Hype cycle for AI 2024 (Gartner)

Analysis

What You Need to Know

Generative AI (GenAI) has passed the Peak of Inflated Expectations, although hype about it continues. In 2024, more value will derive from projects based on other AI techniques, either stand-alone or in combination with GenAI, that have standardized processes to aid implementation. To deliver maximum benefit, AI leaders should base future system architectures on composite AI techniques by combining approaches from innovations at all stages of the Hype Cycle.

As the volume and scale of AI projects have increased, second-order effects have come into play. Increasing attention is therefore being paid to governance, risk, ownership, safety and mitigation of technical debt. These factors are being addressed at national, enterprise, team and individual practitioner levels, but, even with regulations reaching advanced stages, maturity is far from being achieved.

The Hype Cycle

The two biggest movers on 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.

Innovations at the Innovation Trigger include composite AI, AI-ready data, causal AI, decision intelligence, AI simulation and multiagent systems. These reflect the growing need to advance process and decision automation beyond single-model outputs into orchestrated multiturn composite services.

At the Peak of Inflated Expectations, responsible AI, AI TRiSM, prompt engineering and sovereign AI point to increasing concerns about the governance and safety aspects of the rapidly expanding use of AI by enterprises and individuals.

Soon to leave the peak or already in the Trough of Disillusionment are synthetic data, ModelOps, edge AI, neuromorphic computing and smart robots. These innovations still have momentum, but levels of implementation vary, and they are frequently used incorrectly or subject to inflated expectations of business value. Neuromorphic computing and smart robots have advanced significantly in the past year, indicating the potential for rapid progression through the rest of the Hype Cycle.

Cloud AI services have regressed on the Hype Cycle since last year, due to the number of GenAI-based cloud AI services that have come to market. Vendors and end users of these services have experienced problems with service capacity, reliability, model update frequency and cost fluctuation, which may, however, be considered growing pains.

On the Slope of Enlightenment are AI technologies that have many years of innovation behind them and are getting nearer to mainstream adoption. Usage of autonomous vehicles has increased in some locations, despite severe skepticism in certain quarters, the imposition of restrictions and the withdrawal of some operating licenses. Intelligent applications, now powered by GenAI, have entered the workforce, but more time is needed to objectively quantify their impact on productivity.

New entries on this year’s Hype Cycle include quantum AI, embodied AI and sovereign AI, as companies and governments are starting to respond to the potential, and dangers, of an AI-dominated future.

Figure 1: Hype Cycle for Artificial Intelligence, 2024

Innovations such as knowledge graphs and cloud AI services are plotted on the Hype Cycle for artificial intelligence based on market interest and time to commercial maturity, as of 2024. It gives you a view into how innovations will evolve over time, guiding investment decisions.

The Priority Matrix

Compared with many other Hype Cycles, this one is unusual in having so many innovations of transformational or high benefit, none of moderate benefit, and only one of low benefit.

Gartner expects that, within two years, composite AI will be the standard methodology for developing AI systems, and to be widely adopted. Another transformational innovation, computer vision, is already the subject of mass consumer adoption through smart devices.

Innovations two to five years away from mainstream adoption that merit particular attention include decision intelligence, embodied AI, foundation models, GenAI, intelligent applications and responsible AI. Early adoption of these will lead to significant competitive advantage and ease the problems associated with using AI models within business processes.

Among the innovations five to 10 years away from mainstream adoption, neuromorphic computing could open doors to novel AI architectures. An influx of new ideas and entrepreneurial ventures will be essential for further development of this technology.

AI leaders should balance strategic exploration of potentially transformative or highly beneficial innovations with investigation of innovations that do not require extensive proficiency in engineering or data science, and that have been commoditized both as stand-alone applications and as components of packaged business solutions.

