Whether you are dystopian with your thinking and believe the world we now live in will come to an end, or you are utopian and can see a harmonious relationship with machines, the simple fact is that Artificial Intelligence (AI) is now a conversation at every level of life, and it is happening in all aspects of our daily lives!
The thing is, AI is not new; it has been around since the 1950s, and its journey continues to this day. The basic premise of AI is that it encompasses systems that mimic human behaviour and decision-making.
In simplistic terms, two types of AI are around today (referred to as narrow AI), and that is AI that’s trained to perform a specific or limited range of tasks:
- Reactive AI – operates only on present data and has limited capabilities. These are statistical models that can interpret large volumes of historical data and produce a meaningful output – things like spam filters or recommendation engines are examples of this.
- Limited Memory AI – can store data from past experiences temporarily; it gets smarter the more data it’s trained upon and gets smarter over time. They were designed to work with imperfect information and learn from historical data. Self-driving cars and chatbots are examples. These models do require vast amounts of data to learn simple tasks and can be retrained to advance, but changes to the AI’s environment would force it into full retraining from scratch.
The Hype
The current fanfare, as such, is based around Generative AI (Limited Memory Machine), which, unlike other types of AI, focuses on creating new data (e.g. images, text, audio) rather than just providing an outcome to existing data. This has some great capabilities to help drive efficiencies for businesses – namely helping get to better business decisions quicker.
In every conversation that I have been a part of, either with customers or prospects, every event or seminar that I have attended, everybody is talking about the benefits of Generative AI that it can potentially bring, and the fact that it is not about whether we should be using Generative AI, it is about what and when we are going to be using it for. To this point, I want to summarise what I think about Generative AI and how organisations should be approaching its use.
Applicability Of Generative AI
Because Generative AI can not only learn, but because it can create new data, there are huge efficiency possibilities for organisations. I highly recommend a book written by the futurist Patrick Dixon: How AI Will Change Your Life if you want to get a feel for how it could impact your day-to-day life and certainly how he sees the impact that it will have on certain industries.
To give you an idea of how I am working with my team currently, we have identified and are actively working on use cases for Generative AI whereby it will deliver efficiency gains around time, quality, and consistency for some manual tasks, which impact customer experience, such as Statement of Work (SoW) and Proposal development. This is just the tip of the iceberg.
Generative AI is also transforming industries like healthcare (through diagnostic analysis), customer service (with personalised responses), and media (automated content creation). When you think more generically, you can identify areas such as:
- Creation of Synthetic Data
- Chatbots
- Email Responses
- Diagnostic Analysis
Whilst this all sounds great, on the surface, it is my opinion that organisations should weigh up the applicability of Generative AI on a use-case-by-use-case basis, as it may not be as good as it looks on paper!
There are two broad areas for me that organisations need to consider before going full steam ahead.
1. AI Readiness
Is your organisation ready to effectively utilise and scale AI technologies?
Whether it’s the data, your infrastructure, or your organisation – not being ready to embed this capability effectively will end in failure.
- Organisation: Do you have the governance, processes, culture, skills, and strategies in place to adopt and deploy AI solutions? This includes having the correct teams and a clear strategy for integrating AI into business processes.
- Infrastructure: Do you have the correct hardware, software, and cloud solutions that can support AI workloads?
- AI-ready software: Are your applications designed to integrate with AI models, either by having APIs, plugins, or modules that can easily connect with machine learning algorithms?
- AI-ready data: Is the data structured, clean, and formatted in a way that allows AI models to easily use it for training or processing? This can include labelled datasets, standardised formats, and the removal of noise or irrelevant information.
2. AI Fit
Is your use case appropriate for AI, or can its outcome be achieved in another fashion?
Being fit for AI means that a problem or process is well-suited for AI technologies, considering factors like data availability, scalability, practicality, and alignment with strategic objectives.
- Problem-Solution Fit: Can AI address the problem better than traditional methods? For example, tasks that involve pattern recognition, prediction, natural language processing, or automation are generally well-suited for AI. If a problem can be defined clearly and has data that can be used to train a model, it’s likely to be a good AI fit.
- Data Availability and Quality: An AI solution is more likely to be effective if there is enough high-quality, relevant data. Without adequate data, even the best AI models won’t perform well. Therefore, AI fit includes having the right type of data that can be leveraged for training and refining AI models.
- Scalability & Efficiency: AI fit also considers whether AI will improve the scalability, speed, and efficiency of a process. For example, repetitive tasks, large-scale data analysis, or situations where human effort would be too time-consuming are typically a good fit for AI-driven automation.
- Feasibility & Practicality: Sometimes, a problem might seem appropriate for AI, but the implementation is not feasible due to costs, ethical concerns, or technical limitations. AI fit evaluates whether it’s practical to use AI for a particular application, considering factors like cost, complexity, and the required infrastructure.
- Strategic Alignment: For organisations, AI fit means that adopting AI aligns with business goals and strategy. Implementing AI should provide a clear value proposition, whether it’s increasing revenue, improving customer experience, or reducing costs.
Summary
Whether you envision us living in harmony with machines like in The Jetsons, or constantly watching out for something like SKYNET, AI is here to stay. While using Generative AI comes with challenges, as with most things in life, the results depend on the effort put in—and this applies to the success of Generative AI in the workplace.
For me, it is important that, when considering the use of Generative AI in a business, proper governance and control are exercised. Additionally, the business must ensure that the use case is fit for purpose when deploying and managing such technologies.
At Nephos, we combine technical expertise and the strategic business value of traditional professional service providers to deliver innovative data solutions. Our solutions ensure your data is fully prepared for AI, enhancing data quality and compliance, so you can achieve more accurate, actionable insights and unlock the full potential of AI-driven outcomes. Click here to know how.
