Techaisle Blog

The landscape of GenAI is rapidly evolving, and midmarket firms are striving to keep pace with this change. New data from Techaisle (SMB/Midmarket AI Adoption Trends Research) sheds light on a fascinating trend: adopting multiple large language models (LLMs), an average of 2.2, by core and upper midmarket firms. Data also shows that 36% of midmarket firms are piloting with an average of 3.5 LLMs, and another 24% will likely add another 2.2 LLMs within the year.

The survey reveals a preference for established players like OpenAI, with a projected penetration rate of 89% within the midmarket firms currently adopting GenAI. Google Gemini is close behind, with an expected adoption rate of 78%. However, the data also paints a picture of a dynamic market. Anthropic is experiencing explosive growth, with an anticipated adoption growth rate of 100% and 173% in the upper and core midmarket segments, respectively. A recent catalyst in midmarket interest for Anthropic is the availability of Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock.

This trend towards multi-model adoption signifies a crucial step – midmarket firms are no longer looking for a one-size-fits-all LLM solution. They are actively exploring the functionalities offered by various models to optimize their specific needs.

However, the data also raises questions about the long-term sustainability of this model proliferation due to higher costs, demand for engineering resources (double-bubble shocks), integration challenges, and security. Additionally, market saturation might become a challenge with several players offering overlapping capabilities. Only time will tell which models will endure and which will fall by the wayside.

Furthermore, the survey highlights a rising interest in custom-built LLMs. An increasing portion of midmarket firms (11% in core and 25% in upper) will likely explore this avenue. In a corresponding study of partners, Techaisle data shows that 52% of partners offering GenAI solutions anticipate building custom LLMs, and 64% are building SLMs for their clients, indicating a potential shift towards smaller specialized solutions.

Why Multi-Model Makes Sense for Midmarket Firms

The journey from experimentation to full-fledged adoption requires a strategic approach, and many midmarket firms are discovering the need to experiment with and utilize multiple GenAI models. There are several compelling reasons why midmarket firms believe that a multi-model strategy might be ideal:

Specificity and Optimization: Various LLMs specialize in different tasks. Midmarket firms believe they can benefit from a multi-model strategy, using the best-suited model for each purpose. This may enhance efficiency and precision across a broad spectrum of use cases. Since GenAI can reflect biases from its training data, a multi-model approach also serves as a safeguard. Combining models informed by diverse datasets and viewpoints ensures a more equitable and efficient result.

Future-Proofing: LLMs are rapidly advancing, offering a stream of new features. Without a visible roadmap from LLM providers, midmarket firms hope to benefit from using various models to stay current with these innovations and remain flexible in a dynamic market. As business requirements shift, a diversified model strategy enables modification of their GenAI tactics to align with evolving needs. This strategy permits businesses to expand specific models to meet increasing demands or retire outdated ones as necessary.

Despite the benefits, midmarket firms are also experiencing challenges

High Cost: LLMs have a high price tag, particularly for smaller midmarket companies. Creating and maintaining an environment that supports multiple models leads to a substantial rise in operational expenses. Therefore, a small percentage of midmarket firms are conducting a thorough cost-benefit analysis for every model and optimizing the distribution of resources to ensure financial viability over time. Managing and maintaining multiple LLMs is time-consuming, as different models have varying data formats, APIs, and workflows. Developing a standardized approach to LLM utilization across the organization has been challenging, and a lack of engineering resources has surfaced.

Specialized Skills: Deploying and leveraging multiple LLMs necessitates specialized skills and knowledge. To fully capitalize on the capabilities of a diverse GenAI system, it is essential to have a team skilled in choosing suitable models, customizing their training, and integrating them effectively. Midmarket firms are investing in training for their current employees or onboarding new specialists proficient in LLMs.

Integration Challenges: Adopting a multi-model system has benefits but can complicate the integration process. Midmarket firms are challenged to craft a comprehensive strategy to incorporate various models into their current workflow and data systems. The complexity of administering and merging numerous GenAI models necessitates a solid infrastructure and technical know-how to maintain consistent interaction and data exchange among the models.

