Build vs Buy: Navigating Enterprise Generative AI Solutions
Panagiota
Marketing Specialist
September 24, 2024
in
🤖 AI automation
As enterprise generative AI continues to transform the business landscape, you face a critical decision: Should you build your own solutions or partner with established providers? This choice significantly impacts your organization's AI strategy, time-to-market, and overall success in leveraging cutting-edge technology. The rapidly evolving nature of AI development and the complexities involved in creating robust, scalable systems make this decision even more crucial for your enterprise's future.
To make an informed choice, you must consider various factors such as integration challenges, vertical-specific requirements, and ongoing maintenance needs. Data privacy and security concerns also play a vital role, especially when dealing with sensitive enterprise information. Additionally, you must weigh the benefits of customization against the expertise and resources required for in-house AI development. By exploring these aspects, you'll gain valuable insights to help you navigate the enterprise AI landscape and determine the most effective approach for your organization's unique use cases and regulatory compliance requirements.
Why Enterprises Should Avoid Building GenAI Internally
The GPT illusion
You may think all you need is OpenAI’s ChatGPT, and you can effortlessly deploy a fully functional AI agent. While GPT-4 is a highly sophisticated engine, enterprises seek a complete, vertical-specific solution. By focusing solely on GPT, companies may find themselves a crucial component but missing the broader infrastructure required to operationalize it effectively. After investing time, money, and resources into integrating GPT, they realize they still lack critical components such as workflows, customization, and security measures.
By leveraging models like GPT-4 or Claude, enterprises can quickly develop "impressive" internal demos that might achieve about 60% of the desired functionality. However, pushing beyond this threshold—to 70%, 80%, and beyond—becomes increasingly challenging due to the lack of transparency into the model’s inner workings. What starts as a promising demo often stalls before it can evolve into a fully scalable, enterprise-grade solution. Additionally, building the necessary auxiliary components from scratch requires significant effort and expertise.
How do you improve it if the Agent makes a mistake?
How do you keep feeding it new data?
How do you ensure the Agent follows processes in high-risk scenarios such as canceling a transaction?
How do you connect your agent to your messaging channels?
How do you extract analytics
How do you scale to many requests without breaking the bank?
Read more: We wouldn't use OpenAI's GPT-4 as an enterprise
Complexity of Implementation
Implementing enterprise generative AI solutions internally presents significant challenges. The process requires substantial investments in expertise, infrastructure, and ongoing maintenance. Many organizations struggle to obtain high-quality data that represents their specific product domain, which is crucial for accurate and relevant AI model outcomes. Without this foundational dataset, the results may lack precision and applicability to your business needs.
Customizing off-the-shelf GenAI models to suit specific business requirements requires expertise in data science and machine learning. This specialized skill set is often difficult and costly to acquire. As a result, companies frequently underestimate the complexity involved, leading to unrealistic timelines and the release of unfit-for-purpose solutions.
Ongoing Maintenance Challenges
Maintaining and improving GenAI solutions over time requires a robust framework for monitoring accuracy metrics. This involves rigorously testing models across different conditions to statistically verify improvements, eliminating reliance on guesswork. The rapid evolution of AI technologies necessitates continuous effort and resources to stay current, which can be overwhelming for in-house teams.
The maintenance burden for GenAI solutions is particularly heavy due to the speed of market evolution. Unlike traditional software systems that may require updates every few years, the GenAI landscape moves at a pace where new models and technologies have lifecycles measured in weeks. This constant flux demands ongoing attention and expertise that many enterprises struggle to maintain internally.
Data Privacy Risks
Generative AI models rely heavily on vast datasets, which can include sensitive information. You might assume that by building a generative AI solution internally, you avoid data privacy issues. However, handling such extensive and potentially sensitive data raises serious privacy and security concerns. The risk of inadvertently exposing personal identifiable information (PII) to third-party systems is a major issue.
Ensuring compliance with data protection regulations like GDPR and SOC2 adds another layer of complexity. These regulations mandate stringent controls over how personal data is collected, processed, and stored, emphasizing the importance of obtaining explicit consent and ensuring data minimization. Building internal systems that adhere to these evolving regulatory requirements demands significant ongoing effort and expertise.
