For more than a decade, Robotic Process Automation (RPA) has been the backbone of back-office efficiency. By mimicking keystrokes and clicks, RPA freed employees from repetitive, rule-based tasks like invoice entry and claims processing. It delivered measurable cost savings and faster processing times. But RPA has limitations: it breaks when processes change, struggles with unstructured data, and ultimately only scratches the surface of what’s possible with automation.
Today, a new wave is taking shape. AI Agents and Agentic AI are redefining how organisations think about digital transformation. The shift is not just from faster scripts to smarter bots — it’s from automation as task execution to automation as orchestration.
This journey can be mapped as an automation maturity curve with three stages:
Organisations that understand and climb this curve will move from incremental savings to step-change strategic value.
RPA thrives in high-volume, structured processes. It is best suited for tasks that follow clear rules and rarely change. Examples include:
The value case for RPA is straightforward: efficiency and cost reduction. By removing repetitive keystrokes, organisations gained speed and accuracy. However, fragility is its main weakness. A small change in process or system layout can break an RPA bot. And because RPA relies on structured inputs, it cannot handle the unstructured data that dominates knowledge work — such as emails, free-text fields, or conversations.
RPA is therefore the entry-level stage of automation maturity. It is useful, it delivers savings, but it is not transformative.
Where RPA mimics clicks, AI Agents understand context. Powered by large language models (LLMs) and integrated with enterprise tools and data, AI Agents can:
The breakthrough is in handling unstructured data. Emails, documents, and chat logs can be understood and acted upon. Unlike RPA, AI Agents are not brittle — they adapt to new inputs, guided by human oversight.
This makes AI Agents a bridge between efficiency and intelligence. They do not just automate tasks, they augment knowledge work. For employees, this means less time searching for answers or updating systems, and more time solving higher-value problems. For customers, it means faster, more personalised service.
As an example, a service desk agent supported by an AI Agent can resolve routine tickets instantly while escalating complex cases to human experts. The result is not only efficiency but a better end-user experience.
The frontier of automation is Agentic AI. Instead of following a single-task instruction, Agentic AI systems can:
Imagine the goal “optimise the procurement cycle.”
This is not just automation, it is orchestration. A network of AI agents collaborates to achieve an outcome, with minimal human input. Humans set direction, provide oversight, and make final calls.
The business value is transformational. Instead of speeding up existing processes, Agentic AI enables organisations to reimagine how work is organised. Procurement, supply chain, clinical scheduling, or customer onboarding can shift from sequential, human-driven tasks to parallel, AI-driven ecosystems.
The three stages form a maturity curve:
Importantly, each stage does not replace the last. They build on one another. RPA still has its place for structured processes. AI Agents elevate knowledge work. Agentic AI unlocks orchestration and adaptive decision-making.
Leaders must assess where they are today and design a roadmap for progression. A balanced portfolio will combine all three, applied to the right contexts.
Why does this matter now?
For organisations, the message is clear. Automation is no longer just a tool for cutting costs. It is a strategic lever for redesigning operations.
The companies that win will not be the ones who “just add AI.”
They will be the ones who climb the automation maturity curve, using the right tool for the right context:
We are moving from automation as cost-cutting to automation as strategy. The question is no longer “What can we automate?”
It is “How do we design our operating model for a world of autonomous, multi-agent systems?”
Customer Success teams are under increasing pressure to deliver more value to more customers; without burning out. The challenge is balancing efficiency with the personalised attention that drives long-term loyalty. This is where artificial intelligence (AI) can make a meaningful difference.
The real value of these capabilities is not just efficiency. With AI handling routine tasks, Customer Success Managers (CSMs) can focus on building the trusted relationships that underpin retention and growth.
For leaders, the biggest hurdle isn’t whether AI has potential, it’s knowing where to start. With so many platforms now claiming to be “AI-powered,” it’s easy to feel overwhelmed. Adding technology to messy processes rarely solves the problem. Instead, a product-agnostic approach works best.
