As organizations navigate the complexities of digital transformation, one question repeatedly arises:
“How do we shift from functional design to horizontal design when designing our Operating Model?”
Traditionally, functions like Finance, Procurement, Operations, and Marketing operate within their own silos, each developing its own operating model. However, to drive efficiency and enhance customer experience, organisations must transition to a horizontal, customer-centric design that integrates all functions seamlessly.
This transition is particularly critical as organizations incorporate AI into their end-to-end processes. AI’s potential can only be fully realized if it is embedded in a structure that fosters cross-functional collaboration, data-driven decision-making, and seamless process integration.
Even if different teams work independently, aligning on a shared set of design principles ensures consistency across the organization. This alignment fosters interoperability, clarity in decision-making, and a uniform approach to AI-driven transformation.
To break down silos, organizations need a high-level Operating Model Blueprint—a single-page visual representation of how various functions interact. This helps teams drill down into their specific designs while maintaining a unified, enterprise-wide perspective.
End-to-end processes like Source-to-Pay (S2P) or Order-to-Cash (O2C) serve as the foundation for horizontal design. They ensure visibility across functions, clarify handoffs, and eliminate inefficiencies in workflows spanning multiple departments. This approach forces an organization-wide mindset, promoting AI’s role in optimizing these processes.
Understanding the organization’s core business capabilities—both shared and function-specific—prevents redundant efforts and encourages resource optimization. A well-defined capability model helps leaders identify synergies across teams, ensuring AI investments deliver enterprise-wide benefits.
Governance should be an enabler, not a bottleneck. Establishing a governance framework ensures alignment between teams, facilitates collaboration, and prevents duplication of AI-powered initiatives. It also creates clear communication channels to sustain horizontal integration.
At some stage, all functions must come together—whether at the start of detailed design or during execution. Convergence helps prioritise initiatives based on enterprise-wide value, rather than individual departmental gains. AI implementation particularly benefits from this approach, ensuring resources and technologies are deployed strategically.
Transformational change is most effective when it complements ongoing efforts. Instead of disrupting current initiatives, organizations should focus on enhancing existing processes and integrating AI solutions where they add the most value. This collaborative mindset fosters a smoother transition to a horizontal model.
The shift to a horizontal design is not just a structural change—it’s a strategic necessity in today’s AI-driven world. Organizations that successfully transition can achieve:
With AI at the core, organizations must ensure their operating models evolve to support digital transformation, intelligent automation, and business process optimization. A siloed approach will only limit AI’s impact, while a horizontal, integrated structure will enable long-term, scalable success.
Transitioning from a functional to a horizontal operating model is challenging, particularly in large, complex organizations. However, businesses that embrace a cross-functional, AI-integrated approach will be better positioned to drive operational excellence and unlock future growth.
Organisations are increasingly recognising AI's potential to enhance operational efficiency. However, many implementations deliver only marginal gains because AI is often treated as an auxiliary tool rather than a core enabler. A more transformative approach involves embedding AI at the heart of process design, enabling a fundamental shift in efficiency and effectiveness.
The first step is developing a strong understanding of AI and automation technologies, including:
By establishing this knowledge base, organisations can identify high-impact opportunities and redefine processes with AI at their core.
Rather than layering AI onto existing workflows, companies should rethink processes from the ground up, focusing on:
A phased implementation ensures smoother transitions and continuous value realisation. This includes:
Traditional onboarding involves manual data entry and fragmented communication. AI can streamline this by:
Meetings often suffer from inefficiencies in scheduling, note-taking, and follow-ups. AI can enhance productivity through:
To achieve meaningful efficiency gains, organisations must:
Organisations looking to achieve breakthrough efficiency should prioritise AI-driven process design. By embedding AI as a foundational element, adopting a structured methodology, and iterating based on real-world insights, companies can unlock significant operational advantages and drive sustainable growth.
The procurement landscape is rapidly evolving, with increasing pressure on organisations to reduce costs, streamline processes, and make smarter purchasing decisions. As traditional procurement methods struggle to keep up with growing complexities, AI and automation are emerging as powerful tools that can transform the way procurement operates.
