GenAI Implementation Guide

Generative Artificial Intelligence (GenAI) is emerging as a revolutionary tool in the business world, opening up new possibilities in content creation, automation, and data analysis.

But what is its actual economic potential? According to McKinsey, GenAI could generate between $200 billion and $340 billion annually in the banking sector alone, while in retail and consumer goods, the estimated impact ranges from $400 billion to $660 billion per year. However, to effectively integrate GenAI into a company’s operations, businesses must first establish a strong foundational understanding of its capabilities, management, and associated risks. This article provides a concise guide to help organizations navigate the implementation of GenAI effectively and strategically.

1. Foundations of GenAI

1.1 Generative vs. Classical AI – Key Differences

Classical artificial intelligence (AI) focuses on developing computational systems capable of intelligent behavior, such as learning from existing data. It excels in performing specific, narrowly defined tasks where historical data is available—typically in numerical form (though not exclusively)—which serves as training material. Generative AI (GenAI), however, takes a step further—it does not merely analyze data but also creates entirely new content, such as text, images, or even action plans for unique situations. This fundamental difference significantly expands the scope of applications for both technologies.

Classical AI is best suited for tasks requiring data analysis and pattern-based decision-making, such as recommendation systems or market trend forecasting. In contrast, Generative AI thrives in areas where creativity, contextual understanding, and unique solutions are crucial, rather than relying solely on historical data. As a result, it is widely used for generating marketing content, writing code, explaining complex topics, and even composing music.

1.2 How It Works

Generative AI is based on advanced machine learning models, such as GPT (Generative Pre-trained Transformer), which analyze vast datasets to learn patterns and relationships. This allows them to generate new content—including text, images, and sound—while also understanding user intent and context.

These models are trained on billions of examples, enabling them to predict and generate responses that closely resemble human understanding. A crucial aspect of using generative AI is crafting prompts—precise text-based instructions that define a task for the model. By constructing well-formulated prompts, users can influence the quality and specificity of AI-generated responses, tailoring the content to their specific needs

1.3 Business Applications

Thanks to its creative capabilities, GenAI finds applications across various industries. It can personalize content, automate internal processes, and even suggest new business strategies. Moreover, its potential and capabilities continue to evolve, driving innovation in fields such as finance, healthcare, and logistics.

In future articles, we will discuss how to effectively identify use cases within your organization. For now, let’s explore some of the most popular implementations of GenAI:

• Automated Document Comparison – GenAI analyzes and compares contracts, legal, and financial documents, identifying differences, key information, and potential risks, streamlining processes in legal and financial departments.
• AI HR Assistant – By automatically processing candidate applications, comparing CVs against job requirements, and even conducting preliminary interviews, GenAI supports HR departments and accelerates recruitment processes.
• AI Helpdesk Assistant – GenAI answers customer or employee inquiries, diagnoses technical issues, and suggests solutions, reducing the workload of IT and customer service teams.
• Marketing Campaign Content Creation – GenAI generates personalized advertising campaigns based on a company’s marketing strategy and input recommendations, enhancing effectiveness and consistency in marketing efforts.
• Automated Report Generation – GenAI produces financial, sales, and analytical reports based on data, transforming raw information into clear and comprehensible summaries, saving time and reducing the risk of errors.

2. GenAI in Your Organization

2.1 Challenges

Implementing GenAI is a process that requires careful planning—otherwise, it carries a high risk of failure. A well-thought-out strategy for deploying and using GenAI within a company must consider several key challenges, such as:

• Identifying Use Cases – Understanding where GenAI can be effectively utilized is crucial. Poorly chosen use cases can lead to inefficient deployment.
• Limited Availability of Experts – Implementing and managing GenAI requires specialized knowledge, which is scarce in the job market. However, this gap can be bridged with intuitive tools.
• Data Security Risks – GenAI processes vast amounts of data, including sensitive information. Ensuring compliance with security and regulatory requirements is essential.
• Monitoring and Control – GenAI solutions require constant oversight to ensure they function correctly. Inaccurate results may lead to incorrect decisions and financial losses.
• Scalability – As companies grow, GenAI solutions must be scalable, easily integrated with existing systems, and manageable at different levels of the organization.
• Cost Optimization – Operating large-scale GenAI models can be expensive. Organizations must carefully analyze how to manage AI-related infrastructure and costs effectively.

Selecting the right deployment strategy helps mitigate these challenges and facilitates smoother adoption.

2.2 Deployment Strategies

When introducing new technology into an organization, there are two traditional approaches: partnering with a service provider like a software house or building an in-house team of experts. However, both options present challenges in GenAI implementation due to the unique combination of AI and business expertise required, coupled with a shortage of talent.

