Introduction
Maximo Conversational AI is an evolution of previous chat experiences in the Maximo Application Suite. Maximo Conversational AI combines the power of GenAI with IBM Watsonx, IBM Research AI innovations, and the IBM AI conversational assistant interface of IBM Watsonx Orchestrate.
Conversational AI infuses a generative AI prompt experience with situational awareness, providing insights and automation into asset management workflows. Conversational AI also provides new, intuitive, natural language interactions, offering users deep analytic insights into hidden operational and maintenance data.
Using Maximo Conversational AI, new and experienced users can achieve the results of previously used complex queries and scripts by simply using a text prompt. The Conversational AI design concepts expand the use cases from single ad-hoc text prompts to more advanced use cases, allowing the AI system to recognize the context and use previously submitted text prompts as recommended actions or automation agents.
The Maximo conversational AI is based on siwarex, an IBM Research innovation that enables out-of-the-box conversational technologies for the Maximo application management domain. We refer to this concept as Situational Awareness using GenAI. siwarex uses the domain metadata in a knowledge graph for reasoning on prompt input, decomposes and executes the request into parts, and formats the synthesized results.
User research has been performed to identify key business values and achieve trust in AI conversations by business users.
Why conversational AI and automation?
Use GenAI to unleash hidden intelligence within Maximo to improve work productivity, operational resilience and business outcomes.
- New modern ways of working: Unleash new ways of working with intuitive, natural language interactions, offering users deep insights into operational data.
- Reduce cost: Improve time to value of GenAI, giving time back every day by providing insights and automation into asset management workflows
- Identify and apply best practices: Assist in asset diagnosis and quickly identify most relevant prognosis and repair procedures.
- Build skills: Reduce knowledge silos & gaps due to an aging workforce by using decades of operational failure, repair and maintenance procedural data.
Objective
Conversational AI capabilities may support a user new to the Maximo suite to enter a text prompt and receive deep insights from decades of operational and maintenance data in a fraction of the time and effort required to write a currently required query or script. We also explore the concepts that allow a user to pin a reusable prompt with context, so that Conversational AI starts suggesting the best-recommended actions in similar conditional situations, reducing any user workload to a single click.
User Research
Research objectives
To design a Conversational AI experience for Maximo we needed to uncover customer business needs, their expectations regarding a chat capability, and their willingness to use generative AI to address business problems like reducing effort, cutting costs, and improving operational resilience.
We also need to understand the gap between operational need and skills to use AI chatbots efficiently by technicians in the field.
Conclusions
Generative and conversational AI give users information faster than ever before, but that convenience is only valuable when it’s built on a foundation of trust to data and action.
- Relevance to business value
- Coverage of operational data
- Data privacy and Cybersecurity
User research found that organizations are cautious about the AI response quality, where a change to a prompt wording may alter the response. Organizations also require data privacy, confidentiality of information used, and on-premises storage of sensitive operational data.
IBM AI Design
The Conversational AI design is based on the new ‘Carbon for AI’ design system and the IBM AI chat conversational framework. Carbon for AI is an extension of the Carbon system, designed to give AI instances in IBM products a visually and behaviorally distinct identity. AI chat is a conversational framework between a user and an AI that can aid in creating tasks, finding insights, tracking documents, and more. Carbon for AI is mandatory when introducing AI capabilities in IBM products. As IBM Software design leaders we are meeting weekly to share scenarios, review UI and unify usage of Carbon for AI.
SiWareX
Situational Awareness using Watsonx (SiWareX) is an AI-powered knowledge discovery system that helps unlock new insights and accelerates data-driven decisions with contextualized industrial data.
The system represents the domain metadata in a knowledge graph. This representation can use existing representations delivered out-of-the-box, or create new custom representations using the discovery APIs of an application. For Maximo, the domain metadata model contains assets, work orders, and users to meet the primary key use cases discovered in user research. The knowledge graph used by SiWareX in preprocessing, is for reasoning on and validating the prompt input. The knowledge graph helps filter an efficient subgraph for the request. By validating consistency on entity names and properties, the system can guide, correct, and prevent the LLM from hallucinating. SiWareX processes the request into a step-by-step (Chain-of-Thought) execution. Each step is automatically executed leveraging the domain metadata. Execution is performed by using the LLM to translate the domain metadata into database queries such as database SQL or application API calls. The results of the steps are synthesized and formatted to the expected output, for example, as a computational number, a dashboard KPI, a result of a work queue, a data table, or a workflow action.
