into the company's technological environment, operates efficiently, and is ready to be scaled as needed.

With this multidisciplinary team, your company will be prepared to effectively implement generative AI, from conceptualization to operation in production, ensuring that each step aligns with strategic goals and operational needs.

Step 5: Define Objectives

When defining the objectives of your generative AI project, it is crucial to establish clear and specific goals you hope to achieve. This will guide the project's development and offer a framework for assessing its success. Objectives should be measurable, achievable, relevant, and time-bound (SMART) so that the team has a clear direction and can measure progress towards these goals. For example, if you are looking to improve operational efficiency, you might aim to reduce customer service response time by 20% within three months. Setting clear objectives from the start ensures that all project activities are aligned and contribute directly to the desired outcomes, allowing for a more focused and effective implementation of generative AI. Additionally, it is essential to ensure human interaction to oversee these objectives and thus have constant feedback on progress.

The results some organizations have reported after adopting AI include a 66% increase in operational efficiency, a 57% improvement in customer experience, and a 49% boost in innovation. For more information on these figures, you can consult Google's manual.

Useful objectives might include: accuracy and quality indicators of the model, metrics of tasks performed and response time, customer surveys, cost comparisons with outsourcing or manual processes, quantification of incorrect or undesired outcomes from the model, and metrics of the model's impact on sales increase or claim reduction, among others.

Step 6: Design Prompts with the Tiger Team

It is crucial to collaborate with the Tiger Team to design effective prompts that ensure useful and relevant responses from generative AI. This interdisciplinary team, expert in business needs, AI models, adjustments, and application integration, is fundamental to converting business objectives into clear instructions for AI. Working together, they can create prompts that properly guide the AI model, thus improving interactions for the end-user. Consulting examples of prompts and leveraging the skills of each member accelerate this essential step, making a significant difference in how AI meets business objectives.

Step 7: Generate UI and UX

Developing an intuitive user interface (UI) and user experience (UX) for the generative AI model is crucial for its success in production. This includes simplifying the interface and design, such as offering options for users to customize the model output according to the desired tone, like "formal," "informal," "technical," or "creative".

It is crucial to create a coherent workflow that intuitively guides users through the AI's capabilities, ensuring that the UI/UX seamlessly integrates into the broader ecosystem of existing applications. Additionally, it's important that the interface is responsive and accessible on various devices to improve accessibility and adoption of the model by end-users.

Step 8: Expand to More Users

Once the model is adjusted and tested with a small group (between 2 or 3 people), gradually expand its use to more users within the selected archetype. Increasing between 5 and 10 people is suitable. Ensure that each integrated person is familiar with the archetype's operation, through workshops or training, and then, through surveys, evaluate their valuation and obtain feedback to continue improving the archetype.

Step 9: Create an Operations Plan for the AI Model

Creating an operations plan for the generative AI model is crucial for its success. This plan should cover everything from technical infrastructure to data security, including continuous updates and improvements to the model, and A/B testing to evaluate changes. Having a monitoring system to constantly review the model's performance and security is essential, as well as a protocol for human intervention when necessary. A/B testing is crucial for the iterative improvement of the model, contributing to its adaptability and optimization.

Step 10: Expand to Additional Use Cases

After successfully implementing and optimizing the first use case, it is advisable to expand generative AI to more applications within the same business domain. Ensure that each new implementation benefits from previously learned lessons, thus continuously improving the effectiveness of AI. This iterative approach not only improves outcomes in specific use cases but also allows the knowledge gained to be applied to new challenges within the company, maximizing the technology's impact.

RELEVANT USE CASES AND REAL EXAMPLE

Customer Service Automation at Wendy's:

Generative AI has revolutionized how businesses interact with their customers, offering quick and personalized responses through chatbots and virtual agents. A notable example is Wendy’s, which has used generative AI, developed with Google's natural language technology, to automate ordering services from the car, significantly improving efficiency and the customer experience when handling personalized orders. For more details, it is recommended to consult the mentioned Google guide.

Industries Benefited by Generative AI and Their Specific Use Cases

Generative AI extends across various industries, each with its unique use cases that highlight this technology's transformative potential:

• Retail and Consumer Packaged Goods (CPG): 1:1 personalization in marketing, conversational commerce, and new product development.

• Media and Entertainment: Content discovery, creative assistance, and personalization of the user experience.

• Financial Services: Enhanced virtual assistants, financial document analysis, and personalized recommendations.

• Manufacturing: Monitoring of machine-generated events and supply chain optimization.

• Health and Life Sciences: Digital concierges for patients, acceleration of prior authorization, and generation of clinical trial reports.

• Communication Service Providers: Customer service automation, network planning and operations, and assistance with creative and advertising content.

These use cases illustrate how generative AI is driving innovation and optimizing processes in various sectors.

How Can Botia Assist You in This Process?

Implementing generative AI may seem like an overwhelming task without the proper experience. Navigating the steps for its implementation, from selecting data sources to creating an intuitive user experience, requires specialized knowledge that can be difficult to quickly accumulate. However, this challenge is significantly simplified when you have the support of an experienced team.

The Botia team, experts in Machine Learning, prompt engineering, and software development, specializes in implementing advanced technologies like Google Vertex AI, AWS Step Functions, Microsoft AI Studio: (Copilot, Power Automate), Google AppSheet, Make, Zapier, ChatGPT, Botpress, WhatsApp API, Dialogflow, and Amazon Lex. Our personalized approach ensures tailor-made solutions that align with your business objectives, propelling your leadership in the era of generative AI with innovation, autonomy, efficiency, and informed decisions.

This approach allows you to implement generative AI more easily, effectively, and quickly, making the most of its benefits without worrying about technical and operational details. With Botia, you choose a partner committed to your success, ready to help you forge your company's future in the exciting world of generative AI.

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