All work is to some extent knowledge work, but what is mostly done with thought work and creativity is usually included within the concept of "knowledge work". Most knowledge workers are relatively highly paid and their time should therefore be used wisely. Through automated knowledge work, tasks that are distracting and where employees do not use their time and skills in the best way can be handled.
Antti Nivala is the founder and CEO of M-Files and he predicts what will characterize development in 2024. The trend is clear around what enables us to avoid information chaos, spend less time on manual tasks and instead work smarter and more efficiently with the help of AI and "knowledge work automation". Here is a sample.
Knowledge management will become more holistic.
We know that employee productivity is crucial for companies' growth and profitability. We also know that they work best in a concentrated state where they can be creative, think freely and solve problems. Anything that inhibits that flow creates a distraction from the work. In the coming year, we can therefore expect more companies to take a holistic approach to all flows and automate work steps to become more productive.
By investing more in "knowledge work automation", it is also possible to eliminate information chaos and streamline the business processes. It ultimately reduces the amount of time-consuming manual work and allows employees to work smarter while offering customers a better experience.
Integration and data curation will become imperative.
Lack of integration between systems often prevents the flow of information and creates silos that artificial intelligence has difficulty overcoming. For an AI application to work, it needs access to the data to be processed. In addition to access, organizations must therefore ensure that AI applications only access relevant and accurate data.
In 2024, we can therefore expect organizations to prioritize the integration and curation of data. All to ensure that they can effectively feed their AI tools with data that gives them an advantage over the competition.
Ethical AI will take center stage.
Artificial intelligence is now regulated by governments worldwide. A couple of examples are President Biden's executive AI order and the EU's AI law, which will provide stability and guidelines for the rapidly expanding technology. Mass adoption of AI represents one of the most important opportunities in the history of business, and organizations must ensure that they use solutions built on a foundation of high-quality data.
With data quality as a central part of the AI issue, it is imperative to have an ethical approach to AI and understand how large language models (LLM) works and where they get their knowledge from. LLM:ers can be powerful tools, but only if they are based on reliable information. To better contribute to the safe and responsible development of AI, we will see organizations begin to treat AI as a trainee and consider the results as a recommendation rather than instructions. We will also see regular audits and human involvement increase to ensure as much as possible that AI solutions are reliable.
The time of the customized AI assistants is here.
It may take time to see how best to integrate AI assistants into the enterprise toolbox to unlock the full potential. At the same time as AI assistants have a variety of uses, in 2024 we can expect vendors to tailor AI assistants for industry-specific, value-creating tasks. It can help employees quickly absorb information and solve tasks with human creativity.
User-friendly AI assistants will become more industry-specific and more personalized. Users can increasingly expect the assistant to understand industry jargon and tailor the response based on their job role and task.
If AI is to be a success, data governance is required.
With AI assistants relying on quality and timely content to provide reliable answers, data governance will be a success factor in the coming year. A common misconception is that you can direct an AI assistant against any pile of documents and get reliable results.
But the reality is that content integrated across sources needs to be cleaned and validated for AI to work effectively. We will therefore see more organizations using metadata in 2024 to achieve better data governance.
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