Midmarket Firms Intend to Adopt DataOps to Develop GenAI Solutions Economically

While large enterprises have shown how effective DevOps can be for traditional app development and deployment, midmarket firms notice that conventional DevOps approaches may not fit as well for emerging AI-powered use cases or GenAI. Techaisle data shows that only half of the midmarket firms currently have the necessary talent in AI/ML, DevOps, hybrid cloud, and app modernization. Although DevOps is great for improving the software lifecycle, the distinct set of demands introduced by GenAI, primarily due to its dependence on LLMs, poses new hurdles.

A primary focus for midmarket firms is ensuring a steady user experience (UX) despite updates to the foundational model. Unlike conventional software with updates that may add new features or bug fixes, LLMs are built to learn and enhance their main functions over time. As a result, while the user interface may stay unchanged, the LLM that drives the application is regularly advancing. However, changing and or even swapping out these models can be expensive.

DataOps and AnalyticsOps have emerged as essential methodologies tailored to enhance the creation and deployment of data-centric applications, much like those powered by GenAI. DataOps emphasizes efficient data management throughout development, ensuring the data is clean, precise, and current to train LLMs effectively. Conversely, AnalyticsOps concentrates on the ongoing evaluation and optimization of the GenAI applications' real-world performance. Through persistent oversight surrounding user interaction, DataOps and AnalyticsOps empower midmarket firms to pinpoint potential enhancements within the LLM model without requiring extensive revisions, facilitating an incremental and economical methodology for GenAI enrichment. Ultimately, midmarket firms are considering adopting DataOps and AnalyticsOps with a strategic intent to adeptly handle the intricacies inherent in developing GenAI solutions. By prioritizing data integrity, continuous performance assessment, and progressive refinement, these firms hope to harness GenAI's capabilities cost-effectively.

Final Techaisle Take

The success of GenAI implementation probably hinges on a multi-model strategy. Firms that effectively choose, merge, and handle various models stand to fully exploit GenAI's capabilities, gaining a considerable edge over competitors. As GenAI progresses, strategies to tap into its capabilities must also advance. The key to future GenAI advancement is employing various models and orchestrating them to foster innovation and success.

The rapid pace of AI innovation, coupled with the complexity of implementation, creates challenges for many businesses. Concerns around data security, intellectual property, and the high costs of running and managing AI models further complicate their AI journey. This is where Dell steps in, leveraging its extensive expertise in AI and innovative solutions to help businesses navigate these challenges. The company focuses on developing data management solutions, launching powerful computing hardware, and building partnerships to ensure businesses are equipped for the demands and opportunities of AI.

As part of its commitment to democratizing AI, Dell unveiled the Dell AI Factory at the recent Dell Technologies World (DTW) conference in May 2024. This unique initiative stands out for providing customers access to one of the industry's most comprehensive AI portfolio, from device to data center to cloud. The AI Factory, a distinctive combination of Dell's infrastructure, expanding partner ecosystem, and professional services, offers a simple, secure, and scalable approach to AI delivery. Its objective is to integrate AI capabilities directly within data sources, transforming raw data into actionable intelligence and thereby enhancing business operations and decision-making processes. In addition, Dell announced new channel programs to foster collaboration and accelerate AI adoption, recognizing the vital role of channel partners in driving revenue. With Dell's AI Factory, businesses can confidently embark on their AI journey, knowing they have a trusted partner to guide them every step of the way.

Understanding the AI Factory

To adopt AI on a large scale, a robust infrastructure is crucial. Conventional IT setups designed for regular computing often struggle to meet the complex demands of AI workloads. This is where the concept of an AI Factory becomes significant. Picture it as a specialized center with powerful computing systems, advanced data processing tools, and a team of AI experts. The AI Factory is designed to streamline AI solutions' development, deployment, and scaling, making it easier and faster. By consolidating these elements, an AI Factory ensures that AI innovations can be swiftly created and applied, reducing delays and increasing efficiency, thereby simplifying the complex process of AI deployment for businesses. With Dell's AI Factory, businesses can feel relieved of the implementation challenges, knowing they have a trusted partner to guide them every step of the way.

