The big breakthrough moment in the evolution of Artificial Intelligence (AI) was the unveiling of ChatGPT by OpenAI, late 2022. This sophisticated model – accessible through an incredibly user-friendly interface – has swiftly become extremely popular as it was probably the first generative AI (GenAI) solution available to the public that acted human and could solve real business problems.
Business professionals were swift to acknowledge its potential – from handling phone calls in customer service departments to summarising meetings or drafting emails, or helping employees quickly comb through internal information, GenAI has made a big impact very quickly.
Of course, decades-long scientific advancements, the availability of vast volumes of training data and the increasing affordability of processing power for model training have made these lightening developments in GenAI possible. In fact, the technology has advanced so rapidly, it often ‘just works’ for most common use cases.
This means use case development is sped up drastically – but although the technology is no longer the bottleneck, the pressure shifts to other critical factors needed to bring GenAI use cases to life, such as integration into existing processes, tools and workflows, regulatory compliance, quality monitoring, security and scalability.
These factors typically become evident during use case implementation. Hence, a multi-disciplinary approach is vital in bringing these GenAI use cases to life and creating value – what points do you need to bear in mind?
The building blocks to creating generative AI value
- Build a multidisciplinary team: to embed AI successfully in organisations a diverse set of qualified skills is required. The creation of a so-called Centre of Excellence (CoE) for GenAI can help to centralise knowledge for successful planning and execution. The CoE team should be made up of business experts, AI experts, change and communication experts, domain data experts, architecture experts and data management experts. This ensures there are people involved who understand the technology, the business requirements, AI governance issues and that the GenAI project can be implemented successfully.
- Understand strategic objectives: successful generative AI implementations require clear strategic objectives (e.g. increase first-time-right client contact, increase automated fraud analyses using AI) and alignment with the organisation’s context. In low-tech environments, focus on employee engagement; in fast-paced, data-driven settings, prioritise execution capacity. Tailor the approach to achieve impactful results.
- Prioritise use cases: with GenAI, it is important to map out all use cases and prioritise them according to their potential value and feasibility, making sure we thoroughly understand the related processes and context for each. Use cases can be both small and large, transformative or incremental, and it is usually advisable to pursue a balanced mix of these in parallel.
- Balance between technology and business factors: you need to consider the technology and the range of other factors you have to think about with GenAI adoption. It is vital to involve business stakeholders, explaining the potential, and ensuring the model provides meaningful output in a transparent, responsible manner. And any solution needs to be integrated into context, for example, you need to bear in mind the process and location it’s embedded in. All of these aspects combined will determine the value of the solution.
Of course, not all business problems require GenAI solutions and it’s important to recognise which do and which don’t. Integrating GenAI into existing processes and applications can be really challenging. It’s a new and exciting area and like all new developments, there can be problems as well as opportunities. A ‘gently, gently’ approach, taking all the above steps into account is key. It’s only then you can start to create value from your GenAI programme.
If you would like to speak to someone at Valcon about how to create value from your genAI programme, please contact: [email protected]