Table 1: Priority Matrix for Artificial Intelligence, 2024

Source: Gartner (June 2024)

Off the Hype Cycle

The following innovations have been dropped from this year’s Hype Cycle:

• Operational AI systems: Subsumed by AI engineering.
• Data labeling and annotation: Dropped because it is more relevant to the Hype Cycle for Data Science and Machine Learning, 2024.
• AI maker and teaching kits: Dropped due to a lack of hype.

On the Rise

Autonomic Systems

Analysis By: Erick Brethenoux, Nick Jones

Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience

Maturity: Emerging

Definition:

Autonomic systems are self-managing physical or software systems, performing domain-bounded tasks, that exhibit three fundamental characteristics: autonomy (execute their own decisions and tasks autonomously without external assistance); learning (modify their behavior and internal operations based on experience, changing conditions or goals); and agency (have a sense of their own internal state and purpose that guides how and what they learn and enables them to act independently).

Why This Is Important

Autonomic systems are emerging as an important trend as they enable levels of business adaptability, flexibility and agility that can’t be achieved with traditional AI techniques alone. Their flexibility is valuable in situations where the operating environment is unpredictable and real-time monitoring and control aren’t practical. Their learning ability is valuable in situations where a task can be learned even though there is no well-understood algorithm (composite AI) to implement it.

Business Impact

Autonomic systems excel where:

• Conventional automation applying composite AI techniques is inadequate, or using fixed training data is impractical or not agile.
• It is impractical to provide real-time human guidance, or training conditions can’t be anticipated.
• We cannot program the exact learning algorithm, but the task is continuously learnable.
• Continuously or rapidly changing tasks or environments make frequent retraining and testing of machine learning systems too slow or costly.

Drivers

Autonomic systems are the culmination of a three-part trend:

• Automated systems are a very mature concept. They perform well-defined tasks and have fixed deterministic behavior (such as an assembly robot welding cars). The increasing number of use cases around automation using AI techniques is a strong base for autonomous systems.
• Autonomous systems go beyond simple automation to add independent behavior. They may exhibit some degree of adaptive behavior, but are predominantly under algorithmic control (such as self-driving cars or a Boston Dynamics’ Spot robot that has its overall route and goals set by a remote human operator but has substantial local autonomy — that is, for a very specific task). Adaptive AI capabilities are a necessary foundation for autonomic systems and should accelerate the adoption of autonomic systems.
• Autonomic systems exhibit adaptive behavior through learning and self-modifying algorithms. For example, Ericsson has demonstrated the use of reinforcement learning and digital twins to create an autonomic system that dynamically optimizes 5G network performance while creating optimization rules. This trend is showing the feasibility of such systems. Early learning about carefully bounded autonomic systems will build trust in their capabilities to operate independently.

Other drivers include:

• Autonomic behavior is a spectrum. For example, chatbots learn from internet discussions; streaming services learn which content you like; and delivery robots share information about paths and obstructions to optimize fleet routes. The advantages of systems that can learn and adapt their behavior will be compelling.
• Agent-based systems are seeing an adoption renaissance fueled by the increasing complexity of existing applications and the advent of large action models.
• Substantial academic research is underway on autonomics, which will result in more widespread use.

Obstacles

• Nondeterminism: Systems that continuously learn and adapt their behavior aren’t predictable. This will pose challenges (such as legal) for employees and customers who may not understand how and why a system performed as it did.
• Immaturity: Skills in the area will be lacking until autonomics becomes more mainstream. New types of professional services may be required (like autonomous business skills).
• Social concerns: Misbehavior, nondeterminism or lack of understanding could generate public resistance when systems interact with people.
• Digital ethics and safety: Autonomic systems will require architectures and guardrails to prevent them from learning undesirable, dangerous, unethical or even illegal behavior when no human is validating the system.
• Legal liability: It may be difficult for the supplier of an autonomic system to take total responsibility for its behavior because that will depend on the goals it has set, its operating conditions and what it learned.

