As AI advances rapidly, new applications within AIOps are gaining momentum. Dr. Maitreya Natu writes about how collaborative learning and conversational intelligence will emerge as key trends for 2024, driving seamless collaboration between humans and AI in IT operations.
As Artificial Intelligence (AI) advances rapidly, new applications and capabilities in AI Operations (AIOps) are gaining momentum. Among them, collaborative learning and conversational intelligence have emerged as key trends for 2024, driving a paradigm shift towards seamless human-AI collaboration and symbiotic intelligence amplification. This has ushered in a transformative era of application of AI and machine learning (ML) in IT Operations (ITOps).
The true potential of AI emerges when it combines the power of pattern mining in complex data with the intuition and experience of natural intelligence. CL and CI play a key role in this human-AI convergence, facilitating shared learning and natural interaction to foster greater autonomy, predictive capabilities, and collaboration. In essence, they are transforming how AI-enabled IT operations are done.
To understand the impact of CL and CI, it is important to first know what they are, what they bring to the table, and how they are applied in real-world scenarios.
Collaborative learning: Moving beyond isolated AI models
While traditional AI models continuously learn and adapt, they often fail to capture human intuition. AIOps relies not only on data-driven reasoning but also on subject matter experts to understand the nuances of the technology and domain.
CL incorporates human-in-the-loop (HITL) mechanisms into AI solutions, allowing AI systems to dynamically learn from human expertise, experience, and real-world interactions through an interactive approach that integrates human intelligence and oversight into automated or AI-driven processes. Through this collaborative approach, AI models can continuously expand their knowledge, improve their decision-making, and adjust to evolving human preferences and environmental contexts.
The impact of CL is far-reaching. AI will no longer operate in isolation, but will be able to complement and amplify human intelligence. Humans can provide AI with domain expertise, contextual nuances, and creative insights while benefiting from AI’s computational power, scalability, and pattern recognition. This paves the way for breakthroughs that will transform industries ranging from healthcare to finance, scientific research to creative projects.
Collaborative learning (CL) offers valuable applications in a variety of use cases, including:
- Enhanced insights with AI: As AI systems mine data to generate observations and insights, CL brings human experts into the loop to help translate those findings into actionable recommendations.
- Enhanced triage and resolution using AI: For AI solutions that perform automated triage and resolution, CL allows human experts to handle exceptions and unknown situations that the system hasn’t yet learned how to handle. This human-in-the-loop approach allows the AI system to improve over time.
- Validating AI automation tasks: When an AI solution automates repetitive tasks, CL can involve human experts to mitigate automation risks by providing expert validation and correction, ensuring the reliability and accuracy of the automated process.
- Validating AI-generated insights: Once the AI system generates insights, CL can incorporate human experts into the review, validation, and approval process, allowing organizations to leverage AI insights for more effective, proactive planning.
In the example CL scenario, an AI system first mines data to identify the root cause of a problem and recommends a fix. A human expert then reviews the AI-recommended fix and either approves it or provides an alternative solution. The human expert also acts as a designated handler for exceptions that the AI system hasn’t yet learned how to handle. Through this iterative human-AI interaction, the AI system continuously learns and improves its automated triage and resolution capabilities, which improves the overall scope, effectiveness, explainability, and reliability of the automated process.
Although CL has many advantages, it can also come with certain challenges. For example, a CL system may ask too many questions of human experts and there is a risk of over-reliance on human expertise. Therefore, two important principles should be followed when implementing CL:
- Ask the right questions at the right time: Leverages available data, enterprise context, and insights from similar past conversations to make as many inferences as possible, and only turns to human experts for missing information.
- Evaluate when to trust human intuition versus data-driven insightsEvaluate the insights based on factors such as level of support, reliability, currency, and consistency to decide which approach to trust.
Conversational Intelligence: Bridging the Communication Gap Between Humans and AI
AIOps often produces a wealth of insights, but users can struggle to make the most of them due to insight fatigue (feeling overwhelmed by the volume of insights and not knowing what to prioritize or how to trust them).
CI represents an interaction model that enables humans to have intelligent conversations with machines. Leveraging advances in natural language processing (NLP), CI revolutionizes human-AI interaction. Instead of relying on fixed-format interfaces like reports, notifications, and dashboards, CI enables natural and intuitive communication, making it easier for humans to use AI products effectively.
A CI-enabled AIOps platform allows IT professionals to interact with AI systems using plain language: the CI assistant can understand complex queries, provide contextual responses, and engage in multi-turn dialogues that mimic human conversation.
CI helps address insight fatigue by empowering users to identify areas of focus and discover insights through simple interactions. It also brings explainability and trust to the insights derived from AI. For example, a business leader asks the CI system about areas that need attention. The CI engine understands the user’s context, organizational status, historical preferences, and business criticality to provide insights on areas of focus.
The CI system guides the conversation, helping the user prioritize these areas, providing details, and recommending actions. Importantly, the CI engine adapts to the flow of the discussion, providing root cause analysis or identifying similar issues. Through each interaction, the CI engine deepens its understanding and refines its responses to suit the user’s needs, building trust and providing increasingly valuable explainable insights.
There are two important principles to consider when implementing CI:
- Engage in intelligent conversation with humans: A CI system needs to capture the context of the conversation, form a perspective, guide the dialogue, and adjust its response based on user feedback, allowing the system to have meaningful, dynamic interactions with users.
- Bring explainability to your AI insights with Explainable Intelligence. Leveraging concepts from the field of explainable AI, CI should provide both textual and visual evidence of the reasoning process behind its insights, allowing users to better understand and trust the AI-driven output.
see next: Five lessons to avoid AIOps pitfalls
Exponential power: CL and CI are revolutionizing AIOps
The convergence of CL and CI creates a new metaphor for augmented intelligence and is transforming AIOps platforms. CL enables AIOps solutions to continuously learn by understanding systems, data, and operational knowledge, connecting the dots, filling in the gaps, and receiving ongoing training and validation from human experts. CI empowers humans to effectively leverage AI-powered insights, making them explainable and easily consumable, increasing trust and adoption of AIOps.
Combining CL and CI paves the way for conversational, explainable, and trusted operational intelligence where humans and AI form a true partnership. As they mature, they provide the means to tackle complex IT challenges, drive innovation, and extend knowledge with greater precision by enabling a variety of use cases, including:
- Combining AI pattern recognition with human contextual understanding improves real-time anomaly detection and prediction.
- Enhance automated incident resolution through situational understanding and efficient human-AI collaboration in resolving new and exceptional cases.
- We provide highly customized recommendations that take into account environmental, business, and operational factors.
- Build trust and confidence in AIOps to increase adoption
The powerful marriage of CL and CI paves the way for a collaborative future where artificial and human intelligence coexist. AI processes vast amounts of data quickly, identifies patterns, and automates tasks precisely. Humans provide intuition, creativity, emotional intelligence, and contextual frameworks that AI lacks. By integrating the two, humans can harness the power of AI, freeing them to focus on higher-level cognitive tasks and strategic decisions.
CL and CI are game changers that bridge the gap between powerful AI capabilities and human expertise and wisdom. Through the symbiosis of humans and AI in AIOps, organizations can proactively optimize IT operations, make informed choices, and become more agile. This partnership will expand what humans and machines can achieve in many domains in 2024 and beyond.