Executives seeking to embrace enterprise intelligence aim for enhanced agility compared to their rivals, fostering a culture that promotes ideation and innovation across all levels of the organization, while leveraging past experiences to propel the company forward.
According to research conducted by IDC on Big Data and Analytics, organizations expended over $250 billion on data analytics and AI software, hardware, and services in 2022. Despite this investment, many organizations still face challenges in developing essential capabilities related to enterprise intelligence. These include effectively synthesizing information, encouraging collective learning, delivering insights at scale, and fostering a data-driven culture.
The pursuit of enterprise intelligence represents a strategic shift for businesses aiming to remain competitive in an ever-evolving market. Executives recognize the importance of agility, as it enables organizations to swiftly respond to changing customer demands, market trends, and technological advancements. By prioritizing agility, companies can adjust their strategies, products, and services to meet evolving needs, gaining a crucial edge over competitors.
The emphasis on encouraging ideation and innovation throughout the organization signifies a shift away from traditional hierarchical structures. In an increasingly interconnected world, companies that empower employees at all levels to contribute ideas can tap into a wealth of collective knowledge, uncovering novel solutions to complex challenges and fueling growth.
However, despite the significant investment in data analytics and AI technologies, many organizations face difficulties in fully harnessing the potential of their data.
A recent global survey conducted by IDC, sponsored by Teradata, reveals that only one-third of organizations consider themselves experts in fully harnessing the value of their data. The modern data environment presents significant challenges due to the highly distributed, diverse, and dynamic nature of data. Nearly 70% of respondents stated that their data complexity has increased over the past two years, and approximately 85% expect it to continue growing or remain constant in the next two years. In fact, around 20% of respondents believe that data complexity has significantly escalated in the last two years and will continue to do so at the same pace in the following two years. This raises the question of how organizations plan to manage this rising complexity, extract value from data, and bolster their enterprise intelligence.
To address this challenge, many leaders are turning to generative AI, viewing it as a potential catalyst for innovation and disruption in the technology landscape. Generative AI has quickly captured widespread attention and imagination, revolutionizing content creation, data analysis interpretation, and coding processes.
Despite the hype surrounding generative AI, organizations must prepare for the challenges that come with its implementation. Proven use cases with strong business outcomes will eventually emerge as the technology matures, leading to innovation and adaptation across industries. While generative AI shows immense promise, it may not be a one-size-fits-all solution for every enterprise challenge. Careful consideration and gradual integration will be crucial to harness its full potential and drive sustainable growth and transformation.
While 75% of respondents felt well-prepared to leverage technologies like large language models, only 27% had fully operationalized previous AI initiatives, and merely 30% claimed maximum expertise in advanced analytics, data science and machine learning. Despite recognizing the value of AI, with 80% considering their organization's investments sufficient, there is a significant skills gap, as 58% anticipate shortages in generative AI-related skills.
There is also the pressure from the market and boards that is mounting on executives to invest in generative AI, with almost 95% feeling at least some pressure to implement it within the next six to 12 months.
As generative AI promises to revolutionize computing and streamline automation and enterprise intelligence use cases, organizations must be cautious. Ensuring the proper guardrails are in place before launching generative AI or any AI initiatives becomes paramount, especially considering the current low data literacy levels among key executives such as CFOs and CEOs.
When it comes to adopting generative AI, organizations have undergone significant changes and adaptations, with three major factors being highlighted: a greater emphasis on ESG initiatives, economic and geopolitical challenges and a transformation in the hybrid workforce.
The level of enterprise intelligence varies among organizations based on their capabilities in information synthesis, collective learning, insights delivery, and data culture. Approximately 50% of organizations reported performing above average in extracting value from data, and 36% claimed their C-suite was well-prepared to navigate market realities and economic uncertainties. These high-performing organizations foster open information flow, breaking down silos across business groups and organizational levels, with 18% citing highly unconstrained information flow and 36% indicating very unconstrained information flow.
IDC's research revealed that organizations with the highest levels of enterprise intelligence achieve three to four times better business outcomes than their peers.
Organizations that possess a well-defined enterprise intelligence strategy aligned with their business goals exhibit the ability to identify growth opportunities and innovations even in challenging market conditions. Such organizations have a competitive advantage by leveraging disruptive technologies like generative AI. By staying ahead in enterprise intelligence, they can differentiate themselves and maintain resilience and adaptability in an ever-changing business landscape.
Edited by
Alex Passett
