Even in a simple development environment, machines and algorithms are still powered by human intelligence.

Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.

Ever since I can remember, artificial intelligence has been the holy grail. Films have portrayed it, from BladeRunner to the more recent Her. In the meantime, business leaders promised it would revolutionize the workplace. In both cases, we've been presented with scenarios in which AI transforms the daily grind. Indeed, AI has been talked about as a scientific discipline since 1956.

Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.

AI has now made it onto CEOs' agendas. While the topic certainly isn't new, CEOs have learned that the idea of AI is far simpler than its effective application. To get there, companies need to start with their business objectives and then use AI in ways that advance those objectives rather than just implementing AI for AI's sake and hoping it can add value later. Meanwhile, CEO attitudes about AI and machine learning or ML (a subset of AI techniques) have been changing as it relates to digital disruption. In the beginning, it was about understanding what digital disrupters do and how they do it.

Anyone working in data management and data science can attest to the challenge and time-consuming nature of mapping a set of data from a new source into a platform where it can be cleaned, validated, and ultimately analyzed and used to train algorithms. After all, your algorithms are only as good as the data used to train them. Now imagine if these data sets are coming from hundreds of external users who have employed any number of systems to collect this data, from Excel files to actual shoeboxes full of photos. That is the challenge that non-profit wildlife conservation machine learning and artificial intelligence service provider Wild Me has faced over its more than a decade of operation. The organization builds open software and AI for the conservation research community.

Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.

If you work for a big company, you know how frustrating it can be to keep track of all the different systems and applications you need to do your job. Where do you go for your expense reports, your performance review, your GDPR compliance training, your open enrollment? What if you haven't been to that particular application in several months and don't remember your password? Do you have to go to a different application or maybe open a service ticket with IT to reset your password for the first application? How many hours have you wasted navigating to the right places and figuring out again how they work?

As COVID-19 vaccination rates rise, conversations about the future of work are picking up again. It's no longer the workplace of 2019; the landscape has changed significantly since then. The automated, digitized world of work that we knew would arrive "soon" is suddenly here, and many of those changes are here to stay. Chief information officers and IT leaders have a key role to play in facilitating employee adoption and encouraging buy-in for the future of an AI-enabled workforce. Organizations accelerated their digital transformation plans over the past year, or improvised along the way, to accommodate the rapid shift to virtual work.

The Bureau of Labor Statistics lists jobs in data science in the top 15 fastest growing occupations with projected 31 percent job growth over the next 10 years. With data increasingly becoming the lifeblood of all organizations, data scientists need to be equipped not only with the right technical skills, but a robust dose of business acumen as well. In 2021, machine learning methods like transfer learning and transformers are drawing a lot of attention because they are rapidly driving innovation in a number of different spaces. For building and training neural networks, PyTorch has a lot of momentum behind it, and Keras and TensorFlow are also commonly used. There is also a rich ecosystem of software libraries, many open source, that can help accelerate machine learning and data science applications.