Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could artificial intelligence (AI) help companies meet growing expectations for environmental, social and governance (ESG) reporting? Certainly, over the past couple of years, ESG issues have soared in importance for corporate stakeholders, with increasing demands from investors, employees and customers. According to S&P Global, in 2022 corporate boards and government leaders "will face rising pressure to demonstrate that they are adequately equipped to understand and oversee ESG issues -- from climate change to human rights to social unrest."

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Could artificial intelligence (AI) help companies meet growing expectations for environmental, social and governance (ESG) reporting? Certainly, over the past couple of years, ESG issues have soared in importance for corporate stakeholders, with increasing demands from investors, employees and customers. According to S&P Global, in 2022 corporate boards and government leaders "will face rising pressure to demonstrate that they are adequately equipped to understand and oversee ESG issues -- from climate change to human rights to social unrest." ESG investing, in particular, has been a big part of this boom: Bloomberg Intelligence found that ESG assets are on track to exceed $50 trillion by 2025, representing more than a third of the projected $140.5 trillion in total global assets under management.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. While AI isn't everywhere yet, adoption is growing at a rapid pace, according to new research released today at IBM's annual Think conference. The 2022 Global AI Adoption Index analyzed responses from over 7500 IT leaders around the world. Key highlights of the report include that 35% of organizations are using AI today and an additional 42% are "exploring AI." AI use is helping to improve sustainability efforts as well as narrow important skills gaps, according to the report. "One finding that stood out to me is that more than half of organizations polled say they've accelerated their AI rollout over the last two years," Seth Dobrin, chief AI officer at IBM, told VentureBeat.

Now that AI is heading into the mainstream of IT architecture, the race is on to ensure that it remains secure when exposed to sources of data that are beyond the enterprise's control. From the data center to the cloud to the edge, AI will have to contend with a wide variety of vulnerabilities and an increasingly complex array of threats, nearly all of which will be driven by AI itself. Meanwhile, the stakes will be increasingly high, given that AI is likely to provide the backbone of our healthcare, transportation, finance, and other sectors that are crucial to support our modern way of life. So before organizations start to push AI into these distributed architectures too deeply, it might help to pause for a moment to ensure that it can be adequately protected. In a recent interview with VentureBeat, IBM chief AI officer Seth Dobrin noted that building trust and transparency into the entire AI data chain is crucial if the enterprise hopes to derive maximum value from its investment.

It takes a well-designed information architecture -- IA -- to ensure good AI. The challenge is getting both people and data on the same page when it comes to AI work. And there's much work to be done on both fronts. That's the word from Seth Dobrin, global chief AI officer at IBM. "Data is the food for AI, yet few organizations sit down at the table to design an AI strategy with a full accounting of where all their data resides and how organized it is," he says. "IT professionals are drawing from at least 20 data sources to inform their AI, and some have to draw from hundreds, so this is a big data infrastructure issue."

The last 12 months have seen many carefully laid technology plans blown off course, driving IT investments in unforeseen directions. COVID-19 has had a major impact on IT investment decisions. Its distorting effect has seen investment in some areas accelerate massively, while other plans have by necessity fallen by the wayside. So with recovery in sight, where do we go from here? What should enterprise IT plans look like, and how confident should CIOs feel about implementing them?

Collectively, they create a huge asset for any AI team--diversity of thought. Ultimately, though, success with AI comes back to the foundation: data strategy. "You could have the best, most diverse talent in the world," says Dobrin, "but if you don't have a good strategy and have that first part nailed, you're likely not going to be adding value."

Last year, IBM announced a Data Science Elite team whose only job is to help big enterprise companies push their first AI models into production. Now, more than a year after the program's launch, Rob Thomas, the IBM executive overseeing the AI SWAT team, reports that it has been a "huge success." The team has increased from 30 data scientists to 100, and there are plans to grow significantly next year. "We hire them wherever we can, actually," Thomas said, noting that these data scientists operate all over the world. Companies as diverse as Harley Davidson, Lufthansa, Experian, Sprint, Carrefour, and Siemens used the team for a necessary kickstart on AI projects. And the best part: It's all for free -- or at least there are no contractual obligations to pay.

As a business leader, finding a qualified data scientists is a critical step in your company's ability to harness big data and machine learning technologies, which is a competitive advantage. We reached out to data science leaders to get their thoughts on the matter. One of the most important steps to building a successful data science team is hiring a senior data scientist who can lead the further development of the data science team, says Seth Dobrin, who heads up IBM's Data Science Elite Team. "Until you get a credible senior person in your organization that's a data scientist, it's hard to get others to come on board," says Dobrin, a PhD with more than 20 years of experience in data science fields. "There are some clients that just can't find talent."

Plenty of IT shops have machine learning and AI projects underway, or they have plans to launch one. But many struggle to come up with their first use case, or to properly marshal the technical and human resources to ensure ROI. It is Seth Dobrin's job, as head of IBM's recently formed data science elite team, to help corporate users achieve those goals. Dobrin, officially vice president and chief data officer for IBM Analytics, discussed his team's experiences and observations with their first engagements comprised of more than 30 customers. What surprises you the most as you work with users on their first AI or machine learning projects?