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When “The Matrix” movie first aired in 1999, it showed us an extreme version of artificial intelligence and the power of data that in many ways seemed impossible. More than 20 years later, the latest installment has been released into a world where the perception and adoption of AI and data has drastically changed.
In 2022, AI and data are no longer “nice-to-haves” or unworkable ambitions. For years, organizations have accepted the realization that each of these capabilities is essential to gain an advantage and grow -- and the proof is clear. Consumers are increasingly comfortable and confident with AI-enabled interactions, enterprises are pushing through common barriers to scale their AI programs, and companies are shifting away from gut-feel and intuition to relying squarely on data-driven decision making.
With several high priority use cases creating prime opportunities for adoption, here are four AI and data trends I expect to see take shape this year:
In 2022, it is critical that companies move beyond merely extracting insights from the data generated within their own organizations. A key differentiator can come from collaborating with partners and suppliers. Research published in 2021 shows that organizations that capture additional insights from data belonging to companies in their ecosystems have twice the market capitalization. Such data sharing can also lead organizations to partner on new products, services, and experiences -- and in 2022, we’re going to see companies to tackle new initiatives they couldn’t otherwise build by themselves.
Data is in and of itself important, but in 2022, the focus will be on leveraging data to solve business problems. The time for proofs of concept is over -- as data and AI engagements are becoming bigger, more strategic, and more mission-critical, companies need to tailor their roadmaps to support the overarching business goals, with a particular emphasis on gaining value from data and AI. Organizations have a lot of untapped ROI to exploit in this area this year and in the years to come -- as only 16% of organizations are currently mastering both data and AI at scale. Getting to the next level of business-focused transformation in 2022 requires business leaders, including CXOs, to get more personally involved in data, analytics, AI, and data governance programs, which is still not the case in most organizations. Breaking down these business and IT silos may seem like a leap, but companies must more closely link them to maximize their potential benefits from each.
Disruption caused by the pandemic has required enterprises across nearly all industries to address challenges with supply chains and prioritize resilience. To achieve this, supply chains need to be AI-enabled in all process areas and take advantage of the data ecosystems being built through partner collaboration. Historical data, along with existing supply chain planning approaches and models, will be less relevant in 2022 due to changes in consumer demand and purchasing patterns over recent years. From supply planning and demand planning, to raw material sourcing and digital manufacturing, supply chains in 2022 need to be reengineered, AI-enabled, and most importantly: future-proofed.
The AI and data landscapes are constantly evolving -- and one of the major consequences is a continuously changing talent market. In 2022, organizations seeking AI and data talent need to invest in world-class recruiting and retention-related initiatives to combat the Great Resignation, promoting inclusivity and a life-long culture of learning and internal growth. In addition to their day-to-day roles, employees working in these fields are seeking opportunities to work on purposeful, rewarding projects in areas like environmental sustainability -- and companies need to ensure they create paths for their AI and data talent to get these experiences. This is especially true for industry-specific organizations, that may face increased competition from larger tech-focused companies when looking to recruit and retain team members with AI and data skillsets.
轉貼自： InformationWeek
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