• Software House – While outsourcing to a software house provides access to external technical knowledge, it is costly and risky. Changes to the project can be difficult and expensive, and lack of direct control over development teams may cause delays and quality issues. Moreover, software houses often lack deep business expertise, leading to solutions that do not fully align with company needs.
• In-House Team – Establishing an internal AI team is even more challenging and costly. Recruiting the right talent in a highly competitive market takes time and resources. Additionally, maintaining and continuously developing an AI team incurs significant operational costs, making the return on investment uncertain.
• Enterprise-Grade Platform – A viable alternative is using ready-made AI solutions, such as Extentum AI, which enables GenAI implementation and monitoring without hiring external service providers or building a new team. Business users can create LLM applications and AI assistants through a user-friendly interface, significantly reducing deployment time. The platform also provides scalability, performance analytics, and cost control. Additionally, it features an advanced data anonymization system that detects and replaces sensitive data with fictional equivalents during interactions, ensuring compliance with regulations and data security.

2.3 Preparing the Organization

Introducing GenAI into an organization is not just about tools—it’s about people using them. Fortunately, interacting with LLM models is highly intuitive, making team adaptation relatively straightforward. Preparation primarily involves establishing clear usage policies and supporting teams in identifying new applications and updating knowledge.

• GenAI Usage Policy – Ensures compliance with legal regulations, data protection, and company interests. It defines data processing rules, privacy protection, responsible AI use, and procedures for handling incidents. A well-crafted policy increases trust and reduces legal risks. Companies can either develop their own policies or use ready-made, regularly updated policies provided by platforms like Extentum AI.
• Use Case Identification – Although identifying use cases is an iterative process (as awareness of GenAI capabilities grows, more potential applications emerge), it is beneficial to pinpoint high-impact areas early on. Investing in these key areas first allows companies to maximize benefits before scaling solutions across the organization. Tools that simplify this process, such as Extentum AI, offer ready-to-use templates and performance analytics.
• Skill Development – While using GenAI is intuitive, even small improvements in prompt engineering can significantly enhance the quality of AI-generated responses, reducing costs and increasing efficiency. Well-crafted prompts lead to more precise and valuable answers, optimizing AI expenditures. To facilitate this, Extentum AI provides pre-designed prompt templates tailored to various business scenarios, ensuring best practices are followed.

3. Implementation

3.1 Traditional Development Cycle (Software House or Internal Team)

After deciding on the most suitable approach for implementing GenAI, organizations must plan the actual deployment process. This should ensure long-term benefits, return on investment, security, and regulatory compliance. The traditional implementation of generative AI solutions follows this structure:

• The business team identifies a potential use case
• Compliance analysis with requirements, standards, and regulations
• Workshops between the business team and the development team (internal or external) to convey the solution vision
• The development team builds the solution based on provided guidelines
• Prototype testing
• Implementing changes based on mismatches or evolving business needs
• Second round of workshops between both teams
• Further changes applied by the development team
• Deployment of the final solution
• Attempts to scale the solution, analyze efficiency, and manage access and data

The primary issue with this approach is the prolonged implementation process, particularly when changes are required. Delays lead to higher operational costs, increased risk of errors, misalignment with business needs, and difficulties in maintaining control and security across different levels of the organization.

3.2 Implementation with Extentum AI

To enhance flexibility, reduce costs, and ensure business value, the implementation process should be optimized. A solution that delivers long-term benefits for GenAI is Extentum AI, which automates the traditional deployment model, saving time, money, and unlocking human potential. How does deployment work on our platform?

• Business users independently build solutions tailored to their needs (or use predefined templates), allowing early-stage efficiency testing.
• Efficient solutions are rapidly scaled across selected levels of the organization.
• Sensitive data is automatically detected and anonymized before queries are sent to external LLM models, ensuring user privacy and minimizing data leakage risks.
• Immediate and cost-free modifications are possible as business needs evolve. The platform provides complete control over GenAI processes, offering cost and efficiency measurement tools across processes, roles, and teams, as well as access management and change tracking.

This approach significantly accelerates both the solution creation process and the implementation of changes. As a result, organizations can expect benefits such as better alignment with business requirements, lower operational costs, reduced risk of errors, and easier control and security management across all organizational levels

4. Conclusion

Is investing in GenAI worth it?

This question should be considered from two perspectives. The first is the market perspective. According to Gartner, over 80% of companies will implement GenAI by 2026. Given the significant productivity gains this technology offers (up to 40% improvement), it is clear that organizations adopting it will gain a substantial competitive advantage over those hesitant to act. With the market rapidly evolving, GenAI implementation is becoming a necessity rather than an option.

From the organizational perspective, companies can achieve tangible benefits in a short time—from reducing operational costs to freeing up human resources. However, successful and rapid deployment requires a well-thought-out approach and comes with inherent risks. The key to success lies in choosing the right implementation strategy tailored to the organization’s needs. A strategic decision minimizes the risk of failure, maximizes returns, and ensures rapid achievement of expected results.

GenAI implementation is no longer optional—it is essential. The challenge is selecting a method that enables organizations to do it efficiently and effectively.