Why take a platform-based approach to Gen AI?

In recent years, with the rise of Generative AI (Gen AI) and Large Language Models (LLMs), almost every customer we have spoken to has jumped into experimentation, encouraging individual teams to develop use cases. However, this fragmented, use-case-by-use-case approach has revealed several significant challenges that limit scalability, efficiency, and long-term success. While it may seem flexible initially, such a piecemeal approach to Gen AI implementation is unsustainable.

This leads to a range of organizational issues, such as:

Teams often create their own environments with different technology stacks and tools, leading to fragmented efforts that are difficult to scale or govern. A recent survey by Deloitte found that 60% of executives expressed concerns about security risks¹associated with decentralized AI projects; centralized governance is very much needed.
The lifecycle of AI projects varies widely, making it hard to ensure consistent production readiness and maintenance. Companies face an estimated $1 trillion in technical debt² due to inefficient practices in AI development, including redundant processes and siloed operations.
Teams often deploy solutions that bypass standard IT protocols, leading to security and compliance risks. This is particularly concerning given that 75% of companies are prioritizing responsible AI practices³, which are harder to implement without a unified approach.
Teams working in isolation often reinvent the wheel on common processes like data parsing, model training, and deployment, leading to redundant efforts and technical debt.

‍

How would a platform-based approach solve these challenges?

Organizations that implement a centralized platform for AI management could experience minimum +30-50% increase in operational efficiency⁴, as they can optimize processes and reduce redundancy in AI initiatives.
‍

A platform approach allows for consistent security policies, automated compliance checks, and robust audit trails across all AI projects and use case realizations. Guardrails can be centrally developed and leveraged to ensure consistency and control.
‍
A consistent framework for managing AI projects from inception to production includes standardized data handling, lifecycle management, and common approval processes, ensuring that projects are evaluated based on business impact rather than individual teams' priorities.
‍
By centralizing AI infrastructure and processes, companies can avoid the pitfalls of scattered, resource-hungry experiments. A shared platform allows for better resource allocation with scalable solutions that can handle varying levels of demand without the bottlenecks of individual silos.
‍
A common platform enables the creation and sharing of reusable components, such as data parsers, model templates, and monitoring tools. This reduces redundant work and speeds up time-to-market for new AI solutions.
‍
Rather than scattered data sources leading to inconsistent results, a platform ensures that AI projects operate from a central, trusted data repository. This reduces data inconsistency, increases trustworthiness, and simplifies data governance.
‍
Responsible AI considerations—such as fairness, transparency, and accountability—are embedded into every AI project.
‍
Without a centralized approach, each team is forced to reinvent the wheel for compliance, often leading to gaps and potential liabilities. A platform-based approach allows for centralized logging, monitoring, and error tracking. This is crucial when deploying Gen AI applications into production, ensuring that there is a structured approach to monitor and control AI initiatives.
‍

Ad-hoc AI development is no longer sustainable. By centralizing security, governance, data management, and reusable components, companies can build a more efficient, scalable, and secure AI ecosystem. Oraczen’s zen platform enables this by providing centralized AI governance, security, and operations. With built-in observability, audits, and data management, zen helps businesses avoid fragmented efforts and ensures consistent, secure Gen AI adoption at scale.

‍

Sources:

Deloitte. (2023). AI governance: A framework for responsible AI deployment. Retrieved from https://www2.deloitte.com/us/en/insights/industry/technology/ai-governance.html

Virtasant. (2024). From layoffs to profits: AI operational efficiency's impact. Retrieved from https://www.virtasant.com/ai-operational-efficiency

Forbes. (2023). Smart automation: AI's impact on operational efficiency. Retrieved from https://www.forbes.com/sites/forbestechcouncil/2023/01/16/smart-automation-ais-impact-on-operational-efficiency/

AIMultiple. (2024). Top 10 sustainability AI applications - Research AIMultiple. Retrieved from https://research.aimultiple.com/sustainability-ai/

Grand View Research. (2023). Generative AI market size, share & trends analysis report by application. Retrieved from https://www.grandviewresearch.com/industry-analysis/generative-ai-market