AI and Data This Year: Bigger, Bolder, and Business-Focused

When The Matrix premiered 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 edition has been released in a world where the perception and adoption of artificial intelligence and data has fundamentally changed.

In 2022, AI and data are no longer “nice” or impractical ambitions. For years, organizations have accepted the realization that each of these capabilities is essential to gaining an advantage and growing – and the evidence is clear. Consumers are increasingly comfortable and confident with AI-enabled interactions, companies are pushing across common barriers to scale their AI programs, and companies are shifting from gut feeling and intuition to relying directly on data-driven decision-making.

With so many high priority use cases creating key opportunities for adoption, here are four AI and data trends I expect to see taking shape this year:

1. Collaborative data ecosystems will be a top priority for businesses

In 2022, it is critical for companies to go beyond extracting insights from the data that is generated within their organizations. The main differentiating factor can come from cooperation with partners and suppliers. Research published in 2021 shows that organizations that get additional insights from the data that belongs to the companies in their ecosystems have twice the market value. This data sharing can also lead to organizations partnering on new products, services, and experiences — and in 2022, we’ll see companies taking on new initiatives they couldn’t have built themselves.

2. Data conversion is not the end, it’s everything

Data is important in and of itself, but in 2022, the focus will be on leveraging data to solve business problems. The time for proof-of-concept is over – As data and AI become larger, more strategic, and more mission-critical engagements, companies need to design their roadmaps to support overarching business goals, with a particular focus 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 – with only 16% of organizations currently mastering both data and AI at scale. Reaching the next level of business-focused transformation in 2022 requires business leaders, including business process managers, to engage more personally in data, analytics, artificial intelligence, and data governance programs, which are still lacking in most organizations. Deconstructing these businesses and IT silos may seem like a leap, but companies must connect them more closely to maximize the potential benefits from each.

3. AI will enable every supply chain to be efficient

The disruption caused by the pandemic has required companies in nearly all industries to address challenges in supply chains and prioritize resilience. To achieve this, supply chains need to enable AI in all areas of operations and take advantage of the data ecosystems that are being built through the collaboration of partners. Historical data, along with current 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 re-engineered, enabling AI, and most importantly: future-proofing.

4. Relentless focus on “everything is a talent”

The fields of artificial intelligence and data are constantly evolving – and one of the key outcomes is the ever-changing talent market. In 2022, organizations looking for talent in AI and data need to invest in world-class recruitment and retention initiatives to combat great resignation, foster inclusion and a culture of learning and lifelong inner growth. In addition to their day-to-day roles, employees working in these areas are looking for opportunities to work on purposeful and rewarding projects in areas such as environmental sustainability — and companies need to ensure they create pathways for their AI and data talent to acquire. expertise. This is especially true for industry-specific organizations, which may face increasing competition from large technology-focused companies when seeking to recruit and retain team members using AI and data skills.

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