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Techaisle survey shows The Rise of Generative-AI in SMBs and Midmarket Firms

According to recent survey data from Techaisle, the use of Generative-AI is rapidly increasing within SMBs and midmarket firms. The survey found that AI has become a priority for 53% of small businesses, up from 41% in April 2023. Among core-midmarket firms, 87% prioritize AI, up from 75% in April 2023. Similarly, 89% of upper-midmarket firms prioritize AI, compared to 87% in April 2023. Overall, 60% of SMBs and 84% of midmarket firms are either using or planning to use Generative-AI within the next six months.

The survey also found that between 40% and 45% of midmarket firms have developers and architects specializing in AI/ML, DevOps, hybrid cloud, and app modernization. Additionally, between 35% and 45% of these firms plan to increase their investments in Edge computing, Containers, Open-source technologies, app development, and analytics. Most notably, 72% of midmarket firms are increasing their in-house hiring for Generative-AI.

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Small and mid-sized businesses (SMBs) are initially focusing on using Generative-AI to automate repetitive and time-consuming tasks. These tasks include data entry, report generation, and email management. By automating these tasks, SMBs can free up time and resources for higher-value activities such as product development. 

In content marketing, Generative-AI is bringing about a transformative shift. The technology enables the generation of high-quality blog posts, social media content, and product descriptions in a fraction of the time and at a fraction of the cost.

At a broader level, Generative-AI is also being used to power chatbots that offer personalized assistance and customer support. This enhances customer retention and satisfaction. Additionally, the technology is revolutionizing email communication by enabling businesses to craft more effective and personalized messages using language models powered by Generative-AI.

A significant majority of small and mid-sized businesses (SMBs) expect their technology suppliers to provide AI-infused cloud applications, with 63% of SMBs having this expectation. Modernization is a priority for 95% of midmarket firms, and 60% are investing in modernization. However, nearly half of these firms face challenges in determining the right “AI-first” approach and identifying current data and application dependencies for training their own large language models (LLMs).

In specific verticals, Gen-AI has the potential to drive innovation. For example, in the healthcare sector, AI-powered algorithms can assist with medical image generation and analysis, leading to more accurate diagnoses and treatment planning. In the pharmaceutical industry, AI-driven drug discovery and development processes are accelerating research and bringing new treatments to market more quickly.

According to the survey by Techaisle, the SMB segment’s Information/Media, Financial Services, Retail/Wholesale, and Manufacturing verticals (in that order) are leading the way in Gen-AI adoption. On the other hand, Resources and Personal services are lagging. Among upper midmarket firms, Financial services, Professional services, Information/Media, Manufacturing, Healthcare, and Hospitality are the top verticals.

Overall, Gen-AI offers significant potential for SMBs and midmarket firms to enhance their operations and drive innovation. By leveraging this technology effectively, these businesses can improve efficiency, productivity, and customer satisfaction.

Small and midmarket organizations have consistently reported similar anticipated benefits. The top four benefits, ranked in the same order by both groups, include improved customer satisfaction and retention, increased business agility, enhanced profitability, and higher revenue.

Challenges Faced by SMBs while Implementing Gen-AI Solutions

One of the primary challenges SMBs face when it comes to Gen-AI is acquiring large and diverse datasets for training their models. Compared to larger companies with more extensive resources, SMBs may need help to gather or access a wide range of data, resulting in training data that is limited in scope and may not adequately represent different demographics. This can lead to content that exhibits biases derived from the limited dataset, hindering the ability of the models to create fair and inclusive material. Therefore, SMBs must design and train their Gen-AI models using broad and neutral datasets.

Another challenge for SMBs and midmarket firms is finding and retaining individuals with expertise in AI. These businesses may require more in-house proficiency to understand and harness AI solutions' potential fully. The high demand for AI experts in the job market and competition from larger enterprises can make it difficult for SMBs to build a competent team capable of effectively deploying and managing Gen-AI technologies.

Gen-AI also presents cybersecurity-related challenges. While it offers many benefits as a tool, businesses with limited resources and expertise in cybersecurity may be more vulnerable to potential Gen-AI-powered attacks that can breach their defenses. The ability of malicious code to morph and evade detection using Gen-AI technologies can heighten this threat, making it even more challenging for businesses to protect themselves using traditional cybersecurity measures.