The Benefits of Partnering with GenAI Solution Providers
Specialized Expertise
Partnering with enterprise generative AI solution providers gives you access to a wealth of specialized knowledge. These providers have teams with diverse skill sets spanning various AI subfields, from natural language processing to computer vision. This breadth of expertise allows you to tap into knowledge that would be challenging to recreate in-house. For instance, an AI outsourcing company may have experts in reinforcement learning, deep learning, and machine learning operations, providing comprehensive solutions that a single in-house team might struggle to match.
Faster Deployment
Speed is crucial in today's competitive landscape. By collaborating with GenAI solution providers, you can significantly accelerate your time-to-market, empowering you to quickly deploy AI-driven innovations and maintain a competitive edge. According to Forrester Research, companies leveraging outsourced AI solutions reached their target markets 20% faster than those relying solely on internal teams. This acceleration allows you to quickly deploy AI-driven innovations and maintain a competitive edge. Outsourcing can slash project timelines by up to 50%, according to Deloitte. This rapid deployment is possible because GenAI solution providers have established development processes and readily available expertise.
Continuous Innovation
Partnering with GenAI solution providers ensures you stay at the forefront of AI advancements. These providers continuously invest in research and development, keeping their teams updated with the latest technological breakthroughs. This ongoing innovation is crucial in the rapidly evolving field of AI, where new algorithms, frameworks, and tools emerge constantly. By leveraging their expertise, you can ensure your AI solution uses the most relevant, up-to-date technologies available. Moreover, many GenAI solution providers have partnerships with leading technology companies and academic institutions, giving you access to cutting-edge AI developments and best practices.
Evaluating GenAI Vendors for Enterprise Needs
Security and Compliance Standards
When selecting enterprise generative AI solutions, security and compliance are paramount. You need to choose vendors that prioritize data protection and adhere to industry regulations. Look for providers that offer robust security measures, including encryption methods, access controls, and audit trails.
To mitigate risks, consider vendors with on-premise installation, ensuring that your data remains secure in your systems.
Read: Moveo's proprietary LLMs vs OpenAI's GPT-4 for customer service
Integration Capabilities
Seamless integration is crucial for maximizing the capabilities of generative AI solutions and enabling faster time to production. Choose vendors that offer out-of-the-box integrations with the tools you use, like Zendesk, Intercom, HubSpot, and Salesforce.
Customization Options
To address the unique needs of your enterprise, look for vendors that offer customization options. Model customization techniques such as supervised fine-tuning, parameter-efficient fine-tuning, and low-rank adaptation (LoRA) can help tailor the AI model to your specific domain and use cases. This customization enhances the model's ability to understand industry jargon, technical terms, and context-specific nuances, leading to more accurate and relevant outputs.
Evaluate the vendor's capability to provide fine-tuning options that allow you to adapt pre-trained models to suit your needs, especially if multilingual support is required for your product. Additionally, consider vendors that offer Retrieval Augmented Generation (RAG) techniques, which combine the strengths of retrieval-based and generative AI approaches. This method enables the AI to generate responses based on indexed knowledge sources while maintaining accuracy and trustworthiness.
Conclusion
The decision to build or buy enterprise generative AI solutions has a significant impact on an organization's success in leveraging cutting-edge technology. While building in-house solutions might seem appealing, it comes with substantial challenges in implementation, ongoing maintenance, and data privacy concerns. Partnering with established GenAI providers like Moveo.AI offers benefits like specialized expertise, faster deployment, and continuous innovation, enabling companies to stay ahead in the rapidly evolving AI landscape.
For enterprises looking to harness the power of generative AI, carefully evaluating vendors based on security standards, integration capabilities, and customization options is crucial. By choosing the right partner, companies can address their unique needs while mitigating risks and ensuring compliance.
To explore how tailored GenAI solutions can drive your business forward, schedule a demo to check out Moveo's GenAI agents.