AI in procurement offers more than just cost savings—it brings a new level of insight and efficiency. From predicting demand to automating supplier management, AI-driven tools allow procurement teams to focus on strategic activities while leaving repetitive tasks to intelligent systems.
This white paper explores how AI and automation can help procurement teams achieve their goals. We delve into specific use cases, such as predictive analytics for spend forecasting, automating supplier onboarding, and AI-driven decision-making in sourcing. Additionally, we outline the critical success factors for implementing AI solutions and provide a roadmap to realising the full benefits of these technologies.
As organisations navigate economic uncertainty and increasing global competition, AI in procurement provides a clear path to optimising processes, reducing risks, and driving long-term value.
The employability sector faces growing challenges in meeting the demands of participants, providers, and funders alike. With increasing administrative burdens, tighter budgets, and the pressure to deliver better outcomes, organisations are seeking innovative solutions to optimise their processes and enhance participant engagement.
AI and automation present a powerful opportunity to transform the employability landscape. By integrating these technologies, providers can streamline administrative tasks, personalise participant support, and make data-driven decisions that improve overall outcomes.
This white paper explores how AI and automation can help employability providers reduce admin, increase participant engagement, and improve job outcomes. It outlines key use cases, including automating participant communications, predictive tools to identify high-impact factors for job success, and optimising case management. Additionally, we examine the critical success factors necessary for a smooth AI implementation and provide actionable insights for realising the full benefits of these technologies.
How AI and Automation Can Help Optimise Costs in Higher Education
Higher education institutions in the UK are facing unprecedented challenges. With Brexit making it more difficult to attract international students—who often contribute significantly to university revenues—the sector is feeling the strain. Adding to this pressure, the government has made it clear that it won’t be offering financial bailouts to universities, forcing institutions to take decisive action to manage their costs. As a result, cuts must be made, and universities are under increasing pressure to streamline their operations without compromising on the quality of education.
The Slow Road of Traditional Cost-Cutting Measures
In response to financial strain, many universities are resorting to organisational redesigns, such as restructuring departments and cutting underperforming courses. While these measures can lead to cost reductions, they often take a long time to materialise. Courses may need to be wound down over several years, leading to a delayed realisation of benefits and return on investment (ROI).
Given the urgency of the current situation, relying solely on traditional approaches may not be sufficient to safeguard the financial sustainability of universities.
The Fast-Track Solution: AI and Automation
This is where AI and automation come into play. By integrating these technologies into university operations, institutions can accelerate cost-saving measures while unlocking long-term value. AI and automation provide immediate relief by reducing the administrative burden, freeing up staff capacity to focus on higher-impact work, and improving operational efficiency.
The key benefits of implementing AI and automation include:
Critical Success Factors for AI Implementation
While AI and automation present exciting opportunities, their success hinges on certain critical factors:
Conclusion: The Way Forward
With Brexit-related challenges and limited government support, it is a critical time for UK universities to rethink their cost structures. While organisational redesigns and course reductions are traditional approaches, they may take too long to deliver the necessary impact. AI and automation offer a faster, more sustainable solution, providing immediate cost relief and enabling universities to focus on what they do best—delivering high-quality education.
By embracing AI and automation, universities can streamline their operations, enhance student experiences, and unlock long-term savings, all while positioning themselves for a successful future in an increasingly competitive landscape.
In this post, we’ll explore how AI and automation are transforming the future of procurement and sourcing.
Imagine starting your sourcing process with AI analysing vast amounts of data to find the best suppliers. Machine Learning, analyses key factors like cost, quality, risk, and sustainability to recommend the most suitable suppliers. It continuously learns from past decisions, refining its recommendations over time.
AI Agents manage routine supplier outreach and basic negotiations, while complex issues are escalated for human intervention.
