This question is often asked at the end of a seminar or webinar after we demonstrate how AI is automating certain tasks in the strategic sourcing cycle and that more tasks are scheduled for automation in 2018. So the short answer to the question is yes, jobs will be lost but a more complete answer is that new roles will be created that drive greater value. Nostalgia won’t play a part in business decisions that will always favour efficiency and speed. From the perspective of career path development, new technology waves should be surfed because businesses will always be trying to find ways to utilise new technologies more effectively than competitors. New roles will emerge with new opportunities for those willing to engage. So there is both a threat and an opportunity and being informed is a good initial move when change is afoot.
Category Managers should be aware that AI is set to automate tasks concerned with data organization, mechanism design and bidder management tasks in strategic sourcing. Understanding the imminent changes that are likely will allow people to be prepared for changes but also seek out opportunities that will present themselves in terms of managing more advanced technology. Some will inevitably fight the tide and argue there are still things that humans can do better. This will have some truth but that will decrease over time as the inevitable wave of automation of strategic sourcing takes hold. CPO’s need best practice to be executed in strategic sourcing and if AI is executing processes faster and to a higher standard then deployments will accelerate quickly. In short, if many activities in strategic sourcing can be automated with an attractive RoI, then it will be automated and the battle lines in terms of improving efficiency and performance will move to another frontier such as how greater intelligence is embedded in the software, better data is acquired to improve performance or how better innovation or supplier relationship management is conducted. Not everything can be automated, so those tasks will grow in importance.
Best practice in most categories is complex and the choice of bidding process and the execution to elicit clean noiseless data and optimally induce competitive tension is perfectly suited to automation via Intelligent Systems based upon a mixture of AI and non-AI technologies during a sourcing cycle. Often, there is a cohort in an audience that will greet such news with trepidation or even scepticism. But fears over jobs will not change reality, a wave of automation is heading the way of procurement and those enterprises that grasp it first will gain a competitive advantage.
The issue of the scale of job losses requires a more nuanced investigation as the nature of work will evolve as manual tasks are increasingly automated but new roles emerge that complement intelligent systems. We are seeing significant changes with RPA (robotic process automation) for back-office tasks such as payments processing being implemented by many firms. The primary incentive for such technology investments is simple headcount reduction and there are clearly job losses already occurring in Accounts Payable and Receivable functions. The adoption of RPA yields significant savings but is still just the tip of the iceberg when it comes to the transformational nature of truly intelligent systems or the wider Procurement function. Much more significant changes are afoot with the advent of sophisticated AI for performing strategic sourcing tasks.
Technological revolutions are not new so the best way to help us forecast the nature of future changes is to look at how industries such as Investment Banking rapidly evolved as trading became increasingly automated. The number of traders at investment banks decreased by 50-75% in most banks whereas the number of Quantitative Analysts increased. In many ways procurement is more complex than investment banking because stocks and bonds are simpler contracts with objective parameters. Risk Management in Invesment banking, however, has very sophisticated modelling requirements that rely on increasingly sophisticated financial instruments. The scientific techniques applied in finance tend to rely on mathematical modelling of precise quantities. In Procurement and Supply Chain, however, decision making has more complexity due to more subjective factors, noisier data, non-standard terms and conditions and thus relies on a broader family of technologies from Artificial Intelligence to model the environment and decision making.
If a Sourcing Bot specialising in a category such as Facilities Management, Steel or Transportation, is trained to execute sourcing processes that follow best practice automatically, then sourcing managers should be wary whilst also accepting that this can help raise standards. Category managers should be watching for opportunities that will spring up as AI needs guidance or assistance along the way. New roles will flourish that complement intelligent systems implementations and we anticipate that the following business needs that will increase in importance:
• data stream integrations to enrich the decision making environment. Such roles will involve a mix of business and technical skills to decide on key 3rd party or internal data sources that should complement decision making in strategic sourcing scenario analysis.
• smart contract integrations for managing exported data from sourcing events into contract lifecycle management systems so that automated discounting or penalties kick in based upon measurable metrics.
• risk management is overlooked in too many categories as personnel shortages mean it is impractical to monitor suppliers constantly. More time will be dedicated to hedging against certain risks in sourcing negotiations that inevitably require human input because risk assessments tend to involve humans taking a view on key risks for a buying organisation.
Ultimately, businesses need to stay ahead of their competition by making faster and better decisions. So the nature of work will change, jobs will be lost, other jobs will be created and society on average will be better off as there is more wealth to be shared as we become more efficient as a society at processing information and executing efficient trades. That’s the nature of progress, it’s up to politicians to help manage these changes so there is fair wealth redistribution that may become an increasing challenge as technological change accelerates.