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How AI is being leveraged in Work Management



AI is having a profound impact on society and the economy. It is reshaping our methods of work, communication, and interaction with technology. The landscape of field operations is becoming increasingly competitive, and the strive to optimise operations is pushing SaaS providers to stay at the forefront of AI integration, as they anticipate increased market demand for quicker, more optimised, and accessible work management solutions for various industries.


Traditional service delivery operations are automating routine tasks, gaining, and providing insightful analytics, and enhancing decision-making processes using AI, revolutionising ERP modules across various industries.


The goal is to no longer require human effort and intelligence to properly code and enter every detail of a project, business transaction, or work order to complete an operation. People will no longer have to approve work orders, assign work orders, monitor asset statuses, resource statuses or inventory statuses. Comprehensive project reports of every job performed can be drafted automatically. Managers and business owners will be able to gain critical business insights that pull from various reports across multiple industries, jobs, and situations to provide clear and comprehensive imaging of the business’s performance whilst forecasting.


AI can automatically generate work orders in response to service requests or incidents reported through various channels, including tickets logged and emails. It analyses the content of these communications to understand the issue and creates detailed work orders that include the necessary actions, parts, tools, and most importantly, resources needed to resolve the issue and automatically schedule and dispatch those resources.


Generative AI assistants in the form of conversational bots (intelligent chatbots) are allowing users to talk or text with a system, enabling them to generate orders, enter expense reports, update job statuses, confirm product receipt in warehouses and inspect assets, among other tasks. These AI powered bots are performing tasks that previously required the user to go through the system and manually key data into it. AI assistants deliver real-time actionable, user-specific insights harnessing all data sources (internal and external) to aid field resources on the jobsite. Not only does this integration of AI save time and improve efficiency, but it improves workflow for field technicians.


Often information that comes into Work Management systems is incomplete or possibly incorrect. Expense reports, purchase order line-item details or general ledger journals may be missing segments of necessary data required to complete processing. A customer rep may not be aware that a client has recently moved and input an old address for the client. All minor human errors that can have varying degrees of effect on an organisation’s performance. When AI is applied, errors and inconsistencies are automatically detected, incorrect data can be updated and solutions to complex problems can be provided in an instant.


AI is unifying data from any source, internal and external, including business, IoT, performance, and third-party data, to deliver a complete view of an organisation and allow seamless workflow between various teams within an organisation. Integrated into organisational systems, the AI learns and adapts to ways of working, therefore, automating, and optimising processes across traditional operational and data silos. With its overview of business operations, history, customer data and customer history AI enables faster, intelligent customer relationship management.


Based on an analysis of the progress that is being made by various companies in Work Management and AI integration, one can see which functionalities are being enhanced by AI to varying degrees within their systems and how they plan to progress as AI develops even further.


One of the most impactful applications of AI within any organisation is its integration with email systems. AI has the capability to meticulously comb through all emails related to an individual, generating comprehensive reports based on your interactions. These reports can detail the individual’s organisation, their status as a customer, any work-related correspondence, and gauge their emotional responses, such as satisfaction or frustration.


AI-driven chatbots are equipped to suggest actions in response to emails, whether it is replying, scheduling meetings, or addressing customer concerns and inquiries. By analysing the content of an email—take a customer’s issue, for instance—the AI can craft a complete response by drawing on similar past correspondences. This proposed solution can then be reviewed by a sales representative or support manager for accuracy before being sent to the customer.


Moreover, by accessing an organisation’s email archive, AI leverages real-time data from various sources to inform project management, scheduling, customer relationship management, supply chain, procurement, and more. Employing an organisation’s email database as a foundational dataset allows AI to predict outcomes and devise solutions based on historical and contextual data, significantly enhancing productivity and overall output.


Generative AI has the capability to leverage information from similar projects to recommend a detailed project plan upon receiving a project’s name and description. It can outline tasks, estimate their duration, and suggest appropriate resources for assignment. This automatically generated plan remains fully customisable. Furthermore, AI efficiently monitors service execution, financial transactions (both current and historical), budgetary compliance, and revenue performance, alongside tracking individual team member progress. This enables the automatic generation of comprehensive project status reports, highlighting potential risks and financial insights, and suggesting mitigation strategies.


This technology excels in forecasting potential risks that could adversely affect a project’s timeline, offering practical solutions to pre-emptively address these challenges. By maintaining oversight of the project, AI can identify clients with pending payments, detailing the necessary information to prompt the team to initiate contact. Through email integration, it can even draft reminder emails to be sent directly to clients. Additionally, AI adapts by rewriting, modifying, or creating new workflows based on observed user behaviour, unusual patterns, and insightful data analysis, further optimising operational efficiency.


AI has the capability to evaluate and prioritise tasks by assessing their urgency, impact, and deadlines, using historical data, and aligning with company priorities. It enhances task management by considering key factors such as customer history, emotional responses, and the potential for future business, thus optimising task prioritisation for customer satisfaction and business growth. By analysing past data, AI identifies optimal scheduling strategies and anticipates potential challenges, streamlining operational planning.


