How to be part of the AI digital transformation
The business mandate to be innovative and follow the trends increases the risk of spending money rather than investing them, if we don’t consider these three questions;
- Have we defined the problem and the desired goals thoroughly?
- Do we have a deep understanding of the AI offerings and their level of sophistication to identify the most relevant solution?
- Have we got the process expertise to integrate the solutions in a manner that will generate the desired benefits?
Frequently, a small number of process experts are involved in testing and dry-runs, while most of us remain observers of these process enhancements. At the end, we only get the use training when new systems and tools are implemented.
To become part of the AI digital transformation, we need to develop tech acumen and agility. Building this knowledge, we get ourselves involved in the design and tailoring of the solution and ensure it will generate the anticipated benefits. In addition, our professional profile is advancing through the insights and experience gained so we remain competitive in the changing and demanding employment market.
Developing tech acumen and agility is not a knowledge acquired in a training classroom. It is rather tacit, implicit knowledge which results into a mind shift. We can start by doing three things:
- Understand technology in a way that we can teach others. Reading few articles and learning buzzwords is scratching the surface. Gaining an understanding of how the AI works, what types of AI applications there are, and which offering best meets our requirements, is needed to introduce relevant solutions. For example, understanding the difference between digitalisation – digitalisation refers to enabling, improving or transforming business process by leveraging digital technologies (e.g., APIs) and digitized data- and digital transformation – digital transformation is the profound transformation of business activities, competencies, and business models to fully leverage the opportunities of digital technologies- is essential to research the market and to find the best solution for the problem we want to solve. “API Products and API Solutions” is a great article to learn more about this. An AI tool can provide automation, replacing some time-consuming tasks, like diary planning (i.e. ai), or it can analyse meetings at work and report on the demonstrated Emotional and Social Intelligence of the attendees and improve how effective a team meeting is (i.e. WinningMinds.ai). These two different propositions can be leveraged by different audiences and will generate different benefits for your business.
- Learn basic process engineering skills. Most AI offerings are not standalone, they have been developed to streamline a process, to make it faster, easier or more effective. The return on investment from an AI solution depends; on whether we have a deep understanding of the problem that we want to solve, on our relevant organisational capability (process, data, people) and mostly on how effectively it has been integrated into the relevant ways of working. To streamline a process requires expertise, understanding of the metrics, its linkages and interdependencies. Having this expertise and performance-driven mindset we can get involved, describe the problem and have a saying on the best way to incorporate any AI tool into the process in such a way to make it lean and productive.
- Be ready to take the risk – to ask for feedback having a “trial and error” mindset. Innovation rarely comes with a guarantee, if we want to learn, we have to try. Novel solutions sometimes work perfectly, other times less well, and that is nobody’s fault. We are expected to work thoroughly on many things and on many angles at the same time, to present a meaningful business proposition, not just a “quick and dirty” solution. Getting our ideas and suggestions together and make educated proposals to our leaders will get us the required endorsement. Shifting our mindset to growth rather than perfection to perceive any outcome as a learning asset rather than failure, is also important. This comprehensive article “The Pragmatist’s Guide to Applying Machine Learning in the Enterprise” will provide some more guidance on how to incorporate an AI solution to our processes, and how to iterate the solution and gain feedback.
To make a difference and add value to a company it is expected to demonstrate initiative and commitment in developing our capabilities, in conducting research, in being comfortable to unlearn and re-learn.