GenAI Roadmap : A Guide for Enterprises on How to Implement Gen AI Applications, Part 1

Ali Arsanjani
7 min readApr 13, 2024

This article provides a guide for enterprises on implementing generative AI (GenAI) applications. It covers a range of key considerations, such as data ownership and licensing, the importance of robust input validation and sanitization, model robustness, data privacy, and compliance. These elements are essential to ensure legal rights to use training data, protect AI systems from security threats, and adhere to data protection laws.

It explores the technical challenges of integrating GenAI with existing systems and highlights the necessity of overcoming potential biases to ensure ethical AI use. It also stresses the need for adequate technical expertise in developing, deploying, and maintaining AI applications to ensure their long-term viability and effectiveness.

One of the key points is to outline common GenAI applications across industries, including chatbots, content generation, code generation, data augmentation, and enhanced search functionalities. These use cases demonstrate the versatile capabilities of GenAI in creating new content, understanding context, extracting causal relationships, and making recommendations.

The article serves as a practical framework for companies at various stages of AI adoption, from experimental internal applications to more sophisticated, production-grade deployments.

High-level Roadmap

The roadmap steps below presuppose that you have a GenAI Strategy in place and have prioritized a set of use-cases predicated on ROI. For more details on GenAI Strategy and ROI, please refer to my other blog post on GenAI Strategy and ROI.

  1. Data Ownership and Licensing → Legal Compliance and Use Rights

Ensuring the right to use data for training models is essential. Understanding data ownership impacts whether the data can be used, modified, or resold, and compliance with data regulations is mandatory.

2. Input Validation and Sanitization → Security Against [Injection] Attacks

Robust validation and sanitization of inputs prevent malicious data manipulation, securing the AI against injection attacks that could compromise the system.

3. Model Robustness → Resistance to Adversarial Attacks

Enhancing model robustness through adversarial training and input filtering increases the AI’s ability to resist attacks designed to elicit incorrect or harmful outputs. Ability of the model to maintain quality in different data distributions.

4. Data Privacy and Compliance → User Trust and Legal Compliance

Adhering to data protection laws (e.g., GDPR, HIPAA) through measures like anonymization and encryption ensures the security of user data and compliance with regulations, building trust among users.

5. Technical Challenges (e.g., Integration with Existing Systems) → Efficient Operational Workflow

Overcoming technical hurdles in integrating AI with legacy systems and designing effective APIs ensures that AI implementations enhance rather than disrupt existing operational workflows.

6. Ethical Considerations → Bias Mitigation

Addressing potential biases in AI applications by diversifying training data and adhering to ethical guidelines prevents skewing of public perception and ensures fair AI outputs.

7. Technical Expertise → Sustainable AI Deployment and Maintenance

Developing and maintaining AI applications requires technical expertise, which is crucial for navigating the challenges associated with large-scale AI models and ensuring their long-term viability and effectiveness.

8. Problem-Solution Fit → Effective Application of AI Technology

Matching the AI’s capabilities with the right problems ensures that the technology is used effectively, maximizing its benefits and suitability for specific tasks.

9. Data Availability and Quality → Accurate and Effective AI Outputs

High-quality and relevant data is crucial for training AI to perform effectively, ensuring that the AI can accurately understand and respond to user queries.

Common Gen AI Adoption Strategies and Application Areas

Generative AI (GenAI) is an convergence of Natural Language Processing, Understanding and generation in AI that is part of every single company’s strategy that wants to not only stay relevant, but increase their business impact through greater productivity, efficiency and speed of execution.

Using GenAI we can create new or synthesized content, reason, understand query / prompt context, extract causal relatioships and make recommendations. It has a wide range of applications across many if not all industries.

Adoption Strategies

Many companies start with internal use cases, with less risk, lower priority use-cases and business impact areas, exercise gain more confidence and trust in the results through imposing guardrails and hallucination mitigation strategies then gradually move its application to externally facing applications.

Experiments and research gradually give way to more plausible and compelling applications and use-case implementations. They mature into more production grade applications. Many internal hurdles still exist as projects mature to move to production: legal, infosec, etc. Correspondingly an increase in sophistication if skills develop and expertise matures as more complex use-cases and proof points materialize.

