Technology Reseller v65

01732 759725 38 GENERATIVE AI Alex Luketa, CTO of artificial intelligence (AI) for business consultancy Xerini, explains how businesses can get the most out of generative AI competitive advantage, businesses will need to do more than just use standard models. However, this doesn’t have to be complex; software engineers can build a straightforward wrapper that transforms an OpenAI model into something specific to their use case. For example, wrapping GPTstyle models with prompts or guard rails can help configure them quickly and help overcome accuracy issues. OpenAI tools can also be made more traceable and formatted to show why an answer was given, with links to the source material so that humans can double check the answers. Bias is another weakness in OpenAI systems. I experienced this myself when I recently asked ChatGPT to write a thank you note to my wife and found its answer heavily emphasised her role as a mother. Naturally, businesses do not want to implement biased AI systems, particularly if they are using the tool for something sensitive like screening a CV. Software consultants can help build guard rails and prime an OpenAI system to minimise biases or train an AI tool on the business’ proprietary data, which is less likely to contain biases. Managing privacy concerns ChatGPT is fuelled by online data, and employees may inadvertently hand over sensitive data in their queries, which is a privacy concern. It is possible to make small modifications that sit over the OpenAI API to improve this, such as holding the data for a limited number of days and ensuring it is not used for training. Often, when engaging with a consultancy to develop bespoke solutions, businesses prefer to retain ownership of IP. In many cases, it’s not the tool (e.g. Xefr) or the AI model where IP resides, but the data + model + training process that gives rise to a trained system. This will be unique for each client and is something they can own. Where we do need to write custom software (e.g. business-specific calculations) we can do this in auxiliary projects that are separate to Xerini codebases and this, too, is something the customer (or partner) can retain IP ownership of. Finally, where there are privacy concerns, we can train and fine-tune open-source models that are bespoke to the client and can run on private hardware without leaking data to thirdparty services. Managing the process Any investment in technology is ultimately a risk. The largest barrier is ensuring that the benefits are tangible and properly understood. Taking an incremental approach and breaking things down into manageable phases, with each delivering identifiable value but carrying less risk can be a pragmatic solution. There is also a people element, especially the fear of losing jobs or even agency within current roles. With peoplecentric concerns, it’s important that we convey a message of enhancement rather than replacement. Employees will be able to get more done in less time, and this will make their lives easier rather than making their role redundant. Getting this message across is key because it reduces the number of objectors and potentially turns them into champions. Working with an experienced software consultancy can streamline the AI integration process greatly. An AI expert can help identify the tools that will best meet the needs of your business and can customise them to deliver more value. This can help avoid expensive platform migrations and enable you to integrate your desired AI functions quickly and smoothly. We believe all businesses regardless of size and situation are ready to start their AI journey whether that is through making better use of available tools like ChatGPT, integrating their systems and data via platforms such as Xefr or with fully bespoke model generation. While it’s exciting to type a quick query into ChatGPT – and ChatGPT is receiving an estimated 96 million visitors per month – the real value of AI will be realised when businesses can seamlessly integrate it with existing systems and data. Once connected to a company’s internal systems, AI can help solve challenging business problems in a quick and costeffective way. Some of the most impactful areas for AI in business are data management and analysis. Both structured and unstructured data can be tagged and classified, so that information is more accessible and easier to find using natural language search. For example, a business may want to onboard thousands of legacy certificates so that data is easily available to staff or simply ask the system a question raised by a client. Another example is data analysis, such as automatically screening CVs to shortlist candidates for a job role. Many of these tasks would previously have been too labour-intensive or technically challenging to be worthwhile. Before AI, the cost vs. benefit didn’t stack up. Now it does. There are hundreds of artificial intelligence tools and models with varying use cases, which can make the market difficult to navigate. There is no universal tool for every application and choosing the right one is important, so before investing in a tool, a business needs clarity on its capabilities. This means achieving good visibility on the data it does – and doesn’t – collect, knowledge of where and how it is stored, a clear articulation of the problem that needs to be solved, and the expected benefits of solving it. After establishing this, businesses can explore various options, including using OpenAI in its current state, enhancing its functionality through integration with a low-code data orchestration platform like Xefr, or opting for a fully customised private model. In our experience, to gain a true Natural language processing: a practical guide Alex Luketa