- Enterprises adopting AI are 81% more likely to have advanced data management capability and 73% more likely to report mature cloud capabilities.
- The majority of enterprises responding to MIT’s global survey have initiated AI projects, reporting that they are in the planning stages or have already piloted or implemented AI technologies.
- When CEOs and senior management teams get involved in defining, piloting, and guiding new AI use cases, AI pilot yield rates jump and more move into production.
- Implementing AI successfully requires CIOs to prioritize cloud/data centers, data management, software development, and cybersecurity over the many other projects that compete for their time and resources.
These and many other insights are from MIT Sloan Management Review’s recent study completed in collaboration with SAS, How AI Changes the Rules: New Imperatives for the Intelligent Organization. A copy of the survey can be downloaded here (PDF, 24 pp., no opt-in). It’s a quick, interesting read that provides examples from enterprises actively adopting AI today, sharing their lessons learned. The methodology is based on a global online survey completed during June and July 2019, interviewing 2,280 survey respondents from MIT Sloan Management Review readers. 80% of respondents hold C-suite, board, or management roles distributed across 110 countries. 37% of respondents are from North America, 22% from Asia, and 20% from Europe. MIT Sloan Management followed up with interviews with analytics experts, including practitioners, consultants, and academics. These individuals provided insight into how the drive to implement AI is changing organizational culture, technology strategy, and technology governance.
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The following are the key insights from the report:
- Enterprise’s enthusiasm for AI is growing, with 62% increasing their spending last year. The majority of enterprises have initiated AI projects and are progressing from planning AI adoption, piloting AI projects, and implementing AI in their operating environments. The MIT study team found those succeeding with AI are differentiated from their peers by how their leadership teams prioritize and commit to planning, pilot, and production-level results while balancing risk and being mindful of the ethical trade-offs AI presents.
- 63% of enterprises expect a significant change in their organizational performance based on their AI investments and initiatives. C-level business and tech executives interviewed by MIT have high expectations for AI making substantial contributions to their business. Their expectations are the highest for increasing the collaboration between functions (75%), expect more cross-training across disciplines (70%), and enabling more organizing people into multidisciplinary teams (66%), all orchestrated around making AI initiatives succeed at scale across their organizations.
- Enterprises who excel with their AI initiatives have exceptional data management and more mature cloud capabilities than their peers by a wide margin – and have a passion for keeping their teams trained on AI technologies. Enterprises getting results from their AI strategies and successfully progressing pilots into production are 81% more likely than their peers to have advanced data management capabilities. They’re also 73% more likely to have mature cloud capabilities. It’s affirming to see those enterprises who deeply value education, training, and skills development pulling away from their peers as they know it pays to train their teams early and often. They’re 46% more likely to excel at AI initiatives than their peers because they place such a high value on training their teams and keeping them current.
- Having mature cloud capabilities and plans for increased spending on Cloud Services further differentiate enterprises that excel at transitioning AI pilots into production. 82% of enterprises are increasing their spending on Cloud Services this year, also indicating this is a foundational technology for improving AI and machine learning outcomes. The use of the Internet of Things (IoT) systems, sensors and platforms for providing real-time data to develop machine learning algorithm accuracy further is a priority for 49% of the enterprises participating in the study.
- There continues to be a wide gap between enterprise leaders’ enthusiasm for AI and how many AI pilots are progressing into operations across their organizations. While 42% of enterprises have active AI engagements across their organizations, only 5% have implemented AI widely across their organizations. Nearly one out of every five (19%) are piloting AI projects today, and almost a third (27%) are investigating and researching how AI adoption can help their organizations.
- Trust in AI remains guarded despite the majority of enterprise execs seeing the potential of its many business benefits enterprise-wide. The MIT researchers created a baseline for trust in AI by asking respondents to rate just how reliable they find the results and recommendations of AI-based systems in two different contexts. The first is in their personal lives, as consumers, and the second is at work, interacting with their organizations’ AI-based systems. On a scale of 1 to 10, with 10 being most reliable, respondents’ overall rating for personal AI technology was 7, while AI used in their organizations earned a 6.
- Succeeding with AI initiatives that progress from pilot to production requires general management and tech leaders to be all-in on every phase while factoring in risks and ethics. Enterprises who are getting results from their AI strategies share three common characteristics across their senior management and tech leadership teams. AI requires more significant organizational change than other technologies, placing new demands on CIOs and CTOs, and requiring all leaders to focus more on risk management and ethics. AI requires both quality data and ongoing support to improve the efficacy of its results and to achieve strong ROI. AI also requires a long-term mindset that can be difficult to square with the pressures CEOs face, says Ray Wang, principal analyst, founder, and chairman at Constellation Research. “AI requires a long-term philosophy. You have to understand that you’re going to make this long-term investment with an exponential payoff toward the end. Leaders often don’t understand how to do that, so they keep making short-term decisions for earnings per share instead of thinking about the long-term health of the company,” he says. “Companies that are going to succeed in AI are the ones that have a long-term view, that understand that this is a significant competitive edge, and this is a long-term investment. Everything else is just lipstick on a pig.”