Conversational AI Scenarios
The conversational AI experience design was focuses on the following user scenarios.
- Provide assistance in workflows through a guided, interactive, and hands-on experience for new users during trial and onboarding.
- Provide an answer to an ad-hoc question, faster than building a query, creating a work queue, implementing a script, or creating a report or report by navigating screens.
- Provide data insights to users that today cannot be achieved in the Maximo UI.
- Create a high degree of automation using text prompts for actions and instrumentation.Design
Designing a Conversation Experience
“Infuse a generative AI prompts experience, with situational awareness, providing insights and automation, into asset management workflows.”
The Maximo Conversational AI should be thoughtfully designed to deliver a seamless and effective user experience by integrating with existing asset management workflows. It should function as an assistant, providing users with answers to questions, finding and analyzing information, and suggesting workflow-based actions. Conversations should be highly responsive, delivering quick and accurate feedback. Responses should be kept short and summarized to ensure insights are concise, consumable, and actionable. Additionally, the AI should provide navigation links to more detailed data in Maximo and may use the list pages in Maximo to present tabular information. The AI must also be contextual, understanding and adapting to the user’s intent during the conversation. It can leverage memory from previous interactions to suggest recommended actions, assisting users in achieving their goals effectively and intuitively. The system should continuously learn and refine its interactions to improve outcomes, evolving to assist users in achieving their objectives with increasing efficiency and accuracy.
Assistance through a guided, interactive, and hands-on experience for new users during trial and onboarding.
Designing the Interface Experience
The Maximo Conversational AI is based on the IBM AI standards, services, and components:
- Carbon for AI design
- AI Chat Component
- Watsonx Assistant
- Watsonx Orchestration
Examples of Conversational AI Designs
A few examples of prompt and action flows in Conversational AI are shown in the image below.
Example of Carbon design language for Conversational AI.
For new users, the conversation assists by suggesting exploring the capabilities of Assets, Work Orders, and Work Planning. Guidance is provided through data templates for bulk creation. For more experienced users, the conversation performs an analysis and identifies assets with high risks, and the common problem code. As an advanced case, a diagnosis is performed and details about an asset are collected and presented.
Maximo Conversational AI Preview Design
The Maximo Conversational AI preview was announced at Maximo World 2024. The implementation is built on Maximo Manage 9.0 baseline. The design for Maximo Conversational AI preview uses floating chat components.
Reduce Potential Failures on Business-Critical Assets
High-priority assets are at risk for failure when they have incomplete and overdue work orders. As a maintenance manager, I need to know which of my high-priority assets have overdue work so that I can start tracking them and remove blockers.
Scenario flow:
- Find assets – “Show only asset information about high-priority assets at the Bedford site that have incomplete work orders.
- Find assets w/ overdue work – “Make that work orders that are overdue.”
- Automate and create new work queue – “Create a work queue named Assets with overdue work.”
- Work and prioritize overdue work – “Open work queue.”
Improve Work Efficiency and Reliability on Business-Critical Assets
High-priority assets are at risk due to frequent failures, unclassified failure codes, and long completion time for overdue work orders. As a maintenance manager, I need to understand the completion time and resolution for recently completed work orders to help with my planning and asset reliability.
Scenario flow:
- Find work orders – “Show work orders, completed this year, for asset number PM-6072 at the Bedford site.”
- Find frequent failures – “Which problem codes occur most frequently among these work orders?”
- Understand response time – “Among those work orders with problem code ‘CAVITATN,’ show the average completion time.”
- Seek opportunities for WO AI intelligence – “Among those work orders, list their work order number, asset number, and long description.”
Business Outcome
Next-Gen AI Enabling Situational Awareness in MaximoMaximo World 2024 session on Conversational AI.
carbondesignsystem.comLearn more about Carbon for AI.
SiWareXView research paper on SiWare: Contextual Understanding of Industrial Data for Situational Awareness.