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In ten years’ time, the procurement profession and the role of the Chief Procurement Officer (CPO) has changed in many unique and profound ways. Yet, just like 2010, CPOs and other procurement leaders are entering the new decade grappling with intense challenges (some old, some new) and driving forward to achieve aggressive goals and objectives.
On that front, I’m pleased to continue with our exciting, new series on CPO Rising – “20 for 2020” which examines a broad range of CPO-driven topics. Today we continue with 20 for 2020: Key Themes for the Modern CPO’s Agenda (#18 – Artificial Intelligence), which is designed to help procurement set their organizations’ course for the critical months and years ahead. Enjoy!
20-for-2020: Theme #18 for the Modern CPO’s Agenda: Artificial Intelligence
Artificial intelligence (AI) is one of the most disruptive technological innovations to hit the business community since perhaps the introduction of the commercial internet in the early 1990s. More than process automation, business intelligence and advanced analytics, and robotic process automation (RPA) tools, AI has a rich legacy as both an academic discipline and a continuously-evolving set of autonomous and self-learning operating systems whose development spans the better part of a century. It is simultaneously one of the most widely lauded, feared, and misunderstood phenomena of the contemporary business environment, representing significant opportunity for many knowledge workers and significant peril for many others.
Understanding AI
Artificial intelligence can be classified into two basic sub categories: 1.) narrow, or weak AI, and 2.) artificial general intelligence (AGI), or strong AI.
Narrow/Weak AI: As the name implies, narrow/weak AI is fairly limited in scope and focuses on just one task or problem set. Some forms of narrow/weak AI employ deterministic- or search-based algorithms to isolate and retrieve information and present it to users. Other forms of narrow/weak AI employ machine-learning algorithms to scan large data sets to find hidden patterns, learn and adjust to user behaviors, and offer predictive insights based on historical data analysis.
Examples of narrow/weak AI include chat bots, natural language processing, optical character recognition, and RPA. They are currently employed in many business applications and platforms to reduce the number of steps or touch points needed to execute tasks; to rapidly and repeatedly scan large batches of data, documents, or images; and to respond to basic (and predictable) queries in a conversational manner. As such, narrow/weak AI is generally considered not to be a threat to most knowledge workers, as it augments and generally improves the user’s experience rather than replacing them.
Artificial General Intelligence: AGI is where the rubber hits the road for business technology. It has been in the making for more than half a century, although technology evangelists perennially declare that AGI will be operational in five years. Artificial general intelligence employs deep learning and neural networks that seek to replicate the human brain and give technologies the ability to learn, problem solve, make decisions, predict outcomes with a high degree of accuracy, and execute tasks independently. Some solutions providers have gone to market with early deep-learning and neural networks integrated into their products, although it remains to be seen how well they perform in practice.
Final Thoughts
Presently, narrow/weak AI technologies rely on initial programming and human interfacing, whereas truly intelligent systems – still largely theoretical – should be able to mimic human-driven business processes while functioning independently of the human hand. They should also be able to generalize knowledge acquired in one domain and apply it to others. Using historical data and past experiences, AGI systems should be able to predict outcomes with a high degree of accuracy, incorporating real-time data to adapt to their environment. It is here where AGI has profound potential to augment and in some cases replace knowledge workers.
For the time being, consider the facts as we understand them: AGI is perennially five years away from realization, and there is a widespread desire for AGI to make knowledge workers better — not obsolete. We can and absolutely should be able to control our own destiny with intelligent systems, especially as they become more intelligent, independent, self-learning, and self-actuating. Our self-obsolescence is not pre-ordained; and it doesn’t have to be.
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