Insights from Gartner’s Hype Cycle for Artificial Intelligence

As we stand at the cusp of a technological revolution, the world of artificial intelligence (AI) continues to evolve rapidly, reshaping industries and redefining possibilities. Gartner’s Hype Cycle for Artificial Intelligence offers an overview of the current landscape, highlighting the most transformative trends and technologies. Embracing composite AI, responsible AI and AI engineering will be key for IT leaders to unlock the full potential of AI and drive sustainable innovation within their organization.

The two biggest movers in this year’s Hype Cycle, AI engineering and knowledge graphs, highlight the need for means of handling AI models at scale in a robust manner. AI engineering is a fundamental requirement for delivery, at scale, of enterprise AI solutions that demand new team topologies. Knowledge graphs provide dependable logic and explainable reasoning, in contrast to the fallible, yet powerful, predictive capabilities of the deep-learning techniques used by GenAI.

Composite AI: The Fusion of Intelligence

Composite AI, a concept gaining significant traction, refers to the integration of various AI techniques to enhance learning efficiency and broaden knowledge representation. Unlike traditional AI models that rely on singular approaches, Composite AI combines machine learning, deep learning, rule-based reasoning, graph analysis and optimization techniques. This fusion not only broadens AI's abstraction mechanisms but also makes AI solutions more adaptable and effective in addressing diverse business problems.

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One of the most compelling aspects of Composite AI is its ability to function with less data and energy. In a world where data availability can be a limiting factor, Composite AI leverages small datasets, complementing them with techniques like knowledge graphs and generative adversarial networks (GANs) to generate synthetic data. This approach is particularly beneficial for organizations that may lack extensive historical data but possess significant domain expertise.

Composite AI also enhances the interpretability and resilience of AI models. By integrating various AI techniques, it supports augmented intelligence, providing better insights and decision-making capabilities. For instance, combining rule-based models with deep learning can improve predictive maintenance by using sensor data to identify inefficiencies and predict potential failures.

The rise of generative AI further underscores the importance of composite AI and why Gartner believes it is on the cusp of significant media and industry interest.

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Responsible AI: Navigating Ethical and Societal Challenges

As AI technologies become more pervasive, the need for responsible AI (RAI) practices has never been more critical. RAI encompasses the ethical, legal and societal implications of AI, ensuring that AI systems are developed and deployed in ways that are fair, transparent and accountable.

One of the primary drivers of RAI is the regulatory landscape. With regulations including the EU’s AI Act now beginning to be enforced, organizations must navigate complex legal requirements to ensure compliance. These regulations emphasize the need for transparency, accountability and fairness in AI systems, pushing organizations to adopt robust RAI frameworks.

However, implementing RAI is fraught with challenges. Many organizations lack awareness of AI’s unintended consequences, turning to RAI practices only after negative impacts have been experienced. This reactive approach underscores the need for proactive measures, including comprehensive training and education on AI ethics and governance.

Another obstacle is the difficulty in measuring the success of RAI initiatives. Creating principles and operationalizing them without regular assessments makes it hard to sustain responsible practices. To address this, look to designate champions for each AI use case, ensuring accountability and promoting a culture of ethical AI development.

AI Engineering: Building the Future of Scalable AI

AI engineering is emerging as a foundational discipline for the enterprise deployment of AI and generative AI solutions at scale. This field unifies DataOps, MLOps and DevOps, creating a coherent framework for developing, delivering and operating AI-based systems.

The demand for AI solutions has surged, driven by the hype surrounding generative AI. However, scaling these solutions from pilot projects to enterprise-wide deployments presents significant challenges. AI engineering addresses these challenges by establishing consistent pipelines that support the entire AI lifecycle, from development to deployment and maintenance.

One of the key benefits of AI engineering is the elimination of traditional silos. By integrating data and model pipelines, AI engineering reduces redundancy and accelerates the delivery of AI solutions. This integrated approach enables organizations to build high-value AI portfolios that are secure, scalable and efficient. Additionally, AI engineering facilitates the reuse of AI artifacts across the enterprise. By creating discoverable and composable repositories of data catalogs, knowledge graphs and model stores, organizations can streamline AI development and deployment. This is particularly important for scaling AI initiatives across hybrid, multi-cloud and edge environments.