Organisations face a difficult dilemma: Should they accelerate AI adoption despite imperfect data, or should they wait until their data is fully optimised?
The instinctive response is to fix data first. After all, AI relies on clean, structured, and reliable data to deliver results. However, this approach comes with risks. Data perfection is an endless pursuit, and waiting too long to address every data challenge can stall AI initiatives—leaving organisations lagging behind competitors who take a more pragmatic approach.
The reality is, AI and data quality must evolve together. The organisations that succeed in AI transformation are not the ones that delay adoption but those that strategically integrate AI while continuously improving their data foundations.
Every organisation faces data quality issues. While resolving these challenges is important, insisting on perfect data before AI deployment presents three major risks:
Data governance programmes struggle to secure funding because they don’t deliver immediate revenue or visible quick wins. As a result, they are often deprioritised in favour of more tangible initiatives.
By the time data issues are fully addressed, competitors will have already implemented AI, gaining operational efficiencies and market advantage. In a fast-moving environment, waiting for perfection can be a costly mistake.
The phrase is too vague to act on effectively. Without a structured framework, data governance efforts can become resource-intensive, slow-moving, and misaligned with business goals.
Rather than treating AI adoption and data governance as separate projects, organisations should take a structured approach that allows them to balance immediate AI-driven impact with long-term data improvement.
Instead of postponing AI initiatives, businesses should establish a data improvement workstream alongside AI deployment. AI can actively contribute to better data management rather than waiting for a “clean slate.”
A structured roadmap ensures AI and data quality improvements align with strategic objectives. Key steps include:
Senior leaders, eager for AI-driven efficiencies, may push for rapid implementation. A structured approach ensures AI adoption aligns with corporate goals while maintaining realistic expectations about data readiness.
AI is not just dependent on clean data—it can also enhance data integrity. AI-driven tools can:
By using AI as part of data management, organisations can refine data while implementing AI, rather than delaying transformation efforts.
The notion that “AI can’t work until data is fixed” is outdated. A layered approach—one that unlocks AI’s value now while progressively improving data quality—is the most effective path forward.
It’s not about rewiring the entire house before installing smart lighting. Businesses can modernise where it makes sense while strengthening the foundation over time.
AI is changing how organisations operate, bringing both tremendous opportunities and significant challenges. On 22 October 2024, Hudson & Hayes leaders, along with the Digital and AI Community, outlined guiding principles for AI adoption. This collaborative session, led by David Gerouville-Farrell, formed the foundation for Cutting Through the Noise: A Business Leader’s Guide to AI. These principles provide the structure necessary for organisations to realise AI’s potential while effectively navigating its challenges.
AI should support your core business goals, not stand as a separate objective. To deliver tangible value, set AI goals that address specific needs, such as improving customer and employee experiences, improving profitability or creating a competitive edge.
Avoid AI for the sake of AI. Focus on measurable business benefits to ensure every initiative contributes directly to organisational growth.
AI’s use brings ethical concerns like data privacy, fairness, and accountability to the forefront. Create a governance framework that suits your organisation’s specific risk profile. Governance should vary depending on whether your organisation primarily consumes AI (uses external AI products) or builds AI (develops custom solutions). For example, a company that builds AI might emphasise governance around model transparency and ethical training data, while a consumer might prioritise privacy and data protection policies.
AI requires a robust infrastructure. Ensure AI solutions integrate well with existing systems and that interoperability supports a smooth user experience.
Even with AI, there’s always a user at the other end. Involve users in the design phase and focus on creating solutions that address their needs effectively, improving their overall experience.
Many teams lack foundational AI knowledge or expertise in tools. Invest in training programmes from basics to tools like Microsoft Co-Pilot, and consider bringing in partners to support growth.
AI can spark excitement and concern. To manage expectations, develop a communication plan for each stakeholder group, keeping everyone informed as the technology evolves.
AI should enhance, not replace, human work. While some roles may shift, communicate these changes transparently and focus on how AI can augment existing roles.
Moving to a product-focused model can help your organisation keep pace with AI’s rapid development. For example, shifting from traditional to agile workflows may increase your AI adoption speed and responsiveness to market changes.
Ensure AI delivery is accessible to all teams. Develop a clear model aligned with change processes to avoid bottlenecks and make AI adoption practical for every part of the organisation.