Generative AI adds further value in contract management. It can draft standard contracts, extract key information such as payment terms, and track contract performance. AI tools can automatically monitor contract details, flagging potential risks like overspend or missed deadlines before they become problems, ensuring full visibility and control without the need for constant manual oversight.
Machine Learning and AI-powered analytics are driving predictive pricing and spend management. By analysing historical data, market trends, and supplier performance, AI can forecast future price fluctuations. This gives procurement teams a heads-up on upcoming changes, allowing them to act early, lock in favourable prices, and avoid unexpected cost hikes.
Much like monitoring stock prices for the best time to buy, AI keeps a close watch on market conditions and alerts businesses when the optimal moment to make purchasing decisions arises. AI also runs simulations to show how changes in suppliers or consolidating spend could impact overall costs, providing data-driven insights for better decision-making.
Morning: Reviewing AI-Generated Insights
As you start your day, AI tools have already processed supplier data, market trends, and internal performance metrics. The system flags a potential price increase for a key material and suggests an alternative supplier. With just a few clicks, you can assess the AI’s recommendations and make an informed decision quickly.
Midday: Automating Supplier Negotiations
By midday, an AI agent is handling basic supplier negotiations, while AI tracks contract details, allowing you to step in only for complex discussions. This frees up time for you to focus on building deeper relationships with key suppliers and identifying further opportunities for cost optimisation.
Afternoon: Predictive Spend and Strategic Optimisation
In the afternoon, AI flags procurement categories that could benefit from cost-saving measures. You use AI tools to run simulations and scenario analyses, evaluating the potential outcomes of switching suppliers or renegotiating contracts. AI gives you clear, data-driven insights, helping you make confident, proactive decisions that enhance overall procurement efficiency.
End of Day: Planning Ahead with AI Insights
As your day winds down, AI alerts you to upcoming contract renewals or potential supply chain risks. Armed with this real-time information, you begin planning future procurement strategies, confident that AI will continue to support you in making smarter decisions.
AI and automation are revolutionising procurement and sourcing. From supplier selection to contract management, predictive pricing, and spend optimisation, tools like Machine Learning and Generative AI are enabling businesses to work more efficiently and make smarter decisions.
For Category Managers, AI shifts the focus from routine, time-consuming tasks to high-level strategic work. AI empowers you to make informed, proactive decisions that lead to better supplier relationships, cost savings, and improved overall procurement performance. The future of procurement is here, and businesses that adopt AI-driven technologies will stay ahead in this rapidly evolving space.
Introduction
Delivering successful change initiatives, particularly when integrating AI, requires more than just deploying technology. Organisations that thrive in transformation understand that principles such as clarity, leadership, and strategy alignment are critical for both traditional change and AI-driven transformation. In this blog, we’ll explore the key principles that underpin effective change and how these same fundamentals are essential to AI transformation.
The Challenge
Many businesses struggle to implement change initiatives effectively, often due to a lack of clear direction, inadequate leadership, or insufficient relevance to the workforce. Similarly, AI transformations are often derailed by excitement over technology rather than a structured approach. Without a strong foundation, AI projects can fail to deliver value, becoming just another underutilised tool.
Below, we outline the principles that ensure successful change initiatives and how they also apply to AI transformation.
For any transformation, clarity is the first step to success. A well-defined vision provides direction and purpose, especially in AI projects where the complexity of the technology can overwhelm teams. Your AI transformation must begin with a clear understanding of the business challenge you're addressing and how AI will deliver tangible value. Without this, teams may lack focus, and the project risks veering off course.
Even the most advanced AI technologies require solid foundations to succeed. This means your organisation must have strong processes and reliable, well-organised data. Before embarking on an AI initiative, ensure that your data infrastructure and operational processes are capable of supporting the new technologies. AI must build upon solid processes and accurate data to drive meaningful outcomes.
Successful change isn’t achieved on the sidelines, and neither is AI transformation. Both require dedicated resources, including skilled personnel and sufficient time for implementation. Organisations often underestimate the time and investment needed for successful AI deployment, treating it as a secondary task. For AI to deliver real value, it requires focus, budget allocation, and ongoing support.
