We love a good survey. While the tech industry is always pushing its customers towards the latest technologies, those same IT organizations are often busy struggling with more fundamental issues, deferring big changes. Some technology transitions, however, are impossible to ignore, like the rapid emergence of generative AI. So, are IT Organizations Ready for the GenAI Revolution? Let’s Ask.
Over the past few months, we’ve seen surveys published by tech companies across the spectrum that show us how gen AI is forcing IT organizations to assess their readiness for its adoption and deployment. The surveys offer a comprehensive view of the industry’s current stance, showing enthusiasm balanced by caution around organizational and infrastructure challenges.
Let’s examine recent surveys from the tech industry itself to see what they say about IT’s readiness to tackle the challenges of generative AI.
Enterprise Readiness
Before looking at the readiness of IT organizations to implement an enterprise’s AI strategy, we first need to understand how enterprises as a whole are approaching the topic.
A recent McKinsey survey shows a significant increase in AI adoption, with 72% of surveyed organizations utilizing AI technologies, up from about 50% in previous years. This surge is largely driven by the potential of generative AI to enhance productivity and innovation.
Similarly, Deloitte‘s “State of Generative AI in the Enterprise” report reveals that three-quarters of respondents expect generative AI to transform their organizations within three years.
Despite the enthusiasm, many organizations need help scaling AI initiatives. Boston Consulting Group‘s research shows that 74% of companies struggle to achieve and scale value from their AI efforts. This difficulty often stems from a need for clearer strategies and insufficient investment in necessary infrastructure.
At the same time, the rapid advancement of genAI is also reshaping enterprise operations, yet many organizations need to prepare their IT infrastructures to support these sophisticated technologies. Recent studies illuminate the challenges and necessary steps for building AI-ready infrastructures.
IT Organizational Readiness
Integrating GenAI into enterprise operations is the focus of IT leaders looking to enhance innovation and maintain competitiveness. Recent studies, however, show a significant gap between the enthusiasm for GenAI and the readiness to implement it effectively.
Strategic Alignment and Leadership
According to IBM‘s 2024 Technology Leader Study, a significant number of tech leaders are grappling with blind spots that hinder effective AI integration. The study emphasizes the necessity for clear AI strategies and strong leadership to navigate the complexities of GenAI deployment.
Cisco‘s AI Readiness Index agrees. The survey assesses organizations across six pillars: Strategy, Infrastructure, Data, Governance, Talent, and Culture. The 2024 report indicates a decline in readiness across multiple areas, with less than one in seven companies ranked as Pacesetters. Despite significant investments, many organizations have yet to realize expected gains, highlighting the need for a comprehensive approach to AI integration.
The report reveals that only 13% of companies worldwide are fully prepared to leverage AI technologies, marking a slight decline from the previous year. This highlights the critical need for robust and scalable infrastructure to support the computational demands of GenAI applications.
Talent and Skills Deficit
The need for more skilled professionals is a considerable barrier to GenAI adoption. Dataiku‘s survey of senior IT leaders shows that while 73% plan to invest over $500,000 in GenAI projects within the next year, only 20% have deployed GenAI in production. Challenges include data quality issues, governance concerns, and a need for more specific tools for managing large language models.
Workforce Faces Fear & Uncertainty
Beyond a skills gap, there’s also the question of how the workforce feels about the new technology – a technology that many believe may replace their jobs. We rely on these workers to implement the enterprise’s AI strategy, yet how motivated will be if they don’t believe it will benefit them?
This question is raised in Slack‘s Fall 2024 Workforce Index, which indicates a cooling of AI hype, with employees expressing concerns about increased workloads and job security. The sentiment expressed in the results suggests that fostering a culture of innovation and providing adequate training are essential elements in encouraging AI adoption among staff.
