Did you know that just a few years ago, Machine Learning (ML) was virtually unheard of outside the geek press? It seems crazy considering, that the term is now blasted past cutting-edge to the top of the strategic agenda. Today, decisions and the different steps leading up to them are constantly fluctuating based on real-time availability, data, and algorithms.
The underlying information for these knowledge workers is more readily available, from data points that inform these processes, which all works in conjunction with Artificial Intelligence (AI) to automate human decision-making.
The new data available creates shortcuts that drive digital (process) transformation, which creates new value. AI will help answer data questions. AI is not “one” technology, it’s process knowledge, or a combination of different techniques, technologies, tools and sets of training data.
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Therefore, when AI utilizes ML, software does not need to be continually updated or reprogrammed because ML works in tangent with AI to apply changes based upon the continual learning gathered from updated data points. This exhibits that data changes everything, especially automation.
Many have made the strong predictions that in the next three years, a fair portion (say 50%) of business processes will be fully automated. When AI is powered by data science and ML techniques, it can be used to have a machine make decisions or provide strong recommendations for actions.
Once existing processes are automated, new processes will be created as we move from a process-driven world to a data-driven world. Based on the insights from data, businesses will create new processes, reshuffle existing processes and digitize the process experience.
An example of this is streaming television and movies. Not too long ago, one would have to browse through the streaming service to decide what they’d like to watch. Today, top picks based on previous shows watched appear at the top, with a degree of match. With the help of ML and AI, watching television became easier and more efficient for all the Netflix and Chill folks.
The question becomes, “Where does one start with machine learning, and does it pay off?”
Fast Adopters Edge Ahead
Recently, SAP and the Economist Intelligence Unit released a first-of-its-kind report on ML, Making the Most of Machine Learning: 5 Lessons from Fast Learners, citing that 68% of companies surveyed said they are using machine learning in some form; among procurement companies, it is about 65%.
The good news is ML is leading to revenue and profits within companies, providing a competitive edge and early adopters, whom the authors dub “fast learners,” are realizing better business outcomes:
- 48% cite increased profitability (6% revenue growth) as the top benefit gained from ML
- 36% are implementing ML into customer-facing and product development functions, such as contact center, marketing, data processing and analytics, and R&D
- 41% say ML translates into higher levels of customer satisfaction
It is evident that ML is having an impact on early adopters, and those on board recognize the importance of the technology.
What Is Needed for Success
Machine Learning requires a great deal of high-quality data. For most organizations, this data is within existing business applications such as finance, logistics and sales. The data in these systems has already been collected, cleansed and stored over a long period of time, meaning they are available to create meaningful, useful predictive models.
When there is a tightly defined decision to be made and the decision can be seamlessly automated as part of an existing business process, ML works best. If the machine can run thousands of times a day, using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm, the greatest results can be made.
Basic Ways Companies Are Implementing AI and ML Today
- Extracting relevant payment or order data from unstructured invoices, forms, or emails (such as product names, amount, currency, payee, address, etc.)
- Classifying transactions for tax compliance
- Predicting when contracts, based on usage, will need to be renewed
- Predicting and acting on stock-in-transit delays
- Calculating the optimal length of time between physical inventories to ensure that it’s in line with automated systems
- Routing customer service requests to the most appropriate teams
- Comparing new regulatory documents with process or product descriptions, classifying and highlighting the nature, changes, and impact
- Redlining, or comparing two or more contracts with each other, and identifying contrasting or conflicting terms and conditions
Fast Learners Vs Strategic Clarity?
Looking into the research behind the ML report, it is interesting to see the gaps between the fast learners and everyone else. One striking difference is a 10-point variance linked around “lack of clarity on strategy.” Those that display a higher level of strategic clarity seem also to be better informed and have more realistic expectations on the possibilities and limitations of technology.
When trying to understand the fast learners, it is evident that they realized early that ML works best where there’s a tightly defined decision to be made thousands of times a day using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm.
A common misbelief about AI is that it will automate the economy and remove jobs. Instead, AI augmentation will free up capacity for employees to actually be more human within service processes and use their talents to create creative value. Most calamitous warnings of job losses confuse AI with automation, and this overshadows the greatest AI benefit – AI augmentation, a combination of human and AI where both complement each other.
Fast learners are retraining employees to focus more on higher-value tasks within their organization when their work tasks are displaced by machine learning. AI is at its best when a decision can be seamlessly automated or augmented to support an existing business process, rather than a moonshot requiring net new processes or radical cultural change.
All in all, AI helps humans to be more human and AI returns humanity back to the business. While ML doesn’t do a human’s job, humans have to decide when to tell ML to do the work. The human component of ML and the importance of creativity when ML is in play is how the two seamlessly work together.
In other words, let the robots process and the humans think! Retrain employees who have tasks displaced by ML to learn higher-value tasks within their organization.
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