Investment goes beyond financial support. Strategic investment in AI transformation means allocating not only money but also human capital and infrastructure. Your AI initiatives should be integrated into the broader business strategy, with investments directed toward long-term sustainability, including future-proofing, upgrades, and continuous improvements.
Change initiatives work best when both leadership and teams share responsibility. The same applies to AI projects, where shared accountability between AI developers, business units, and leadership is crucial. Leaders must remain engaged throughout the project lifecycle, driving alignment with the business’s strategic goals. A lack of leadership involvement can result in misalignment and missed opportunities for optimisation.
Successful change occurs when stakeholders understand the personal and professional relevance of the transformation. AI transformation must not only be seen as a business imperative but also show individual benefits—whether it’s reducing repetitive tasks or improving decision-making accuracy. Clear communication about how AI impacts various teams ensures greater buy-in and adoption.
Effective leadership is critical for any transformation initiative. Whether for business change or AI deployment, leaders are the driving force that ensures the project maintains momentum and stays on track. Leadership must communicate the vision, resolve challenges, and ensure that AI initiatives deliver the desired outcomes. Strong leadership creates confidence in both the technology and the process.
Both change and AI must address real, tangible business problems. Implementing AI for its own sake risks wasted resources and frustration. Instead, AI should be a tool to solve operational challenges, automate mundane tasks, or extract new insights from data. By focusing on solving real problems, AI delivers value that is directly tied to business outcomes.
AI transformation, like any change initiative, must be fully aligned with the company’s broader business strategy. AI adoption should not be driven by external hype but by how it supports long-term objectives. Ensuring strategic alignment from the outset allows AI to become a core enabler of the business rather than a standalone experiment.
A successful AI transformation doesn’t end with deployment—it requires ongoing governance and support. Without sustainability measures in place, the value of AI solutions diminishes over time. Establishing clear ownership, ensuring regular updates, and maintaining the system are key to delivering long-term success. This step is crucial to keeping the transformation relevant and valuable.
AI transformation, like any successful change initiative, must deliver measurable outcomes. Whether it’s cost savings, efficiency gains, or improved customer experiences, the benefits of AI need to be clearly defined, measured, and communicated. Organisations should track these outcomes closely to ensure continued investment in AI delivers ongoing value.
Effective transformation requires action, not just planning. AI projects should focus on delivering tangible results early on, avoiding the common trap of over-analysis. Quick wins build momentum, demonstrate value, and help maintain organisational support. By staying action-oriented, organisations can move from concept to reality more efficiently.
Conclusion
The principles of effective change are universal, and they apply equally to AI transformation. By focusing on clarity, strong leadership, alignment with strategy, and delivering measurable outcomes, organisations can navigate the complexities of AI and ensure it becomes a sustainable driver of business value. When AI transformation is approached with these core principles in mind, it delivers not just technological change but lasting business impact.
FAQs
Generative AI chatbots are becoming essential tools for businesses, enabling automation, increasing productivity, and fostering creativity. With top players like Microsoft Co-Pilot, Google Gemini, and ChatGPT, the challenge is selecting the right AI solution for your business. In this detailed comparison, we’ll explore their key features, help you evaluate which fits your needs best, and even discuss when building a custom AI digital assistant might be the smarter choice.
Microsoft Co-Pilot is a generative AI assistant designed to work within the Microsoft 365 ecosystem, integrating with Word, Excel, PowerPoint, and Outlook. Released in 2023, Co-Pilot leverages OpenAI’s GPT-4 to automate repetitive tasks, provide data insights, and streamline document creation.
Google Gemini is Google’s generative AI chatbot, launched to integrate into Google Workspace tools such as Docs, Gmail, Sheets, and Slides. It replaced Google Bard and offers powerful text generation and image creation capabilities. However, full integration with Google Workspace is still underway.
ChatGPT, created by OpenAI, is a widely popular conversational AI solution. Available in a free version powered by GPT-3.5 and a Plus version with GPT-4, ChatGPT is known for its versatility, handling tasks from content creation and coding assistance to customer service.
