Techaisle Blog

A recent update from CrowdStrike caused a widespread outage, termed the largest in history. The IT outage highlighted the vulnerability of SMBs to large-scale tech outages and their dependence on third-party IT services. It also underscored the need for better preparedness and potentially more diverse IT ecosystems to mitigate such risks in the future. Cybercrime is predicted to cost $14.57 trillion globally in 2024, and it would be the world's third-largest economy after the US and China. This underlines the importance of robust cybersecurity. The incident raises a concerning question: what happens when the guardians become the source of disruption? The CrowdStrike-related outage was not caused by a cyberattack, but rather by a defect in a software update, highlighting the importance of operational resilience beyond just cybersecurity measures. Analysts estimate the global cost of this outage to be as high as $24 billion as of now.

Uneven Recovery for SMBs

A Techaisle survey of 600 small businesses indicates that 34% expect customer reputation damage and 23% anticipate significant bottom-line impacts due to operational challenges. Logistics complications from inventory management to shipment tracking caused business friction. Communication issues with clients, suppliers, and staff led to delays and missed opportunities, while financial activities like processing payments and managing invoices suffered. Some businesses also faced customer service obstacles, leading to customer dissatisfaction and reputational harm.

The recent CrowdStrike outage has highlighted the essential need for dependable tech in today’s businesses and the severe impact that short downtimes can have, especially on SMBs. It's a stark reminder of the importance of solid IT frameworks and contingency planning. Small businesses suffer disproportionally during such outages due to leaner cyber defenses and limited personnel, which are significant hurdles when crises strike. The manual resolution required here spotlighted these weaknesses. Small IT teams, typically stretched thin with various duties, found it tough to manage the fix across several devices, leading to overtime work to fix crucial operations.

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.

Techaisle's study on SMB and Midmarket Security Adoption Trends projects that IT security spending by small and mid-sized businesses worldwide is expected to reach US$90 billion in 2024, showing a 9.4% increase from the previous year. In the US alone, the market expenditure is set to rise by 8.3%. Significant investments will concentrate on Endpoint Protection, Network Security, and Identity and Access Management, with MDR (Manage, Detect, Respond) services seeing the most rapid growth in the SMB and midmarket sectors.

A significant increase in funding for IT security is projected among high-growth SMBs. Notably, 21% of rapidly growing small and medium-sized enterprises (SMBs) and 26% of mid-sized companies are forecasting a minimum growth of 15% in their IT security budgets for 2024. Highly innovative organizations are committed to improving security measures as well, with 18% of SMBs and 21% of midmarket companies planning to boost their spending by 12% compared to the previous year.

Techaisle's data also reveals a concerning cybersecurity preparedness gap among small and medium-sized businesses, with 42% having no cyber incident response plan. Despite nearly half experiencing security breaches, often going unnoticed, only 40% are confident in their recovery capabilities, likely downplaying the risks. These cyberattacks come at a high cost, with SMBs' average annual losses reaching $1.4 million. Contributing to this issue is the fact that 46% of these businesses lack formal risk assessment methods, indicating a serious underestimation of online threats.

Addressing SMB and Midmarket security issues can be done through various lenses. Segmenting security adoption into four categories—Prevent & Protect, Detect & Respond, Adapt & Comply, Restore & Recover—provides an understanding of preferred solutions and areas poised for growth. This framework helps clarify how these companies prioritize and distribute funds for cybersecurity strategies.

Today's cybersecurity landscape is a complex maze, with a multitude of vendors contributing to a convoluted and intricate security stack. The evolution of security from traditional perimeter defenses around private data centers to a distributed network of branch offices, remote workers, and IoT devices has necessitated a radical shift in security strategies, with a focus on enforcement points across the network. At its core, security is a data challenge, where the sheer volume of data often hinders the identification of actionable insights, leading to an imbalanced signal-to-noise ratio and the prevalent issue of alert fatigue. Effective data connection across control points is crucial to transform low-level alerts into critical insights that demand immediate action.

Under the visionary leadership of Jeetu Patel, Executive Vice President and General Manager of Security and Collaboration, Cisco's security product portfolio has undergone a transformative evolution. This radical re-envisioning of security paradigms has significantly refined Cisco's security cloud solutions, streamlining the adoption process for an integrated security platform. In response to the complexities of distributed environments, Cisco introduced 'Hypershield,' a pioneering expansion of the hyper-distributed architecture concept tailored to meet the demands of hyper-distributed security. The strategic acquisition of Splunk has further fortified Cisco's capabilities, enabling it to manage the signal-to-noise ratio effectively. Leveraging Splunk's advanced data analytics, Cisco aims to mitigate alert fatigue by converting many low-level events into meaningful, actionable insights.

The Birth of the Cisco Security Cloud Platform

In June 2022, Cisco introduced the Cisco Security Cloud Platform at the RSA Conference, a visionary solution designed to streamline the complexity of managing disparate security tools. This platform offers a unified experience, ensuring secure connections for users and devices to applications and data, irrespective of location.

The platform's emphasis on openness provides a comprehensive suite for threat prevention, detection, response, and remediation at scale. At its core is a powerful firewall, enhanced with AI for superior analysis. Identity management is flawlessly integrated, allowing every Cisco security product to leverage AI-driven insights and user authentication.

Cisco addressed the challenge customers faced with the vast array of security products—approximately 30 products with over 1,000 variations—by significantly simplifying its portfolio. Customers now have a choice of three intuitive suites: User Protection, Cloud Protection, and Breach Protection. These suites are not merely bundled; they are fully integrated, facilitating seamless communication and improved functionality, making security management far more straightforward and efficient.

Tackling Hyper-Distributed Security with Cisco Hypershield

As an industry analyst, I am convinced that Cisco's recent strides in security innovation are nothing short of impressive. The 2023 launch of Cisco Multi-cloud Defense, Cisco XDR, Cisco Secure Access, and advanced firewall functionalities marked a year of significant progress. The introduction of Cisco AI Assistant was a testament to its commitment to continuous innovation. In 2024, Cisco took a giant leap by introducing Hypershield, a sophisticated, AI-enhanced, cloud-native security system set to redefine cybersecurity.