Prevalent Use-cases and Application Areas

In this section we will explore some of the common Gen AI application areas that have gained traction at various Enterprises.

  1. Chatbots and virtual assistants: Gen AI can be used to create chatbots and virtual assistants that can provide customer support, answer questions, and complete tasks.
  2. Content generation: Gen AI can be used to generate marketing copy, product descriptions, and social media posts. It can also be used to create more creative content, such as poems, code, scripts, musical pieces, email, letters, etc.
  3. Code generation and assistance: Gen AI can be used to generate code based on natural language prompts. It can also be used to automate code refactoring, debugging, and test case generation.
  4. Data augmentation: Gen AI can be used to generate synthetic data for training other machine learning models. This can be helpful for augmenting datasets for rare events or underrepresented groups.
  5. Search and information retrieval: Gen AI can be used to improve search functionality by understanding the semantics of queries and providing more comprehensive and context-aware answers.

These are some of the most common Gen AI application areas/use-cases. As Gen AI technology continues to develop, you can expect to see even more innovative applications emerge specialized for each industry domain. In the table below I contrast the initial more limited scope of a proof-of-concept and the broader production grade use case scenarios.

Key Considerations in Developing Enterprise -grade LLM Applications

When developing AI applications with large language models (LLMs), security stands out as a pivotal concern. To safeguard these advanced systems, developers must prioritize a multi-layered security strategy.

As a critical consideration when implementing generative AI applications. we need to overcome some very specific challenges.

Here is a tldr;

Data Ownership and Licensing. Generative AI models are often trained on large datasets. It’s essential to ensure you have the rights to use the data you’re training the model on.

Input Validation and Sanitization. Generative AI models can be vulnerable to injection attacks if they are not properly validated. Input validation and sanitization can help prevent these attacks.

Model Robustness. Generative AI models can be fooled by adversarial attacks. Techniques like adversarial training and input filtering can help improve model robustness.

Data Privacy and Compliance. Generative AI applications may collect and process sensitive data. It’s important to comply with all relevant data privacy regulations.

In more detail.

Input Validation and Sanitization

One of the primary security measures is to robustly validate and sanitize user inputs. This process helps prevent common vulnerabilities such as injection attacks, where an attacker can input malicious data to manipulate the system. It’s imperative to scrutinize every input, ensuring that it cannot interfere with the backend processes.

Model Robustness and Domain Fit

The robustness of an LLM is its ability to functionally provide valid practice it generative outputs for out of distribution units that it was not trained in in a robust, high quality manner.

Another key aspect of model robustness is its ability to withstand and counter adversarial attacks.

These are techniques by which attackers provide input designed to confuse or mislead the LLM into making incorrect decisions or revealing sensitive information. Strengthening an LLM involves training it with a diverse set of scenarios, including potential adversarial examples, and employing techniques such as adversarial training and input filtering. This not only improves its resistance to manipulation but also enhances the model’s overall performance and reliability.

Data Privacy and Compliance

Compliance with data protection regulations is not optional; it’s mandatory. When LLMs process personal or sensitive data, they must comply with global data protection laws such as the GDPR, HIPAA, or others relevant to the user’s jurisdiction. This includes implementing measures to safeguard user privacy, such as encryption, access controls, and regular audits. By adhering to these principles, developers can build trust with users and ensure that their applications are both secure and compliant.

Developers who seek to build LLM based applications or integrate LLMs into their applications to infuse greater intelligence — for increased productivity and faster time to results — should focus not only on the functionality and performance of these models but also on their security posture.

Addressing input validation and sanitation, testing, tuning, augmenting the robustness of their selected models, and ensuring strict compliance with data privacy laws are essential steps in this process.

By doing so, you can deliver not just intelligent but also secure and more trustworthy AI solutions.

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Ali Arsanjani

Director Google, AI/ML & GenAI| EX: WW Tech Leader, Chief Principal AI/ML Solution Architect, AWS | IBM Distinguished Engineer and CTO Analytics & ML