Choosing between Microsoft Co-Pilot, Google Gemini, and ChatGPT depends on several factors. Below are the top seven evaluation criteria to help guide your decision:
Microsoft Co-Pilot and Google Gemini charge approximately £20-30 per user/month, while ChatGPT offers a free version and ChatGPT Plus at £20/month, making it more flexible for budget-conscious businesses.
Microsoft Co-Pilot integrates with Microsoft 365, making it ideal for businesses already using Microsoft tools. Google Gemini works best with Google Workspace, while ChatGPT is cloud-agnostic but may require custom integration depending on your setup.
Microsoft Co-Pilot is ideal for automating business productivity tasks within Microsoft 365 apps. Google Gemini excels in creative tasks like writing and brainstorming in Google Docs. ChatGPT is versatile across many use cases, including content creation, coding, and customer service.
Microsoft Co-Pilot offers enterprise-grade security, suitable for industries with strict data protection requirements such as GDPR and HIPAA compliance. Google Gemini and ChatGPT also offer security features, but it’s important to verify if they meet your specific data privacy needs.
Google Gemini is the easiest to navigate with a clean interface, while Microsoft Co-Pilot offers more comprehensive features that can feel cluttered. ChatGPT is user-friendly and works well across platforms.
ChatGPT is known for its scalability, with flexible API integration options. Both Microsoft Co-Pilot and Google Gemini offer structured but scalable solutions that can grow with your business.
Microsoft Co-Pilot excels in business and data-related tasks with a focus on accuracy. Google Gemini is great for creative outputs, though it may require more fact-checking in technical tasks. ChatGPT strikes a balance, excelling in conversation-based tasks, content creation, and problem-solving.
While off-the-shelf AI solutions like Microsoft Co-Pilot, Google Gemini, and ChatGPT provide excellent functionality, there are cases where building a custom AI digital assistant might be the smarter choice. Here are scenarios where custom AI development could be beneficial:
If your business has specialised workflows or unique requirements, a custom AI solution can offer features that off-the-shelf AI tools can’t. For example, businesses in healthcare or finance may need AI tailored to meet industry-specific regulations.
A custom AI solution provides complete control over features and integrations. You can tailor the AI to work seamlessly with your existing systems and processes, ensuring it aligns perfectly with your business needs.
For industries with strict security protocols, like government or financial services, a custom AI solution can be designed with specific compliance and security measures in place. This allows for greater control over data handling, privacy, and adherence to regulations.
While a custom AI solution may require a higher upfront investment, it can offer long-term savings by being designed specifically for your business. Over time, a custom AI assistant can reduce inefficiencies and deliver a better return on investment (ROI) than a generic tool.
Choosing between Microsoft Co-Pilot, Google Gemini, and ChatGPT depends on your business’s unique needs, technology infrastructure, and budget.
If your business has specific requirements that off-the-shelf AI solutions can’t meet, building a custom AI digital assistant could provide the tailored features, security, and long-term scalability you need.
By carefully considering the evaluation criteria outlined in this article, you can make an informed decision that will help your business maximise productivity and innovation using the right AI assistant.
7 Common Reasons Your AI Digital Assistant Will Fail (And How to Fix Them)
Building an AI digital assistant sounds like an exciting venture. Whether it’s for a specific function or general knowledge base tasks, the promise of automation and efficiency is hard to resist. From building custom AI chatbot to integrating Microsoft Co-Pilot or using models like OpenAI’s ChatGPT, the appeal of AI-driven automation is undeniable. But here’s the reality: your AI digital assistant will likely face challenges at first. Knowing what to expect and how to address potential pitfalls is key to success.
This doesn’t mean you shouldn’t build one, but you need to know what to expect and how to mitigate potential issues. Here are seven common reasons your AI digital assistant may fail and how to fix them.
When developing an assistant, costs often escalate, especially if you're using advanced models like GPT-4. As usage grows, so do expenses for processing power and storage. The more your assistant interacts with users, the higher the cost of managing and scaling your system.
