My workfellow (Henri) and I spent quite a few years as business analysts and consultants, helping companies in their knowledge work challenges. We were identifying knowledge-work related problems and looking for ways to solve them with the help of technology.
The process was pretty straightforward. We would make prior research and gather and analyze all the data that was possible to get. Unfortunately, most of the time those resources were quite limited or non-existent. Well, how many units could honestly say that their process charts, work descriptions, and instructions are always up-to-date?
Even with or without pre-material, the next step was to talk to tens and tens of people from each team, trying to understand bottom-up, what kind of process-related problems they were facing on a daily basis in order to build a big picture. That overall process was really manual, labor-intensive and slow, and definitely not scalable.
Isn’t it paradoxical that with all the technological advancement, the search of hurdles from teams and business operations still depends on totally manual processes, such as frameworks, use cases and case studies?
Could there be a better possibility to FIND talent waste?
Three problems with manual search
It has become a standard nowadays that companies choose the solution first and only then do they find use cases to utilize it, rather than identifying the problems first. That is of course an easier way to manage and run as a program, but it might lead to three things: forgetting alternatives, missing most of the potential, and making things more complex.
What going technology-first means is that by selecting the solution first and then blindly following it, companies might stop considering other options at all because they are too busy finding the problems those very solutions could be used for. Alternative solutions, sometimes more effective and having a long-lasting effect, don’t even have a chance to be considered. Traditional toolkits of process development or IT, which could be better at solving some systematic problems, might be bypassed.
Future of the work becomes increasingly more complex: more communication, more IT systems, more processes. There will certainly not be a silver bullet solution to fix how processes, people, and systems would work in a perfect harmony. That’s why technology-first search will miss most of the potential.
As companies are increasingly adopting easy-to-implement capabilities, understanding exactly where to apply those becomes increasingly important. The worst-case scenario is that widely adopting those solutions might dilute enterprise architecture and embrace taking quick loans to meet short-term objectives.
- Selecting wrong problem to solve
The next issue that manual problem search might lead us to is solving the wrong problems.
When exploring the knowledge work processes in a particular team or unit and gathering the data on what could be improved, the viewpoint is typically quite limited.
As further data is mainly gathered through interviews/observations/some available statistics, there are many assumptions involved. When there are assumptions, narrow observation scope, and low-quality data → easy-to-spot problems are found quickly, but the ones with the biggest impact stay hidden.
It’s relatively easy to spot that team is doing a lot of manual text entry to fill data repetitively into the CRM system. That could be automated, one thinks. But hey, another team is doing this same entering as well, and the third one. So is that task only a reflection of a system problem? Maybe it's an actual master data problem? If that master data problem would be solved, it would erase all those tasks (reflections) once and for all.
With manual search it requires extra effort to try to understand what is the problem to be solved, and in the worst case, missing the root problem might lead to implementing solutions to practically unnecessary problems.
- Wasting people’s time
When doing manual discovery, the biggest problem is that not only does it take time from designated people doing discovery work, but also from professionals who are supposed to get help. Typically, discovery requires interviewing them and observing their work to understand what routines could be supported.
However, people should be involved only during the discussions about the possible verified opportunities, so that they don’t waste their precious time talking, explaining or showing how they do their work.
To wrap it up
Technology should inherently “simplify and ease” everything, right? Well, the process of “finding the problems” should also be “simple and easy”, and solutions applied — long-lasting and impactful.
Without the data-driven approach we can’t really see the big and small picture, alternatives and relations of work in the intersection between people, processes, and technologies. Doing only manual search might lead to solving random issues here and there, and not getting to the root causes of talent waste fixed for good.
Data-backed discovery of opportunities can clearly give the broader picture of where we are and what our options are. It can help to get rid of the talent waste and acquire speed, resilience, and agility so that the teams can effectively meet the business needs of tomorrow. But for that we need a tool, strong and effective one.