Training an AI model to anticipate equipment failures is a significant accomplishment in the field of engineering. The real transformation in business occurs when the AI model effectively identifies a malfunctioning machine, leading to concrete action being taken. While one milestone remains theoretical in a proof-of-concept presentation, the other contributes meaningfully to the company’s financial results.
According to Craig Partridge, who serves as the senior director worldwide of Digital Next Advisory at HPE, the true value of AI lies in inference. Inference is where AI demonstrates its worth, utilizing its training in practical applications within real-world processes. Partridge explains that the focus should be on “trusted AI inferencing at scale and in production,” as this is where organizations are likely to see the most significant returns on their investments in AI.
However, progressing to this stage is challenging, as noted by Christian Reichenbach, who works as a worldwide digital advisor at HPE. Referring to a survey conducted by the company involving 1,775 IT leaders, Reichenbach highlights that while 22% of organizations have successfully integrated AI into their operations—an increase from 15% in the previous year—the majority are still confined to experimental stages.
To advance further, a three-pronged strategy must be adopted: establishing trust as a foundational principle, ensuring execution centered around data quality, and developing IT leadership capable of effectively scaling AI initiatives.
Building trust through reliable inference means ensuring that users can depend on the insights provided by AI systems. This reliability is crucial across various applications such as content generation and customer service chatbots but becomes mission-critical in scenarios like surgical robotics or autonomous vehicles navigating complex environments.
The quality of outcomes derived from inferencing processes heavily depends on the accuracy and integrity of the underlying data. As Partridge emphasizes, “Garbage data inputs lead to flawed inferencing outputs.”
Reichenbach illustrates the consequences of poor data quality with examples of unreliable AI-generated content causing disruptions and inefficiencies within workflows. When trust diminishes due to inaccuracies, productivity suffers, and desired outcomes remain elusive.
Conversely, when trust is ingrained into inference systems effectively, efficiency and productivity can soar. For instance, a network operations team equipped with a trusted inferencing engine can receive rapid and precise recommendations tailored to their specific needs—essentially gaining a valuable partner enhancing operational capabilities.
In earlier phases of AI adoption, companies prioritized hiring data scientists and pursuing complex models with massive parameters. However, with a shift towards producing tangible results from initial trials, organizations are now emphasizing data engineering and architecture.
Reichenbach notes that over recent years, breaking down data silos and swiftly extracting value from diverse data streams have gained significance alongside the emergence of AI factories—a continuous intelligence production line fueled by data pipelines and feedback mechanisms.
This shift represents a transition from focusing solely on models to embracing a more holistic approach centered around data quality and origination. Strategic decisions around platform direction, operating frameworks, engineering roles, trust considerations, and security protocols are all guided by two pivotal questions regarding ownership of intelligence models and proprietary data sources.
Partridge introduces HPE’s four-quadrant AI factory implication matrix as a tool for clients to map their responses to these critical questions and translate them into actionable strategies tailored to their specific contexts.
It’s imperative to recognize that these quadrants are interrelated rather than mutually exclusive; most organizations operate across multiple quadrants simultaneously. For instance, Partridge highlights HPE’s practice of developing models internally for network management before integrating them into products for end-users—a process encompassing multiple quadrants within the matrix framework.
The aspect of “at scale” within Partridge’s inference philosophy underscores a common challenge faced by enterprises deploying AI solutions: what may work well for isolated scenarios could prove ineffective when applied organization-wide. In his view, realizing widespread benefits from AI necessitates solutions that cater to diverse use cases accessible across all levels within an organization.
Partridge stresses that IT departments play a pivotal role in transitioning experimental projects into comprehensive systems serving entire organizations. He underlines that historical lessons from past infrastructure shifts like cloud migration highlight the risks associated with fragmented decision-making—underscoring the importance of structured governance frameworks in managing AI experimentation effectively.
While encouraging experimentation is valuable, IT departments must orchestrate structured approaches toward innovation rather than allowing disparate teams to independently explore tools outside established oversight mechanisms—a phenomenon known as shadow AI.
To navigate these complexities successfully requires establishing robust data platforms integrating enterprise-wide data sources while upholding governance standards and accessibility prerequisites essential for feeding AI initiatives. Standardizing infrastructure elements alongside safeguarding data integrity becomes paramount for maintaining brand credibility amidst evolving technological landscapes demanding speed and flexibility from AI applications.
In outlining pathways towards success amid these challenges, Reichenbach emphasizes clarity across all quadrants—balancing technology ambitions with governance imperatives aimed at generating tangible value throughout an organization’s AI journey.