The Dell AI Factory simplifies AI deployment by offering essential components like servers, storage, and networking in one place. This streamlined approach eliminates the need for businesses to find and combine these components separately – and ensures they work well together, saving significant time and resources. Customers also gain access to Dell's AI expertise and a reliable ecosystem of partners. This comprehensive solution empowers businesses to choose from individual products or create custom configurations to fit their AI needs. The Dell AI Factory also offers different consumption models, including purchases, subscriptions, and as-a-service options, providing businesses the flexibility to adopt AI at their own pace. With Dell's comprehensive AI portfolio, businesses can feel secure knowing they have all the tools they need for successful AI adoption.

The Dell AI Factory is not just a collection of products. It is a comprehensive solution designed to simplify AI integration for businesses of all sizes.  Whether a business, like SMBs, is starting small with PCs or deploying AI across a server network, the Dell AI Factory equips the customers with the tools and expertise to achieve real-world results.

This powerful combination of high-performance infrastructure, industry-leading services, and deep AI knowledge can empower businesses to embrace AI confidently.  The Dell AI Factory goes beyond just hardware, offering a complete package that simplifies the entire AI adoption process, making Dell a key player in accelerating real-world AI applications. 

Dell AI Factory Infrastructure

Training and deploying AI models require significant computational power and vast datasets. While convenient for many businesses, public cloud solutions can become expensive for these resource-intensive tasks and introduce security risks and the potential for IP infringement. Businesses increasingly seek on-premises solutions for greater control over data and resources and cost optimization. The Dell AI Factory addresses these challenges by providing a robust foundation built on Dell's core strengths in infrastructure solutions—servers, storage, data protection, and networking. This robust infrastructure delivers the necessary computational muscle and storage capacity for AI workloads.

Key findings from Techaisle’s SMB and Midmarket AI Adoption Trends Research of 2100 businesses paint a promising future where AI is set to revolutionize traditional channel business models. This technological advancement offers a multitude of benefits, reshaping business operations, transforming IT economics, and enhancing service delivery capabilities. AI's reach extends beyond technical managers, presenting a vast array of opportunities for channel partners to explore and cater to a broader buying group.

SMB and Midmarket AI spending plans indicate that high expectations for business impact will indeed map to significant increases in solution spending. Over 40% of upper midmarket firms, nearly 40% of core midmarket firms, and almost 30% of small businesses expect AI-related IT spending to increase by more than 25% in 2024. The mean increase across these segments ranges from 22% to 28%. This potential increase in solution spending should motivate channel partners to seize the AI opportunity and drive their businesses forward.

Generative Artificial Intelligence (GenAI) is on the brink of a massive expansion. IBM is seizing this opportunity and striving to cater to the escalating demand for this technology. The company has launched watsonx, an AI and data platform specifically designed for businesses, providing GenAI, data management, and AI model governance capabilities.

IBM’s strategic efforts go beyond mere product innovation. They include collaborative ventures aimed at stimulating growth across the industry. The company’s new partner program, IBM Partner Plus, is designed to establish a diverse network of resellers. This initiative is set to ignite growth and spur innovation in various sectors, highlighting IBM’s commitment to making AI accessible and enabling businesses around the globe.

IBM's Journey with watsonx and Responsible AI Governance

Partnerships and acquisitions frequently serve as turning points, transforming entire industries. This is exemplified by IBM’s 2019 acquisition of Red Hat, a prominent provider of open-source software solutions. This strategic move has fortified IBM’s standing in the open hybrid cloud solutions market while simultaneously offering both companies the chance to provide their clients with improved AI capabilities. IBM watsonx is an AI and data platform with a set of AI assistants designed to help organizations scale and accelerate the impact of AI with trusted data across the business. The platform offers flexibility, enabling organizations to start with one component or application and include additional ones as needs grow. It consists of a studio for foundation models, a data store, and a governance toolkit.