Cultural and Strategic Misalignment
IBM‘s “AI in Action” report emphasizes that successful AI integration requires more than technological readiness; it demands a cultural shift and strategic alignment across the organization. Leaders in AI adoption are distinguished by their comprehensive strategies, comprehensive data management practices, and the ability to customize AI applications to meet specific business goals.
Strategic Importance vs. Operational Preparedness
A report by Hitachi Vantara indicates that while 97% of enterprises rank GenAI among their top five priorities, only one-third believe their infrastructure and data ecosystems are adequately prepared for its deployment. This disparity illustrates a critical challenge in aligning strategic objectives with operational capabilities.
Infrastructure and Data Ecosystem Challenges
The Hitachi Vantara report also reveals that over 60% of organizations acknowledge significant gaps in AI readiness, particularly concerning infrastructure and data ecosystems. Nearly 75% agree that their current infrastructure requires modernization to support GenAI initiatives.
Data Infrastructure and Management Challenges
The “State of Data AI 2024” report by Hakkoda highlights that 74% of organizations plan to implement centralized cloud platforms to enhance data management for AI initiatives. However, discrepancies between executive confidence and operational readiness show that organizational dissonance may significantly threaten GenAI’s success.
In its annual “State of Unstructured Data Management” survey, Komprise uncovers that 70% of enterprises are still in the experimental phase with AI, with “preparing for AI” identified as a top data storage and management priority. However, cost optimization remains a higher concern, with only 30% of organizations planning to increase IT budgets to support AI projects. This reinforces the need for strategic investment in infrastructure to accommodate AI initiatives.
Cloudera‘s “The State of Enterprise AI and Modern Data Architectures” report shows that while 88% of enterprises adopt AI in some capacity, many lack the necessary data infrastructure and skilled personnel to fully leverage its benefits. Top barriers include security and compliance risks (74%), insufficient training or talent (38%), and the high cost of AI tools (26%). investment in human capital.
Adoption of AI-Optimized Databases
Apache’s Cassandra Community Survey shows a growing adoption of AI workloads, with 43% of respondents planning to use Cassandra for AI applications. Currently, 36% are experimenting with at least one generative AI application on the platform. Apache concludes that GenAI requires deploying a database capable of handling large-scale AI workloads efficiently.
Evolving Hardware Utilization
Hammerspace’s report “The State of the Next Data Cycle” reveals that enterprises are exploring new uses for GPUs beyond traditional AI applications, such as data analytics and high-performance computing. This shift necessitates an adaptable and scalable infrastructure capable of supporting diverse workloads.
Recommendations for IT Leaders
Here’s what the surveys, in aggregate, really say: enterprise IT’s readiness to adopt and deploy generative AI is marked by rapid adoption and high expectations. Yet, challenges in scaling, governance, and risk management persist. Organizations must develop clear strategies, invest in infrastructure, and educate employees to fully harness generative AI’s transformative potential.
To prepare for GenAI deployments, IT leaders should:
- Develop a Comprehensive AI Strategy: Ensure alignment between AI initiatives and business objectives, with active involvement from executive leadership.
- Invest in Scalable Infrastructure: Upgrade IT systems to handle the intensive computational requirements of GenAI applications.
- Enhance Data Management Practices: Implement centralized cloud platforms and robust data governance frameworks to ensure data quality and accessibility.
- Address Talent and Cultural Challenges: Provide training programs to build AI expertise and cultivate a culture that embraces technological innovation.
- Foster a Culture of Innovation: Encourage experimentation and agility within teams to adapt to AI-driven changes, promoting a mindset that embraces technological advancements.
By addressing these areas, IT organizations can better position themselves to deploy and manage GenAI solutions effectively, thereby driving innovation and maintaining competitiveness that increasingly relies on generative AI technologies.
Companies mentioned: Apache, Boston Consulting Group, Cisco, Cloudera, Dataiku, Deloitte, Hakkoda, Hammerspace, Hitachi Vantara, IBM, Komprise, McKinsey, Slack