Decide whether to augment or build. Off-the-shelf CS platforms can address many needs. For unique workflows, custom AI solutions may unlock greater gains.
AI will not replace Customer Success Managers, but CSMs who know how to use AI will replace those who don’t. The key is to start small, demonstrate quick wins, and build momentum over time.
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 employability sector is facing growing demands to improve outcomes, increase participant engagement, and reduce the administrative burden on staff. AI and automation are providing solutions that optimise processes, free up resources, and ultimately enhance the participant journey. Below, we outline seven key use cases showing how AI is making an impact in employability.
Engagement is crucial for job success. AI enables personalised communication at scale, combining data analytics with AI to boost participant engagement. Automated systems can adjust messaging based on real-time feedback and engagement, helping participants stay on track and increasing their chances of success.
Missed appointments and rescheduling can be a drain on both participants and staff time. AI-driven scheduling tools automate the process, ensuring participants are reminded of their commitments and offering optimised appointment times based on availability and preferences. This reduces wasted meeting slots and improves efficiency across the board.
Participants often have recurring questions about job applications, CV building, interview preparation, or local service providers. AI-powered digital assistants can handle these FAQs, providing instant answers and resources. This not only saves time for staff but also ensures participants receive timely support.
AI can track participant behaviour and predict engagement levels, flagging early signs of disengagement. By analysing data, such as communication patterns and attendance records, AI identifies at-risk participants so that coaches can intervene earlier, improving retention and outcomes.
Using machine learning, AI can assess the likelihood of a participant achieving a job outcome. By assigning a job outcome score based on various data points, AI allows staff to focus their efforts on those participants who need the most support. This insight can also help in forecasting programme success and tailoring interventions more effectively.
In employability programmes, meetings and appointments generate significant amounts of notes and paperwork. AI can automate note-taking and update records in real time. For example, an AI-driven meeting assistant can take notes and enter data directly into the employability platform, saving time and reducing the administrative load for staff.
Post-placement support is essential to ensure participants remain in work. AI-powered systems can automate regular check-ins with participants who have found employment, offering support and identifying potential issues early. These automated systems can trigger human intervention if needed, helping sustain long-term job retention and success.
These seven AI use cases highlight the transformative impact that automation and AI can have in the employability sector. By streamlining admin tasks, predicting engagement, and enhancing participant support, AI helps organisations improve outcomes while freeing up time and resources to focus on what truly matters—supporting participants on their journey to sustainable employment.
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.
Introduction: In this artificial intelligence in procurement case study, we highlight how a global organisation transformed its procurement processes, saving over 10,000 hours annually. By implementing AI-powered solutions, Robotic Process Automation (RPA), and advanced analytics, the organisation automated manual tasks, gained valuable insights, and significantly improved procurement efficiency. We also focused on educating the team on the use of Machine Learning and Predictive Analytics in procurement, which enhanced decision-making and forecasting accuracy.
Challenges: The organisation encountered several challenges that hampered procurement efficiency, such as manual data entry, outdated forecasting methods, and limited visibility into supplier performance. These issues caused delays and prevented the procurement team from concentrating on more strategic initiatives.
Approach: We addressed these challenges by implementing AI and automation technologies while providing tailored training on modern procurement strategies like Machine Learning and Predictive Analytics.
Results: Our initiatives led to a total time savings of 10,000 hours per year. Key results included:
Conclusion: This artificial intelligence in procurement case study showcases the transformative power of AI and automation in procurement. By automating key processes, educating teams on advanced procurement technologies, and optimising existing systems like SAP Ariba, we achieved over 10,000 hours in time savings, allowing the procurement team to focus on higher-value strategic initiatives.
Fill out the form, and we’ll send you a copy! It’s a comprehensive guide to implementing AI in procurement, complete with practical use cases, expert insights, and strategies to help you streamline processes and drive efficiency in your organisation.
© Hudson & Hayes | Privacy policy
Website by Polar
