Artificial Intelligence (AI) is no longer a futuristic concept confined to the realms of science fiction; it has become a transformative force within enterprise IT. As organizations grapple with the challenges of the digital age, AI is emerging as a powerful tool to enhance efficiency, drive innovation, and maintain a competitive edge. However, the integration of AI into enterprise IT is not without its complexities. The technology is evolving rapidly, and companies must navigate this landscape with strategic foresight to harness its full potential.

In this deep-dive exploration, we’ll examine the profound impact AI is having on enterprise IT, delve into the frameworks guiding its adoption, and hear from industry experts on how organizations are leveraging AI to transform their operations.

The Current Buzz: AI’s Role in Enterprise IT

The buzz around AI has been palpable for years, but its impact on enterprise IT is only now being fully realized. “Everybody gets that AI is going to change the world, but nobody is really clear as to how,” observes Geoffrey Moore, author of Crossing the Chasm and The Infinite Staircase. Moore’s sentiment encapsulates the paradox facing CIOs and IT leaders today. They are under immense pressure to invest in AI, yet the path forward is still forming, norming, and storming.

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1. For production use cases, OpenAI still has dominant market share. pic.twitter.com/TwqU1yExSa

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AI’s influence is pervasive across various layers of enterprise IT, from infrastructure to systems of engagement and record. According to Vala Afshar, Chief Digital Evangelist at Salesforce, “AI is reshaping the way enterprises operate by infusing intelligence into every layer of the IT stack, creating opportunities for unprecedented efficiency and innovation.”

Systems of Infrastructure: The Foundation of AI Integration

At the heart of enterprise IT lies the systems of infrastructure—the backbone that supports all other systems within an organization. AI’s impact on these systems has been profound, particularly in the areas of compute power and data management.

“To generate a competitive Large Language Model (LLM) requires a hyperscale compute footprint that only a handful of companies have the resources to deploy,” Moore explains. The demand for computing power has skyrocketed with the advent of AI, leading tech giants like Microsoft, Amazon, and Google to invest billions in expanding their infrastructure capabilities. For other companies, the challenge lies in licensing these powerful LLMs and adapting them for their own use.

Data management has also been revolutionized by AI. Enterprises are now tasked with extracting vast amounts of data from systems of record, engagement, and collaboration, normalizing it, and filtering out sensitive information—all in real-time. This data is then staged in data lakes, ready for AI-driven analysis and decision-making. “AI is driving enterprises to rethink their data strategies, ensuring that they can capitalize on the wealth of information they possess,” says a representative from Scale Venture Partners.

Moreover, latency issues in real-time applications have necessitated the deployment of edge computing. “Some processing needs to be done at the edge rather than in the core,” Moore notes. This shift calls for new infrastructure equipped with both GPUs and CPUs, capable of running real-time operating systems under robust cybersecurity measures.

Systems of Record: Enhancing Core Enterprise Functions

Systems of record—such as finance, HR, and supply chain management—are the bedrock of enterprise operations. These systems are traditionally conservative, prioritizing stability and integrity over adaptability. However, AI is poised to change that dynamic.

“AI changes this game dramatically,” Moore asserts. Generative AI, for instance, enhances the traditional user interface (UI) by introducing natural language processing (NLP). This shift allows employees to interact with systems of record more intuitively, reducing the need for extensive training and making these systems more accessible.

Predictive AI is another game-changer for systems of record. Unlike traditional software that is preprogrammed and static, machine learning algorithms continuously adapt and learn, offering real-time insights for critical functions such as predictive maintenance, fraud detection, and demand forecasting. “It’s like having a six-sigma black belt on duty 24/7,” Moore quips, emphasizing the transformative potential of AI in optimizing core enterprise functions.

Systems of Engagement: AI at the Frontline of Customer Interaction

While systems of record are essential for internal operations, systems of engagement—such as sales, marketing, and customer service—are the lifeblood of external interactions. These systems are less conservative by nature, requiring constant adaptation to meet the unpredictable demands of the market.

“Generative AI has a much bigger role to play in these market-facing applications,” says Moore. AI-driven tools are already being used to create and manage email marketing campaigns, respond to customer inquiries via chatbots, and provide on-demand technical support. These tools not only enhance efficiency but also enable a level of personalization and responsiveness that was previously unattainable.

However, predictive AI faces more challenges in systems of engagement due to the unpredictability of human behavior. Despite this, Moore argues that data-driven decision-making will eventually surpass human intuition in areas like sales forecasting and marketing campaign attribution. “Advanced statistical software that learns outperforms even the best humans eventually,” he adds, citing the success of AI in mastering complex games like Go as a parallel.

Systems of Collaboration: Enhancing Team Dynamics with AI

Systems of collaboration—encompassing tools like video conferencing, messaging, and file sharing—are integral to modern enterprises. These systems not only facilitate communication but also capture the nuances of team dynamics and relationships.

“AI excels at summarizations that cut through the clutter of long communication threads,” notes Moore. AI-driven tools are being used to streamline workflows, send timely reminders, and conduct sentiment analyses, all of which contribute to more efficient and effective collaboration.

The data generated by systems of collaboration is also invaluable for AI models, providing insights that can improve both collaboration itself and other systems within the enterprise. “By including such data in data lakes that feed your AI models, you’ll improve your systems of collaboration and make better recommendations to your systems of record and engagement,” Moore advises.

Systems of Intelligence and Autonomy: The Future of Enterprise IT

As AI continues to permeate enterprise IT, the concept of “systems of intelligence” is emerging as a critical component. Rather than existing as a separate layer, systems of intelligence infuse AI capabilities into every aspect of the IT stack, enhancing the intelligence of core business systems.

“Our core business systems are poised to become more intelligent,” Moore explains. This intelligence is achieved by overlaying advanced statistical software that learns, transforming the enterprise IT landscape. CIOs are now tasked with assessing and enhancing their systems’ “IQ,” ensuring that AI-driven insights are fully integrated into their operations.

However, the rise of “systems of autonomy” presents a new set of challenges. These systems, which can operate without human intervention, are already being used in areas like digital advertising, space exploration, and military threat detection. “The question is when you take the human out of the loop, who or what is in charge?” Moore muses. This is not an intractable problem, but one that requires careful consideration and a robust framework for governance.

Embracing AI’s Transformative Power

As AI continues to evolve, its impact on enterprise IT will only grow. The technology is transforming every layer of the IT stack, from infrastructure to systems of record, engagement, collaboration, intelligence, and autonomy. While the challenges are significant, the potential rewards are immense.

“AI is not just a tool; it’s a transformative force that will reshape the very nature of enterprise IT,” says a spokesperson from IT Chronicles. Organizations that embrace AI will be better positioned to navigate the complexities of the digital age, driving innovation, efficiency, and growth.

However, as Moore cautions, the path to AI adoption must be approached with strategic foresight. CIOs must prioritize where to engage first, ensuring that AI investments are aligned with their organization’s goals and capabilities. By doing so, they can harness the full potential of AI and secure their place at the forefront of the enterprise IT revolution.

As we move forward, the conversation around AI and enterprise IT will continue to evolve. New technologies, applications, and frameworks will emerge, offering fresh opportunities for innovation. By staying informed and adaptable, enterprises can not only keep pace with the changes but also lead the charge into the future of business.