In the fast-paced world of technology, generative AI has emerged as a transformative force for enterprises. As highlighted in Andreessen Horowitz’s recent findings, companies are not only increasing their AI budgets but also shifting towards open-source models to enhance control and customization. This blog explores the evolving landscape of Enterprise AI, focusing on key trends, practical applications, and the unique human-first approach of Origo, a leading AI consultancy that prioritizes meaningful, iterative solutions over mere technology implementation.
2023 may be remembered as the year Generative AI captured the public’s imagination, but behind the scenes, a quiet revolution is brewing. Enterprises are gearing up for a massive shift, one where Generative AI is no longer a novelty but a core driver of business value. At Origo, a leading AI consultancy partner, we’re seeing firsthand how organizations are embracing this technology – and the data backs it up.
A New Era of Investment and Strategy
Generative AI has captured the attention of enterprises worldwide, leading to a substantial increase in budgets dedicated to AI initiatives. According to Andreessen Horowitz, companies are moving from relying on one-time innovation funds to incorporating AI expenses into recurring software budgets. This shift reflects a growing confidence in the technology’s potential to deliver long-term value. As enterprises allocate more resources, they are increasingly focused on measuring the return on investment (ROI) of their AI projects. While productivity gains and customer satisfaction are common metrics, companies are exploring more concrete measures such as revenue growth, cost savings, and efficiency improvements.
How Enterprises are leveraging the power of Generative AI
The surge in enterprise investment in generative AI is remarkable. In 2023, the average enterprise spend on foundation model APIs, self-hosting, and fine-tuning models was around $7 million. Many businesses planned to increase their spending by 2x to 5x in 2024 because of successful POC with AI. This increase reflects a growing commitment to integrating AI into core business operations
Andreessen Horowitz’s recent report on “16 Changes to the Way Enterprises Are Building and Buying Generative AI” provides compelling insights into this transformation. It paints a clear picture of enterprises moving beyond experimentation and into strategic, large-scale adoption:
Exponential Budget Growth
Enterprise spending on Generative AI is projected to skyrocket to an average of $18 million per organization in 2024. This represents a staggering 2.5x increase from 2023, signaling a firm commitment to integrating this technology deeply into their operations.
Shifting Budget Priorities
The days of relegating Generative AI to experimental “innovation” budgets are dwindling. Businesses are now reallocating these funds into more permanent software budget lines. Less than a quarter of enterprises plan to fund Generative AI from innovation budgets this year. This shift reflects a growing confidence in the technology’s ability to deliver lasting, tangible returns.
Embracing Open Source
The enterprise’s desire for control and customization is fueling a surge in open-source adoption. Nearly 60% of AI leaders express a keen interest in increasing their use of open-source models. This trend is further validated by the finding that 46% of surveyed enterprises prefer or strongly prefer open-source solutions moving forward. Open source offers greater flexibility to fine-tune models for specific needs. Also, it provides reassurance regarding data security – a top priority for businesses dealing with sensitive information.
There’s a notable shift in how these investments are categorized. Previously, AI spending often came from innovation budgets—temporary and experimental in nature. However, in 2024, many enterprises are reallocating these funds into more permanent software budget lines. Less than a quarter of the surveyed companies now use innovation budgets for AI, suggesting a maturation approach.
The Shift Towards Open-Source Models
A significant trend observed is the increasing preference for open-source AI models. In 2023, the market was dominated by closed-source models, with OpenAI leading the pack. However, by 2024, nearly 46% of enterprise leaders expressed a preference for open-source models. The shift is driven by factors beyond cost, such as control over data security and the ability to customize models for specific use cases. Enterprises are increasingly valuing the ability to understand and modify the AI models they deploy.
Customization Over Building from Scratch
Instead of building AI models from scratch, enterprises are leveraging fine-tuning and other customization methods. This approach not only reduces costs but also accelerates deployment timelines. For instance, many enterprises are using retrieval-augmented generation (RAG) techniques to tailor open-source models to their specific needs. This shift reflects a pragmatic approach to AI adoption, focusing on practical applications and immediate returns rather than long-term R&D investments
Addressing the Challenges: Talent and Measurable ROI
While the enthusiasm for Generative AI is palpable, enterprises also recognize the accompanying challenges:
- The Talent Crunch: Finding and retaining skilled talent to build, implement, and scale Generative AI solutions remains a significant hurdle. This is reflected in the substantial portion of AI spending allocated to implementation alone in 2023.
- The ROI Equation: Demonstrating a clear return on investment remains a work in progress for many. While 56% of enterprises believe ROI is positive, quantifying it precisely is still a challenge. However, the focus is shifting from simple productivity gains to more impactful metrics like revenue generation.
Companies are choosing AI models influenced by the enterprise’s existing cloud infrastructure. The report highlights that many companies prefer to use AI models offered by their cloud service providers (CSPs), such as Azure, Google Cloud and Amazon Web Services. This preference is due to the ease of integration and enhanced security features provided by these CSPs. Over 72% of enterprises access their AI models via API, with more than half utilizing models hosted by their CSPs. This integration not only simplifies the deployment process but also aligns with existing security and compliance frameworks
Practical Applications and Internal Use Cases
Generative AI is being applied in various internal and customer-facing use cases. Common applications include customer support chatbots, internal knowledge management systems, and automated document summarization. Enterprises are particularly cautious about deploying AI in sensitive areas like healthcare and finance, where issues such as hallucination and data security are paramount. This cautious approach ensures that AI implementations do not compromise sensitive customer data or result in public relations issues
Deploying AI at scale presents several challenges, notably the shortage of specialized technical talent required for implementing and maintaining AI systems. Additionally, enterprises face challenges related to data security, particularly when using open-source models. Ensuring that proprietary data is not exposed requires stringent security measures and careful selection of model providers. Moreover, balancing innovation with risk management is critical as enterprises navigate the complex landscape of AI adoption.
Origo: Your Partner in Navigating the Generative AI Landscape
At Origo, we understand the transformative potential of Generative AI, as well as the complexities of implementing it effectively within an enterprise context. Our human-first approach means we don’t just implement technology; we work collaboratively with your team to understand your unique needs, challenges, and goals.
Here’s how we help you navigate the Generative AI shift:
- Strategic Planning & Implementation: We help you develop a comprehensive Generative AI strategy that aligns with your business objectives, identifying high-impact use cases and building custom solutions tailored to your specific needs.
- Talent Acquisition & Development: We address the talent gap by providing access to our network of experienced AI professionals and offering tailored training programs to upskill your existing workforce.
- Open Source Expertise: We have deep expertise in leveraging the power of open source models, ensuring you maintain control over your data while harnessing the flexibility and cost-effectiveness of these solutions.
- Measurable Results: We help you define and track relevant KPIs, ensuring your Generative AI initiatives deliver tangible business value and a demonstrable return on investment.
The Future is Collaborative
The enterprise adoption of generative AI is accelerating, with significant investments and a shift towards open-source models. As businesses explore these new technologies, they must carefully consider the implications for data security, customization, and ROI measurement. Partnering with experienced consultancies like Origo can provide the guidance needed to navigate these challenges effectively. By focusing on human-first, iterative solutions, Origo helps companies harness the power of AI in ways that are both innovative and practical.
The enterprise shift to Generative AI presents a unique opportunity for businesses to gain a competitive edge. At Origo, we believe the most successful implementations will be the result of close collaboration between enterprises and experienced AI partners. Contact us today to discuss how we can help you harness the power of Generative AI to transform your business.
For more information, contact us at info@origo.ec.