The universal business requirement to work smarter, more productively, and securely has meant the knowledge workers' greatest ally in getting work done is emerging as a combined relationship between new knowledge worker approaches to curation, aided by Artificial Intelligence (AI).
In doing so, this collaborative partnership has shifted the naive conversation from 'Robots taking over the world and ending gainful employment' to starting to demonstrate a return on investment and an apparent competitive edge in delivering actual business outcomes.
The knowledge worker is still in control; AI is an accelerant and helping hand, not an outright replacement. Did lawyers lose their jobs when Word Perfect came along? Or did accountants get less work to complete per day when Excel became their tool of choice? For better, not worse, shift happens.
Our Making Knowledge WorkTM report defines five aspects of knowledge work, and it is clear where AI can play a leading role in ensuring these factors are delivered upon with the utmost diligence:
- What information is relevant to a situation?
- How should relevant information be applied?
- What could the unintended consequences be?
- What risks are involved?
- Who are the other key stakeholders?
Organizations are capturing knowledge, but they are not putting it to work consistently. Consequently, they are missing out on the competitive edge that knowledge could provide in collaboration with intelligent search driven by AI.
Moreover, AI is keeping the knowledge of the highly-skilled worker and converting it into institutional knowledge, building corporate muscle memory, giving life to what would otherwise have become lost knowledge. To understand this shift and maturity of AI applications in knowledge work, we can break it down into four core elements of benefit.
Quite simply, AI does the heavy-lifting, seamlessly and rapidly undertaking the drudge work, in turn unlocking more tantalizing and stimulating work. Doing this gets the highly skilled knowledge worker delivering business outcomes against the extracted knowledge, not preparing that knowledge itself. Acting upon the knowledge commands higher fees.
Most professional service organizations have an imperative to be more efficient. So the ability for machines to allow certain parts of the work to be done more efficiently, whether that's something really 'boring' by automating a document, or it's going to reduce the human time spent pulling data into a spreadsheet to analyze data.
If AI can achieve these tasks more efficiently, that makes things either more profitable or offers new fee structures or cheaper work or helps firms stave off competitors. It also goes a long way in retaining employees by removing the drudge work and enabling more projects of interest.
AI can provide more insights into how your business works and what your business is doing. Uncovering and organizing data affords a structured approach to data-driven decision-making so that you can drive more work in your existing work base.
By way of example, a machine derived outcome tells you X number of people similar to you have delivered upon similar matters in your firm. That's a business development opportunity for you to go to another X number of clients you would never go to before offering that sweet-spot work again.
In collaboration with your knowledge, AI helps you gain clarity and enables better decision-making, driving your business in the direction it needs to achieve optimal success, growth itself, or more of the same type of work that plays to your strengths.
In our Making Knowledge Work study, fewer than 40% of respondents said their department uses Document Automation Technologies when working with extremely or very important digital documents or files. If knowledge is being left behind at a departmental level, think of the missed opportunities at an institutional level.
In a world of ever-increasing volumes of data, AI is essential in keeping on top of the data which matters most to your organization. And the data that matters most might be institutionally nearer than you think, if not geographically.
Consider a multi-region business with collective knowledge in London, Sydney and New York. One office alone has excellent institutional knowledge, experience and the context of the two. That same named business located in Sydney or New York will also have their own and likely different approaches to organizing knowledge, applying knowledge, and billing for knowledge work. Within a centralized and digitized system, AI can rapidly identify patterns and opportunity that will unlock more business, at scale, across the global offering.
Those serendipitous watercooler corridor conversation moments are on hold with remote working methods. Irrespective of a devastating global pandemic, they never happened in multi-region businesses between one office and another. Let AI activate your knowledge and give structure and scale to serendipity.
As part of there being, metaphorically speaking, "more data on the table", there is inherently more risk.
In professional services organizations with diverse skillsets, offerings and ways of working, disparate and vast datasets will inevitably be in play.
Ensuring your data is structured and open to the possibilities of AI organizing knowledge into more meaningful and manageable datasets, it can crunch through contracts and spot risks that need the attention and resolution skills of the knowledge worker. In turn, identifying what might have been a ticking-time-bomb but also more new work for your business.
Organizations that run on knowledge need to activate it and act upon it any time or place where work is happening. To stay ahead, a collaborative approach between knowledge workers and technological systems such as AI is business-critical in unlocking a better way of making knowledge work smarter, more productive and securely.