At CrushBank, our commitment to delivering the best AI-driven solutions for Managed Service Providers (MSPs) and IT organizations has been a cornerstone of our platform since day one. Long before generative AI solutions like OpenAI’s ChatGPT or Microsoft’s CoPilot entered the scene, we chose IBM’s original AI system, called Watson at the time, as the foundation for our platform. It was clear that it offered the robust AI capabilities, governance, and scalability we needed to provide top-tier service to our clients and that has only grown with its transition to becoming watsonx in 2023.
Fast forward to today, with new generative AI models flooding the market, our decision to stick with watsonx has only been reinforced. While platforms like ChatGPT and CoPilot offer impressive, general-purpose AI, they fall short in areas that are critical to our clients—such as data governance, cost efficiency, transparency, and the ability to build highly specialized solutions. Watsonx continues to provide meaningful advantages to our clients by offering tailored, enterprise-grade AI that is secure, reliable, and purpose-built for MSP environments.
In this post, we’ll break down why CrushBank remains committed to IBM watsonx, and how its unique capabilities make it the best choice for helping our clients drive innovation and solve complex IT challenges.
1. Fit-for-Purpose Models: Avoiding the Complexity of General AI
Many companies are drawn to large, general-purpose models like GPT-4, assuming bigger means better. However, this often leads to “sticker shock” as these models require significant computational resources, storage, and energy consumption. The cost of scaling such models from pilot to production can be staggering.
IBM’s solution? Smaller, fit-for-purpose models, like Granite, which are tailored for specific tasks. These models not only outperform larger ones in specific use cases but do so at a fraction of the cost. For instance, summarizing a 30-minute call to a one-page summary with OpenAI’s GPT-4 could be up to 18.3 times more expensive than using watsonx.ai. For businesses looking to manage costs while maintaining high performance, watsonx offers a more efficient and cost-effective approach.
2. Transparency: Protecting Against Legal Risks
One of the primary concerns when using generative AI models is the potential for intellectual property (IP) and copyright infringement. Many organizations adopt large language models (LLMs) without fully understanding the data those models were trained on. This has led to a spate of lawsuits against companies like GitHub, Microsoft, and OpenAI for issues related to copyright and IP infringement.
IBM, however, stands apart. Watsonx offers unparalleled governance and transparency, removing copyrighted material, hate speech, and sensitive information from its training data. IBM’s clients are protected through robust indemnification, not just for copyright issues but for IP as well. This level of protection, along with IBM’s transparent governance process, ensures businesses can confidently deploy AI without fear of legal complications.
3. Comprehensive AI Governance: The Full Spectrum
AI governance is more than just managing the models themselves; it’s about integrating governance, risk, and compliance (GRC) into every facet of a business. While many vendors claim to offer AI governance, IBM is uniquely positioned to deliver. IBM governs not just its own models but any AI system, regardless of where it was developed or deployed. This platform-agnostic governance frees clients from vendor lock-in, ensuring they can manage their AI systems across multiple environments with ease.
IBM’s AI governance starts with data governance, an area where it has long been a leader. This experience allows IBM to offer end-to-end governance, from data to AI and enterprise integration, ensuring a holistic approach to managing AI risks.
4. Flexibility to Use Different LLMs for Different Purposes
One of the standout features of IBM watsonx is its ability to use multiple large language models (LLMs) for different purposes, giving CrushBank the flexibility to tailor AI performance to specific client needs. While some platforms rely solely on a single general-purpose model, watsonx offers a more nuanced approach, allowing us to deploy different models optimized for various tasks.
For example, when dealing with ticket classification or summarization, a smaller, task-specific LLM can offer better performance at a lower cost than a large, generalized model. Alternatively, when handling more complex IT support scenarios requiring deeper understanding and nuanced reasoning, a more powerful model can be applied. This flexibility allows CrushBank to fine-tune its AI solutions, delivering faster, more accurate results while also optimizing resource usage and cost-efficiency.
The ability to seamlessly switch between LLMs ensures that we can continue to meet the evolving needs of MSPs, providing them with AI that is not only effective but also highly efficient. This adaptability sets watsonx apart from other platforms and is one of the key reasons CrushBank remains committed to IBM’s AI technology.
Conclusion: A Meaning Difference for Our Clients
In a crowded AI marketplace, IBM’s watsonx continues to stand out with its strong focus on governance, cost-effectiveness, and adaptability. By offering transparent governance that mitigates legal risks and providing fit-for-purpose models tailored to specific tasks, watsonx enables organizations like CrushBank to confidently deliver AI-driven solutions. As AI governance and deployment strategies evolve, CrushBank is proud to build on the robust foundation of watsonx, ensuring our clients benefit from cutting-edge technology that is secure, scalable, and optimized for their unique needs.