There is no doubt about it, it is happening now, the relationship between people and their workplace technology is changing rapidly.
AI offers the chance to support innovation, creative thinking and imagination and the sense of value add that an employee experiences, in their workplace.
The changes in AI and robotics will undoubtedly fundamentally change the workplace and the jobs we do and importantly how we do them.
Let’s take Generative AI as one example of the many kinds of AI shaping our lives.
In Generative AI we are basically seeing the use of algorithms that use a large amount of content based on trained models and self-training models using multiple types of media (files, text, audio, video etc) to create new content either as an ‘original piece’ or in a flow of answers to questions or series of instructions.
The actual form of the ‘output content’ from the AI can be in many forms: text, image, video, audio, 3D or in fact a design like a diagram or say the design of a new drug.
The base of this Generative AI is LLM’s (Large Language Models) and the concepts that underpin their data relationships, training and connectivity like neural networks. These models often use a concept of ‘Tokens’. Tokens are like say, a character or a package of characters that make up a word. As you enter data, or the AI gets a task it takes what is input and works out statistically, the likelihood of the next appropriate response.
AI is probably the most disruptive technology introduction since the 1990’s.
AI will be like the early days of the internet…a bit obscure then 'hey presto' along comes a web-browser and everyone can get involved. As AI comes in on top of the existing internet base and the large advancements in mobile technology with its reach of technology into everyone’s pocket, the impact will be far greater and far faster than previous large changes like the internet itself.
One key area of change that could be impacted is Knowledge Management as companies seek to bring information to their employees, customers and clients in a efficient and effective manner.
Examples of simplified tasks for Knowledge Management will range from:
- super powered chatbots that not only answer a question but draw on many sources to deliver a response that has cross-referenced many sources of data.
- complex data and digital asset heavy industries where teams of engineers would normally have to be formed across domain expertise to analyse a wide range of data, in order to formulate actions plans and are currently relying on long drawn-out processes of searching and collation data to get the best results.
- Instant help in problem solving e.g. I have a pump A123, inverted with a YYY feed of chemicals that is corroded on the left input and pressure has dropped to 500PSI what are the options for repair?
- Needing to produce product prototypes at rapid speed for assessment in both design, costs and effectiveness.
All these types on innovation in the use of AI on top of the more readily seen ones in the public domains: writing text, providing guides, delivering code (a whole other topic!), creating images, creating presentations will see ‘knowledge’ and ‘knowledge workers’ impacted significantly.
As AI races away in terms of capability what are the key outstanding issues being addressed but still need to be addressed:
- The much openly discussed impact on jobs.
- The issue of AI becoming an ‘entity’ that cannot be controlled or indeed understood.
- Privacy and Safeguarding – how to control AI to protect intellectual property and the rights of individuals and groups.
- Security – ability for companies to have secure data and integrations to other systems and the individuals’ rights.
- Accuracy – removal of incorrect of hallucinatory data.
- Governance structures.
- Legal structures.
Despite these challenges the democratisation and release of human potential to make this a better world using AI is there and will not go away. As they say the genie is out of the bottle!
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