AI categorises work types and intelligently assigns the most suitable resources to specific tasks. In instances where the ideal resource is not available or is geographically distant from the job site, AI selects an alternative based on availability and proximity, using detailed profiles on each resource’s qualifications, experience, skills, and training to ensure the best match. For roles requiring certification, AI manages scheduling to comply with regulations, significantly reducing administrative workload.


AI can also leverage historical customer and asset interaction to optimise resource allocation, ensuring that individuals with the best relationship, most experience, or deepest knowledge of an asset are prioritised. This consideration extends to the availability of necessary parts and tools, with the AI integrated with inventory management systems to ensure resources are adequately equipped, thereby enhancing efficiency and effectiveness in task execution.


AI enhances the recruitment process by performing intelligent resume analysis. It uses natural language processing (NLP) and machine learning algorithms to evaluate the qualifications and experiences of job candidates. The AI can then match candidates to roles that fit their skills and career aspirations, increasing the chances of successful placements, and reducing turnover.


AI systems are designed to understand the unique profiles of each workforce member. By analysing individual skills, past experiences, and performance data, AI can offer personalised recommendations for learning and career development. This could include suggesting specific courses or certifications, recommending projects that align with the individual’s career goals, and identifying potential mentors or peer connections within the organisation.


AI in field management includes advanced analytics to predict workforce supply and demand. It assesses current staffing levels, predicts future needs based on business trends, and identifies gaps in the workforce. This allows organisations to proactively recruit, train, and allocate resources to meet anticipated demands.


In situations where collaboration is needed across various parts of the organisation, AI can identify and link skilled resources who can aid one another. It enables knowledge sharing and cooperation among team members, facilitating a more integrated approach to problem-solving.


Generative AI (GenAI) can be used in field management to provide technicians with step-by-step guides to solving issues in the field. By drawing on real-time insights and historical work order data, GenAI can create comprehensive, context-aware assistance. This not only helps in resolving issues more efficiently but also serves as a learning tool for the workforce, enhancing their skills and knowledge over time.


Through AI-driven processes Work Management becomes more adaptive, strategic, and efficient, enabling organisations to stay competitive in a rapidly changing global market.


Advanced algorithms analyse historical sales data, market trends, and consumer behaviour to accurately predict customer demand. This predictive capability allows for automatic adjustments in supply chain activities to align with anticipated demand, ensuring that products are available where and when they are needed.


AI allows employees to focus on strategic tasks rather than routine administrative work, leading to a more efficient business processes and cost savings for the organisation.


AI’s predictive material and resource planning capabilities aim to minimise inventory carrying costs. By forecasting material requirements and optimising resource allocation, AI ensures that inventory levels are kept lean, reducing holding costs and freeing up capital.


Equipped with all asset details and history, generative AI assists maintenance management by generating insights and recommendations for maintaining assets, predicting when they are likely to require maintenance. Analysing operational data, AI can forecast potential breakdowns before they occur, scheduling maintenance activities proactively to minimise downtime. This includes scheduling maintenance tasks, ordering parts, and even suggesting process improvements to prevent future issues.


AI-enhanced computer vision systems are employed for quality control, allowing for automated visual inspections of assets, products, and components. These systems can identify defects or inconsistencies that might be missed by human inspectors, ensuring high quality while reducing the time and cost associated with manual inspections.


In Customer Relationship Management, extracting pertinent information from emails is the most efficient solution to significantly reducing the time taken to address customer issues, and ensuring that customers are kept satisfied. AI can automatically compile and display all relevant customer data for support teams, providing them with quick, centralised access to the necessary information which can be used to auto-draft appropriate responses to customer queries, as previously mentioned. Streamlining the support process, ensuring that customers receive timely and comprehensive assistance.


Virtual bots, summarise a customer’s query or issue swiftly. By doing so, support staff can quickly grasp the situation and address it effectively. The generative AI draws upon all available company data, including notes from previously resolved cases, to formulate a response that is consistent with solutions to similar issues encountered in the past.


When a job has been completed AI can automatically create and distribute invoices. AI pulls data from contracts and purchase orders to generate invoices, ensuring that they are accurate and sent out promptly, as well as identifying customers with overdue payments and flagging these accounts for follow-up. As mentioned previously, AI can automatically compile financial data related to specific projects, creating comprehensive status reports.


While Work Management solutions were originally designed for the management of large enterprises, the COVID-19 pandemic catalysed an unprecedented surge in public need and demand for remote service delivery by 900%. Throughout the pandemic, remote service delivery became the predominant modality, accounting for 76% of all services. Prior to the pandemic, organisations were already crafting tools, resources, and methods for remote service provision. Post-pandemic, the landscape has evolved further, with remote services carving out new and lucrative opportunities for businesses.

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