In summary, businesses using Gen-AI must consider the technology's limitations carefully and clearly understand their confidence level in the generated outputs. For example, while a self-driving car that is accurate 99% of the time may seem impressive, the 1% error rate could have catastrophic consequences. On the other hand, a lower accuracy rate may be acceptable when using Gen-AI to assist with writing an internal business report. However, using the same outputs in a press release could have severe repercussions if it contains misleading or inaccurate information.

Navigating the Future: Evolving Gen-AI Landscape

Artificial Intelligence (AI) has become an integral part of our lives, with its transformative potential and influence on digital experiences being referred to as the “fourth industrial revolution.” AI is used in various applications, from facial recognition and digital mapping to transforming business operations and customer interactions. While its advancements have often been subtle, with significant moments such as DeepMind’s AlphaGo defeating a Go world champion in 2016 being celebrated but soon fading from public awareness, the latest Generative AI (Gen-AI) applications such as ChatGPT, GitHub Copilot, and Stable Diffusion have captured global attention due to their broad usability and ability to enable interactive communication and content creation.

The introduction of OpenAI’s Generative Pre-Trained Transformer 4 (GPT-4) and ChatGPT has increased interest in Gen-AI over the past year. GPT is one of many Gen-AI platforms that use sophisticated algorithms to generate text and other media. As one of the most widely recognized and adopted platforms, it serves as an important early indicator of the impact of AI.

Gen-AI applications can automate routine tasks and repetitive operations, benefiting businesses, especially SMBs, by enhancing efficiency and productivity. However, despite its potential benefits, Gen-AI also presents challenges.

Competition in the Gen-AI field is intensifying, with technology vendors vying for market share. Some vendors are more deeply involved in the Gen-AI ecosystem than others. In July 2019, Microsoft made a significant initial investment of $1 billion in OpenAI, securing an exclusive license to the underlying technology behind GPT-3. In February 2023, Microsoft launched its chatbot on its Bing search engine. In response, Alphabet, Google’s parent company, introduced Bard, a new version of ChatGPT within its search engine. Chinese tech giant Baidu also introduced its chatbot project, “Ernie.”

As the Gen-AI industry develops, more vendors will likely emerge and launch their products. These alternative offerings may provide more tailored and sophisticated solutions to meet the diverse needs of businesses. The resulting competition will probably drive innovation and lead to a proliferation of AI-powered offerings.

Advancements Made in Gen-AI by Hyperscalers

As businesses recognize the significant potential of Gen-AI, many are turning to cloud-based strategies to leverage its capabilities thoroughly. Hyperscalers, with their vast computational resources and capabilities, are well-positioned to take advantage of Gen-AI. Leading hyperscalers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) have acknowledged the potential of Gen-AI and are making significant investments in developing cutting-edge products and services to meet the growing demand for this technology.

Microsoft, for example, has differentiated itself from its competitors through its exclusive collaboration with OpenAI. On its cloud platform, Microsoft Azure offers a range of tools and services for Gen-AI development, including integrating GPT-3 into Azure Cognitive Services, including text and image generation APIs. Microsoft has also announced Azure AI Studio, a tool for developers to build conversational AI models similar to OpenAI’s ChatGPT and GPT-4 while retaining data ownership.

AWS is also developing its strategy for leveraging Gen-AI. The company has unveiled Amazon Bedrock, a portal that allows businesses to access foundation models from AI21 Labs, Anthropic, Stability AI, and Amazon’s in-house team via an API. Bedrock’s serverless experience enables users to quickly find the suitable model, initiate operations, customize foundation models with their data, and seamlessly integrate and deploy them using AWS’s toolkit and capabilities.

GCP offers Gen-AI Studio, a tool integrated within the Google Cloud Console that allows users to rapidly prototype and test Gen-AI models without building them from scratch. GCP also offers Vertex AI, a comprehensive platform for managing the entire machine learning lifecycle; Model Garden, which provides a range of pre-trained foundation models; and Duet AI, which integrates Gen-AI capabilities into Google Workspace applications.

These offerings demonstrate that cloud hyperscalers are investing significantly in developing innovative products and services to meet the growing demand for Gen-AI.

IBM introduced watsonx at IBM Think 2023, a platform that leverages the company’s Watson AI system and focuses on foundation models and generative AI. Watsonx.ai is a studio that enables companies to train, test, and deploy generative AI for business functions using foundation models. It includes a model library containing pre-trained foundation models offered by IBM and is built on open-source libraries. Watsonx.data is an enhanced and distributed version of the OpenDataHub project and serves as a dedicated data store for developing AI projects using an open Lakehouse architecture.

