By Dr. Tim Sandle
Published December 15, 2023
Teachers at a workshop on ChatGPT bot in Geneva - Copyright AFP Aaref WATAD
According to Ugo Orsi, Chief Customer Officer at Digitate, the business world can expect two impactful things to occur during the course of 2024. The first of these is that businesses will make sense of GenAI in AIOps. The second are involves enterprises finally being able to quantify the return of investment in the area of intelligent automation.
GenAI in AIOps
As generative AI continues to gain momentum in mainstream business, business can expect to see a ‘levelling out’ in 2024. According to Orsi, this will come “as enterprises begin to adopt standards and deploy GenAI in applications that make business sense as they pair it with AIOps.”
Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models.
So how does this differ from current processes? Orsi spells this out: “Conversational AI will drive customer-facing elements such as customer support, directing inquiries, managing UX interfaces, and the initial vetting of customers.”
In outlining the benefits, Orsi continues: “GenAI will be used to provide personalized support to IT users, such as by answering their questions and troubleshooting problems. Beyond that, GenAI will support improved anomaly detection and prediction, automated remediation, which can free up IT staff to focus on more strategic tasks, and enhanced decision-making, providing insights that help IT leaders make better decisions about resource allocation, capacity planning, and other critical areas.”
Enterprises quantifying the ROI of intelligent automation
With the second area of innovation, Orsi foresees that intelligent automation (IA), as a powerful tool that can be used to improve IT operations and reduce costs, will become more widely used.
Intelligent automation involves the use of automation technologies – artificial intelligence (AI), business process management (BPM), and robotic process automation (RPA).
In terms of the reasons why, Orsi finds: “A well-designed IA solution will boost customer satisfaction, operating cost decreases, attrition and turnover for IT staff decreases. However, it can be difficult to quantify the value of IA projects, which can make it challenging to get buy-in from executives.”
In terms of the benefits to be realised, Orsi says: “As enterprises deploy IA, tangible measures of ROI are required that are meaningful to key decision makers.”
And in terms of the coming year, he predicts: “In 2024, enterprises will need to adopt a model for designing IA transformation projects that are more likely to succeed. A “black box” approach to IA transformation will not work. Instead, a more Agile approach that is based on Sagas, Epics, and Sprints is recommended.”
In terms of where business can turn, Orsi puts forward: “Powerful AI-based solutions for IT operations such as Digitate’s ignio can be used to predict incidents, offer ways to prevent them, and even suggest improvements to the technical design/architecture to optimize operations, transparently, making ROI easier to showcase.”
The path to machines thinking more like us will ‘accelerate in 2024’
By Dr. Tim Sandle
Published December 15, 2023
Quantum computing has been touted as a revolutionary advance that uses our growing scientific understanding of the subatomic world to create a machine with powers far beyond those of conventional computers
- Copyright AFP/File LUCA SOLA
Collaborative learning and conversational intelligence will be among the most important AI developments for 2024, according to Dr. Maitreya Natu, Chief Data Scientist at Digitate.
Natu sets out why Collaborative Learning (CL) and Conversational Intelligence (CI) are poised to revolutionize AIOps in 2024. This is based on the abilities of these technologies to: “Usher in a transformative era for the application of AI and machine learning in IT operations. By facilitating collaborative learning and natural interactions with humans, these advancements will drive increased autonomy, predictive capabilities, and enhanced human-AI collaboration, reshaping the landscape of AI-powered IT operations.”
The reference to AIOps refers to an initialism first coined by Gartner. Here, AIOps represents “artificial intelligence for IT operations”. This is defined as the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows.
Of particular interest is natural language processing, as a branch of computer science concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
In terms of the operational specifics, Natu spells out: “CL breaks down silos between AI models, allowing continuous learning and improvement through interactions with other AI systems, humans, and real-world data.”
The advantages of this approach “fosters more comprehensive and accurate AI-driven decision-making by sharing knowledge, insights, and experiences”, Natu observes.
Expanding this further, Natu finds: “CI empowers AI systems to engage in human-like conversations, bridging the communication gap between humans and AI. CI-enabled AIOps platforms enable IT professionals to interact with AI systems using plain language, democratizing AI accessibility and fostering trust in AI-powered decision-making.”
Both CI and CL are descriptors normally applied to humans interacting with each other. In the context of algorithms, the same processes used by humans are attempted to be replicated by machines. The aim is to deliver innovation and development, especially in the case of conversational intelligence, which seeks to recreate the intelligence hardwired into every human being, a process that enables us to navigate successfully with others.
In terms of how these factors come together, Natu sess the advantages as: “The convergence of CL and CI will bring about transformative changes in AIOps in key areas such as real-time anomaly detection and prediction, automated incident resolution, personalized knowledge management, and explainable AI for informed decision-making.”
Collaborative learning and conversational intelligence will be among the most important AI developments for 2024, according to Dr. Maitreya Natu, Chief Data Scientist at Digitate.
Natu sets out why Collaborative Learning (CL) and Conversational Intelligence (CI) are poised to revolutionize AIOps in 2024. This is based on the abilities of these technologies to: “Usher in a transformative era for the application of AI and machine learning in IT operations. By facilitating collaborative learning and natural interactions with humans, these advancements will drive increased autonomy, predictive capabilities, and enhanced human-AI collaboration, reshaping the landscape of AI-powered IT operations.”
The reference to AIOps refers to an initialism first coined by Gartner. Here, AIOps represents “artificial intelligence for IT operations”. This is defined as the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows.
Of particular interest is natural language processing, as a branch of computer science concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
In terms of the operational specifics, Natu spells out: “CL breaks down silos between AI models, allowing continuous learning and improvement through interactions with other AI systems, humans, and real-world data.”
The advantages of this approach “fosters more comprehensive and accurate AI-driven decision-making by sharing knowledge, insights, and experiences”, Natu observes.
Expanding this further, Natu finds: “CI empowers AI systems to engage in human-like conversations, bridging the communication gap between humans and AI. CI-enabled AIOps platforms enable IT professionals to interact with AI systems using plain language, democratizing AI accessibility and fostering trust in AI-powered decision-making.”
Both CI and CL are descriptors normally applied to humans interacting with each other. In the context of algorithms, the same processes used by humans are attempted to be replicated by machines. The aim is to deliver innovation and development, especially in the case of conversational intelligence, which seeks to recreate the intelligence hardwired into every human being, a process that enables us to navigate successfully with others.
In terms of how these factors come together, Natu sess the advantages as: “The convergence of CL and CI will bring about transformative changes in AIOps in key areas such as real-time anomaly detection and prediction, automated incident resolution, personalized knowledge management, and explainable AI for informed decision-making.”
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