User Recommendations

• Start by building experience with autonomous systems first to understand the constraints and requirements (legal, technical and cultural) that the organization is subjected to. Pilot autonomic technologies in cases where early adoption will deliver agility and performance benefits in software or physical systems.
• Manage risk in autonomic system deployments by analyzing the business, legal and ethical consequences of deploying autonomic systems — which are partially nondeterministic. Do so by creating a multidisciplinary task force.
• Optimize the benefits of autonomic technologies by piloting them in situations such as complex and rapidly changing environments where early adoption will deliver agility and performance benefits in either software or physical systems.

Sample Vendors

Aspire; IBM; Latent AI; Playtika Holding; Vanti.

Gartner Recommended Reading

Top Strategic Technology Trends for 2022: Autonomic Systems

Quantum AI

Analysis By: Chirag Dekate, Soyeb Barot

Benefit Rating: Low

Market Penetration: Less than 1% of target audience

Maturity: Embryonic

Definition:

Quantum artificial intelligence is an embryonic field of research emerging at the intersection of quantum technologies and AI. Quantum AI aims to exploit unique properties of quantum mechanics to develop new and more powerful AI algorithms that deliver better than classical performance, potentially resulting in new types of AI algorithms designed to run on quantum systems.

Why This Is Important

Quantum AI is an area of active research. Once commercialized, quantum AI could potentially help in:

• Enabling organizations to use quantum systems to address advanced AI analytics faster while using a fraction of the resources used in conventional AI supercomputing resources.
• Developing new AI algorithms that exploit quantum mechanics to deliver capabilities beyond ones that can be executed on classical systems.
• Unlocking disruptive applications that include drug discovery, energy industry and logistics.

Business Impact

While the business impact of the embryonic quantum AI field today is low, when validated techniques mature, quantum AI will enable competitive advantage across industries; for instance:

• Life sciences: Transform drug discovery by shortening timelines, lowering costs and improving outcomes.
• Finance: Optimize portfolios, minimize risk and improve fraud detection systems.
• Material science: Discover new materials that revolutionize energy transportation, manufacturing and create new revenue streams.

Drivers

• Hype around quantum technologies is driving more businesses and researchers to explore the intersection of quantum and AI.
• The accelerated pace of innovation in quantum systems (including larger volume of higher quality qubits, and greater stability and reliability of quantum systems) is driving greater interest in applicability in areas, including quantum AI.
• Access to quantum computing as a service is lowering the barrier to entry, encouraging greater collaboration among researchers and enabling exploration of new algorithms and techniques.
• Governments and enterprises globally are increasing funding for quantum (and quantum AI) research, resulting in accelerated innovation.
• The halo effect of increased hype around generative AI is driving new focus on alternative research techniques, including quantum AI, that could potentially deliver new disruptive results.
• Universities and training programs are developing programs and curricula to develop a quantum-ready workforce.

Obstacles

• Hardware limitations: Current quantum systems, while getting stabler, are still error-prone and inherently noisy, limiting their utility and impact on practical quantum AI.
• Algorithm limitations: While several quantum AI algorithms have been proposed, very few have been vetted and proven, and they are nowhere close to being enterprise-ready.
• Cost: Despite their limited utility and widespread accessibility, rapidly evolving noisy intermediate-scale quantum (NISQ) systems are relatively expensive, which could inhibit research and development efforts needed to devise quantum AI algorithms.
• Scalability of systems: Scaling quantum systems to the level necessary for enterprise-ready quantum AI continues to be a major technical hurdle.
• Compute paradigms: Integrating traditional data and analytics pipelines with quantum is inherently challenging because quantum systems operate on a fundamentally different paradigm both from a data representation perspective and from a compute (non-von Neumann model) perspective.

User Recommendations

• Prioritize investments in AI and GenAI over any quantum AI investments. Quantum AI is too nascent to warrant focused investments and unlikely to yield material gains in the next two to three years.