Role of Channel Partners in the Gen-AI Space

Channel partners have an essential role to play in the Gen-AI space. With the rise of Gen-AI, led by companies such as OpenAI, partners are beginning to explore how this technology can impact their products. While many vendors claim to offer unique Gen-AI solutions using large language models (LLMs), the reality is somewhat more complex. Some partners have compared the hype around Gen-AI to the trend of “cloud washing” in the past, where vendors would rebrand their products or services by simply appending the term “cloud” to them.

To be effective in the Gen-AI space, channel partners must go beyond vendor-provided education and develop a deep understanding of the various platforms available, from Google Cloud Vertex AI to Amazon Bedrock. They should also familiarize themselves with other players in the market and build expertise in the rapidly evolving research ecosystem of Gen-AI.
Innovative Solutions is an example of a channel partner actively offering Gen-AI consulting and services. At the AWS Summit New York, the company launched Tailwinds, an AI-infused consulting and app development program designed to guide organizations through a three-stage process of Gen-AI adoption: assessment, onramp, and implementation.

Revamping IT Infrastructure for Generative AI

It is clear that Gen-AI will play an increasingly important role in the operations of SMBs in the future. As such, SMB executives must take proactive steps to ensure their IT infrastructures and teams are prepared for future changes. The effectiveness of Gen-AI depends on access to high-quality training data and robust processing power, which presents challenges such as the need for computing resources with powerful GPUs, ample memory, and sufficient storage capacity.

Training large language models (LLMs) involves processing vast amounts of data and optimizing millions or even billions of parameters, making it computationally intensive and time-consuming. Gen-AI workloads, particularly those involving large-scale models like GPT-3, require significant processing power and can benefit from specialized hardware such as GPUs for efficient training. As a result, businesses must find ways to manage the increased demand for server resources, which may necessitate upgrading their server infrastructure to meet this demand. Using energy-efficient hardware and adopting new technologies, such as AI-optimized servers, can also play a crucial role in addressing these demands.

Dell Technologies has introduced new offerings to help customers build generative AI (GenAI) models on-premises. The new Dell Generative AI Solutions expand upon the company’s previous Project Helix announcement and include IT infrastructure, PCs, and professional services to simplify the adoption of full-stack GenAI with large language models (LLMs). These solutions help organizations of all sizes and industries securely transform and deliver better outcomes. The Dell Validated Design for Generative AI with NVIDIA is an inferencing blueprint optimized to speed the deployment of a modular, secure, and scalable platform for GenAI. This solution helps customers generate higher quality, faster time-to-value predictions and decisions with their data. Dell Professional Services offers a broad spectrum of new capabilities to help customers accelerate GenAI adoption, improve operational efficiency, and advance innovation. These services begin with creating a new GenAI strategy that identifies high-value use cases and a roadmap to achieve them. Dell also offers full-stack implementation services, based on the Dell Validated Design for GenAI with NVIDIA, and adoption services that apply the platform to specific use cases, such as customer operations or content creation.

In addition to processing power, Gen-AI models also require access to extensive datasets to improve their learning and performance. The more data available to these models, the more accurate their generated content will be. This means businesses need a robust data infrastructure for storage, processing, and management. This infrastructure should be scalable, secure, and easily accessible to AI models. Cloud-based data storage solutions can provide an ideal option, offering large-scale storage capabilities without costly on-premises hardware.

To prepare for the widespread adoption of AI, businesses must also develop a sound business and capital strategy to invest in the necessary infrastructure and tools and build a skilled workforce. This will enable them to effectively leverage the potential of Gen-AI to drive innovation and growth.

Final Techaisle Take

While Gen-AI has the potential to be highly beneficial, it has its challenges. Small and medium-sized businesses (SMBs) looking to integrate Gen-AI into their critical operations must carefully identify the areas where AI can effectively automate processes. It is important to note that there is no universal formula for utilizing Gen-AI; each business operating in a specific industry or vertical requires tailored solutions. SMBs, which often face capital and human resources constraints, stand to benefit significantly from Gen-AI. The technology can help alleviate these constraints, provided SMBs know how to leverage it effectively. As with any technology, a proper understanding and correct usage are crucial for successfully implementing Gen-AI solutions.

Generative AI is still in its early stages of adoption, but it is here to stay. Now is an opportune time for businesses of all sizes to experiment with this technology and get ahead of the curve. SMBs and midmarket firms that wait to adopt Gen-AI risk falling behind their competitors, who are already taking advantage of this powerful new tool.

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