Adopting these guiding principles helps organisations navigate AI’s complexities. They offer a pathway for business leaders to integrate AI in ways that are ethical, aligned with strategy, and value-driven.
How AI is Transforming Healthcare Diagnostics
Healthcare systems worldwide, including the NHS, are under immense pressure to improve productivity, reduce waiting times, and deliver better patient outcomes. Diagnostics, a critical component of patient care, is one area where AI is poised to make a transformative impact. From streamlining administrative tasks to enhancing diagnostic accuracy and speed, AI offers numerous opportunities to optimise processes and improve patient care.
However, barriers such as disparate systems across healthcare providers and unclear decision-making structures can hinder the widespread implementation of AI. Despite these challenges, the need to improve efficiency and care quality has never been greater. Now is the time to adopt AI to drive meaningful change in healthcare diagnostics.
In this blog, we’ll explore the key steps in the diagnostic process, highlight specific AI use cases that can help transform healthcare diagnostics, and outline critical success factors for successful AI implementation.
A typical diagnostic pathway in healthcare involves several key stages:
Let’s explore how AI can be applied at each stage of the diagnostic process to enhance efficiency and improve patient outcomes:
For AI to be successfully integrated into healthcare diagnostics, several critical success factors must be considered:
As healthcare systems worldwide, including the NHS, continue to face increasing demand and rising expectations, AI offers a clear path to improving diagnostics efficiency and patient outcomes. From automating appointment scheduling to assisting with image analysis, AI has the potential to revolutionise healthcare diagnostics. By addressing key barriers and ensuring that critical success factors are met, healthcare providers can unlock the full potential of AI and provide faster, more accurate care for patients.
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.
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.
The Labour government has set an ambitious target of increasing the employment rate from 75% to 80%, aiming to make it the highest in the G7. With over two million people expected to join the workforce, overcoming barriers to employment—especially for those facing health-related challenges or age-related barriers—is critical. The Restart Scheme and Work and Health Programme have laid a foundation by providing support services such as mental health and job training. However, to meet these ambitious goals, employability organisations must now embrace the full potential of AI, RPA, and Generative AI to streamline and enhance the support they offer.
Imagine a participant’s journey starting with a Digital Assistant powered by AI collecting their details before their first appointment. This eliminates time-consuming form-filling and allows participants to focus on meaningful interactions right from the start. Participants receive real-time responses to their questions, creating a seamless and engaging onboarding experience.
During their first appointment, participants feel fully supported as AI tools, instantly generate tailored action plans. This enables more personalised and productive conversations, while automated communications keep participants informed and engaged throughout their journey. RPA ensures that routine tasks such as form completion and appointment scheduling are handled efficiently, allowing coaches to focus on participant needs.
AI tools, especially Machine Learning, offer predictive insights into the likelihood of participants achieving their job outcomes. This data allows staff to take proactive measures when necessary, ensuring participants stay on track. Generative AI can further assist by automating complex tasks like meeting transcriptions and document creation, reducing manual effort. The use of these technologies improves job outcomes by enabling data-driven decisions and increasing participant engagement.
The integration of Machine Learning and Generative AI into the employability process allows for more effective, data-driven interventions, significantly increasing the likelihood of participants achieving their job outcomes. Predictive insights generated by AI empower staff to act when necessary, ensuring participants have the best possible chance of success.
By automating routine administrative tasks with RPA and offering personalised, real-time support through AI-powered Digital Assistants, participants remain more engaged and motivated throughout their journey. This enhanced engagement leads to more positive outcomes for participants and a higher overall satisfaction rate.
RPA automates repetitive tasks such as scheduling, form-filling, and meeting transcription, freeing up staff time to focus on more strategic activities. This reduces the administrative burden on employment coaches, allowing them to concentrate on the needs of participants and deliver higher-value services.
The future of employability lies in the integration of AI, RPA, Machine Learning, and Generative AI, offering a pathway to more efficient, effective, and engaging services. The Restart Scheme and other employment programmes have laid a strong foundation, but to meet the ambitious goals set by the Labour government, employability organisations must now leverage these technologies. By doing so, they will not only improve job outcomes but also increase participant engagement and reduce the administrative load on staff. The future isn't just about doing more—it’s about doing better, with AI and automation at the heart of this transformation.
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