How to overcome it:
When planning your AI digital assistant, factor in scaling costs upfront. Evaluate the most suitable AI models for your business needs, and explore cost-effective alternatives for basic tasks to avoid over-reliance on expensive models.
Large Language Models (LLMs) like GPT-4 can produce "hallucinations," where the model generates incorrect or unsupported information. This is a serious problem when using an AI digital assistant for business-critical tasks or customer interactions.
How to avoid this:
Implement fact-checking mechanisms and design your AI digital assistant to pull from verified data sources. Proper development and training can significantly reduce the chances of hallucinations.
Your AI digital assistant will only be as good as the data it’s trained on. Many businesses try to point their assistant at unstructured, incomplete, or inaccurate data, leading to poor results and frustrated users.
Solution:
Clean and structure your data before using it to train your AI digital assistant. Ensuring that your data is accurate and well-organized will lead to better and more reliable performance.
An AI digital assistant won’t automatically produce perfect results. Without proper development and continuous learning, responses can be inconsistent or even irrelevant, which will frustrate users and reduce the assistant’s effectiveness.
How to fix it:
Work with experienced AI developers who understand the nuances of AI system development. Additionally, ensure your team is trained to ask the right questions for more accurate responses from the assistant.
Initial expectations for AI digital assistants are often unrealistically high. People expect seamless interaction and flawless automation, but your AI digital assistant will likely require iterations and improvements over time.
How to manage this:
Set realistic expectations with your users from the outset. Be transparent about the assistant's development and emphasize that improvements will occur over time. Clear communication can help users appreciate the long-term benefits and avoid frustration in the early stages.
Many businesses assume that building an AI digital assistant is easy with no-code platforms. However, successful AI development requires experienced developers who understand API integration and the broader infrastructure required to make everything work.
Action:
Invest in AI developers with the right expertise. Having the right team ensures that your AI digital assistant functions smoothly and integrates effectively into your existing systems.
Even the most advanced AI digital assistants won’t succeed if the underlying business processes aren’t adjusted. Your assistant needs to fit seamlessly into your team’s workflows.
What to do:
Redesign your workflows to incorporate the capabilities of the AI digital assistant. Clearly communicate the benefits, such as time savings or improved accuracy. Without proper process adjustments, your AI assistant may be underutilized or ignored altogether.
Key Takeaway: Prepare for a Journey, Not a Quick Fix
Building a successful AI digital assistant is not an overnight process. Expect challenges such as cost overruns, data cleanup, and user adoption hurdles. However, with proper planning, expert development, and realistic expectations, your AI digital assistant can become a valuable asset to your business.
FAQs
Q: What’s the best way to avoid high costs with GPT-4 or similar models?
A: Use a hybrid approach where complex tasks are handled by LLMs like GPT-4, and simpler tasks are managed by more cost-effective tools. Plan for long-term costs when designing your AI project.
Q: How can I avoid hallucinations in my AI digital assistant’s responses?
A: Use retrieval-augmented generation and integrate fact-checking mechanisms into your assistant’s design. Additionally, ensure the model is trained on clean, accurate data sources.
Q: How do I make sure people actually use the AI digital assistant?
A: Focus on process redesign and clearly communicate the assistant’s benefits. AI for process automation only works when users understand how it fits into their daily workflows.
In recent years, Large Language Models (LLMs) have rapidly evolved, becoming a cornerstone of artificial intelligence applications in businesses. The release of tools like ChatGPT has brought LLMs into the spotlight, showcasing the potential of Generative AI for automating tasks, creating content, and transforming customer experiences. But how do LLMs work, and how do you choose the right one for your organisation?
LLMs are AI models trained on vast amounts of text data, enabling them to predict the next word or sentence, similar to a more advanced version of auto-complete. Early models, such as GPT-1, were limited in their output, often producing incoherent text. However, modern models like GPT-4 have significantly advanced, capable of generating thousands of meaningful words with context, coherence, and insight.
