Artificial Intelligence has the potential to transform business processes and deliver new strategic capabilities which will impact employees at all levels of the organization. The Covid-19 pandemic hasn't stalled that potential with McKinsey stating in their annual survey that 56% of respondents surveyed report AI adoption in at least one function.
This all sounds good on paper, but an essential element to remember when you see statistics like the above one, is that AI adoption is not a race. While many believe AI is a “big bang” project that will eliminate our immediate tasks, among those who have really harnessed AI, they have met it with a strategic and proactive mindset that is anchored on long-term objectives. The race to leverage AI is certainly not a sprint but rather a long hilly marathon with lots of moments where you “hit the wall” along the way.
The ACTUAL State of AI
If you believe all the articles out there, your perception might be that AI is here and doing insane things for companies. The reality is more nuanced. Here is our selection of articles to help you tell apart reality from over-exaggeration.
- Two scientists who have spent careers studying artificial intelligence, have curated the six questions to ask yourself when reading about AI. They also debunk two completely exaggerated headlines on Microsoft and conclude that you need to dig deeper when tech titans put out a press release.
- Greg Satell encourages a similar curiosity when you are faced with the question of whether robots will take our jobs. He insists on the fact that automation replaces tasks rather than jobs and how the real value is shifting from cognitive skills to social skills.
To get a realistic picture, take everything you read about dramatic progress in AI with a healthy dose of skepticism—and rejoice in your (for now) uniquely human ability to do so.
- Gary Marcus
- McKinsey's December 2021 survey is a strong overview of where we stand with AI today. The results show how companies seeing the biggest earnings use cloud structures much more than their peers do and there is a trend in the use of Machine Learning Ops (MLOps) by top performers.
In fact, MLOps is a best practice that is set to have a ripple effect across a large range of industries, and the resulting efficiency is already producing unprecedented ROI for companies. The reality is that there are incredible opportunities and advantages to AI, however, implementing AI is a risk, and implementing it correctly is hard.
AI in Action - Company Case Studies
- Scotia Bank (a commercial bank based in Canada) has proven the power of taking things slow when it comes to AI and how catching up fast, requires a pragmatic approach to AI. Their focus on operations and facilitating closer relationships between customers has enabled them to surpass competitors that started earlier with the technology.
- The focus on operations is also embedded in the U.S. Government Accountability Office (a legislative branch that provides auditing, evaluation, and investigative services) framework on how to build accountability into your AI. They emphasize the importance of understanding the entire AI lifecycle and also insist on continuous assessments and reviews of your system. Their main piece of advice? Think like an auditor.
- AI can also help you tap into new sources of data for analytics making it possible to automatically incorporate and process important context from a broad array of sources. AI will enrich the data and help even amateur data users use contextual information to surface more useful and interesting insights.
- Clinical Research Organizations (CRO) are tapping into new sources of data and adopting AI while dealing with enormous industry transitions. Parexel (a Planisware customer in the CRO space) is a great example of how AI and Data can be the solution to great change and their progress in four major areas of AI activity show how useful this technology is, in staying up with rapid industry transformations.
- This HBR article pinpoints how AI has shifted from a “nice to have” to a “must-have” in three areas: predictions, efficiencies, and real-time optimization. The common pain point here is boring, repetitive tasks and clearly, there is a big opportunity to move staff into more high-value areas and mitigate disruptions.
The idea of eliminating these lower-level routine tasks highlights some important questions on how humans will eventually work with Artificial intelligence. This brings the role of team leaders to the forefront and more specifically their ability to coordinate teams in what is known as the “new diversity” where humans and machines work side by side.
How to Manage AI within Teams
- The most striking example of humans working with machines is in chess where Gary Kasparov partnered with a PC running the chess software of his choice. His learnings, along with the recommendations of David De Cremer, form the conclusion that in the end, it's all about the process and how artificial intelligence combines with authentic intelligence (the human) to create the combination known as augmented intelligence.
- A report on Empowering AI Leadership by the World Economic Forum fleshes out how to integrate a machine into your team dynamics. Collaboration between different departments needs to be frictionless going forward in order to scale AI within small teams and eventually expand into the whole organization.
- After integrating this technology into teams, leaders also need to think about how to manage these AI Decision-Making Tools. Consumer demand for instant responses and real-time coordination make increased AI use within your organization an inevitability. To satisfy this demand picking the right management option for each of your AI systems will be essential.
Machines are often introduced as the new super employee and this is true to a certain extent. Their ability to eliminate repetitive tasks will be welcome news to many but this is not a “set it and forget it” system. AI is built on pillars of rigorous preparation and a clear strategy which will require commitment from every level of the organization.