LLMs break down text into smaller units called tokens, which are then processed using mathematical models. These tokens form the foundation for neural networks, which are composed of layers that work together to predict text. The more layers and nodes a model has, the more complex and sophisticated its output becomes.
Before diving into use cases and selecting an LLM, let’s clarify some essential terms:
LLMs are revolutionising business operations by improving productivity, automating tasks, and enhancing customer interactions. Here are some key areas where LLMs are making an impact:
LLMs streamline customer interactions and automate routine tasks:
LLMs enable businesses to automate content generation:
LLMs help break down language barriers:
LLMs automate repetitive tasks and improve efficiency:
LLMs support legal teams in document review and compliance monitoring:
Tools like Microsoft CoPilot and ChatGPT are powered by LLMs. Microsoft CoPilot integrates LLMs into Word and Excel, automating tasks like document drafting and data analysis. ChatGPT provides conversational AI, responding to queries in real time.
Selecting the right LLM can be challenging. Consider the following factors:
| LLM Name | Context Window | Full Multimodal Support | Latency | Developer | Volume (Tokens per second) |
|---|---|---|---|---|---|
| GPT-4 | 128K tokens | Yes | Low | OpenAI | 37.5 |
| Claude 3 | 200K tokens | No | Medium | Anthropic | 57.3 |
| Gemini 1.5 Pro | 1M tokens | Yes | Low | 89.5 | |
| Gemini 1.5 Flash | 1M tokens | Yes | Low | 207.9 | |
| Mistral 7B | 33K tokens | No | Medium | Mistral | 106.8 |
| LLaMA 2 Chat (70B) | 4K tokens | No | Medium | Meta | 52.1 |
| LLaMA 2 Chat (13B) | 4K tokens | No | Medium | Meta | 48.3 |
| Falcon 40B | 4K tokens | No | Medium | TII | 0 |
| Cohere Command-R+ | 128K tokens | No | Medium | Cohere | 47 |
| Jurassic-2 | 10K tokens | No | Medium | AI21 Labs | 87.1 |
| GPT-NeoX | 20K tokens | No | Medium | EleutherAI | 20 |
| PaLM 2 | 32K tokens | Yes | Low | 80 | |
| Bard | 32K tokens | Yes | Low | 80 | |
| Claude 3 Opus | 200K tokens | No | High | Anthropic | 25.4 |
| Mistral Medium | 33K tokens | No | Low | Mistral | 18.2 |
| Mixtral 8x22B | 65K tokens | No | Low | Mistral | 69.8 |
| Command-R | 128K tokens | No | Low | Cohere | 47 |
| Phi-3 Medium 14B | 128K tokens | No | Low | Microsoft | 50.6 |
| DBRX | 33K tokens | No | Medium | Databricks | 86.5 |
| Mixtral Large | 33K tokens | No | Medium | Mistral | 36 |
Source - LLM Leaderboard - Compare GPT-4o, Llama 3, Mistral, Gemini & other models | Artificial Analysis
For businesses looking for high performance, GPT-4 and Gemini 1.5 Pro are robust solutions. If you're on a tighter budget, Mistral 7B and LLaMA 2 Chat are cost-effective options with solid performance.
Open-Source vs. Proprietary Large Language Models
LLMs generally fall into two categories: Proprietary models and open-source models. Proprietary models like GPT-4 are developed by private companies and offer extensive support through APIs or chat interfaces. In contrast, open-source models like LLaMA 2 are freely available, allowing businesses to build directly into their environments and customise to their needs.
When choosing between a custom-built LLM and a pre-trained one, consider the complexity and specificity of your project. Pre-trained models are ideal for generic tasks and fast deployment, while custom-built models are better for complex, domain-specific requirements.
The right LLM can significantly enhance your business processes, automate tasks, and improve productivity. When selecting an LLM, consider factors such as your budget, project scope, and specific use case. Whether you opt for a proprietary model or an open-source solution, staying informed about the latest developments in LLM technology will help you make the best choice for your business.
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