Osama Qazaqi

AI can be used to directly optimize operations at my work. Coming up with innovative services, products, or end user experience all across the different sites and the enablement teams , we need to apply AI systems efficiently and create an AI-driven virtual partner that can generate new business ideas and develop new offerings with real impact. Innovative AI agent (Products, Services, End user experience): Product innovation: with my current employer's case in the domain of the recycling industry, an AI-driven agent identifies new uses for recycled materials, optimizes product design for recyclability, it can further develop the creation of entirely new recycled products based on market trends and saved data history. Service innovation: in an enablement team like a logistics team, an AI agent can create a smart logistics AI-powered environment, like route optimization, truck selection, and can also optimize recycling solutions and services for large corporate customers. End user experience: AI agent can offer real-time education for all staff across the different recycling centres, personalized end-user support for sorting or compliance issues. AI-powered chatbots for HR, Finance, IT, and Customer Service (especially important for distributed locations) can resolve day-to-day queries and enable the enablement team staff to focus on other, more critical issues. AI Opportunities in Enablement Teams Enablement teams are the supporting units, including HR, IT, Finance, Sales & Marketing, and logistics. HR: As part of the capstone project, we are currently creating an AI chatbot for onboarding and day-to-day support for all employees. This will allow smart workforce planning, less overhead for the HR team, and better adherence to rules and regulations, especially with ongoing feedback. Finance: in the Finance team, we can automate invoice processing on both sides, accounts receivable and accounts payable, where human interaction on both roles is minimal, and only clear parameters need to be stated to control cash flow business models. Sales & Marketing: as you can imagine, a well-designed AI can personalise the way we reach customers, provide better and consistent follow-up for all customers , revive old customers , further analyse customer feedback and spot market needs and opportunities, and propose new marketing products and develop and design required campaigns for new products IT: AI can proactively detect infrastructure risks by ongoing monitoring, highlight cyber threats in advance, and automate helpdesk to the maximum level. Other cross-functional opportunities Predictive Maintenance: at a certain level, we can use AI to anticipate equipment failure as discussed on a previous occasion or unsafe conditions in recycling facilities, which will bring down times to the lowest level and cost of maintenance lower and increase staff safety. Comprehensive Reporting: AI can produce reports across all operational facilities and develop an easy to understand that highlights opportunities and positive impacts, and provides feedback. Supply Chain Optimization: The AI system should be able to predict the peak waste generation time in different recycling centres and types of waste, and build an AI-driven SoP (standard operating procedure) to allocate resources, design a clear supply chain loop business model. How to enable AI agents to drive innovation in my environment? Cross-functional workshops: Conduct ongoing workshops for all enablement teams and other departments to brainstorm about new product/service ideas. And how to use AI to propose concepts, streamline processes across all departments. Pilot proposed solutions: conduct quick pilot projects for the different ideas discussed in the brainstorming session, and use a genuine business case template to associate innovative ideas with the current system setup. This will allow clarifying business, environmental, social, and ROI from the innovative ideas. Feedback: We should have a clear feedback cycle for the outputs of the points discussed in the previous point and measure outcomes of the pilot phase, and upgrade the succeeding ideas as “new innovations”. Human-AI collaboration: in each enablement section, we need to compile a set of trusted users from the same department, ensuring that outputs are evaluated in the real world rather than just in models, including benchmarking with standard ethics and compliance. Embracing such an innovative approach will bring our company ahead in the recycling industry on both operational and business model, and strategic innovation.

AI can enable early risk detection within the scope of my IT operations by continuously analysing data from infrastructure subject to continuous monitoring tools and across the entire distributed site infrastructure to spot anomalies, predict failures, and flag possible risks. which makes early intervention both timely and effective. This will allow proper reliability and advanced resource allocation, especially in live applications. AI Risk declaration process: The AI expected solution will be using data logs from different resources (monitoring tools, systems log, in-house developed applications, and sometimes external data which will make the learning process of an AI solution more realistic and tuned to the local solution by: Using real-time reading and logs, this will allow the AI solution to forecast system failures, network dropouts, and downgraded service on critical systems. AI solutions will be able to identify system abnormalities, detect operational risks, and identify any potential system breaches, sabotage, or resource misuse across different sites. The more data volume we have, the more the AI system will improve prediction accuracy and reduce false positives. After building an AI solution, it will be able to prioritise high-severity risks and address required resources, and possible alternatives and solutions. How does this enhance my working environment? Operationally, an AI solution should reflect the following benefits in my working environment: We should be able to observe reduced downtimes by early predictions and proactive actions. Better allocation of IT operations resources and having the opportunity to better plan for the coming projects and ongoing system risk-related patching. Better system compliance and adherence with international regulatory platforms (HIPAA, CCPA) Better cybersecurity detection systems and, as a result, a safer environment (security-wise) An AI solution may help to detect safety issues in different recycling centres, e.g., using AI safe proximity cameras, where it detects any human in the deadly range of any heavy load vehicle. How can an AI solution defeat alarm fatigue? alert fatigue describes how busy workers (in all systems where an alarming system is in place) become desensitised to safety alerts How to achieve that To ensure critical alerts are taken seriously: First, AI will categorise risks into different severity levels, which will keep the action required tailored to each case, and alarm fatigue will be minimised. Second, the AI solution is deleting duplicate alerts and cancelling alerts that have already been resolved automatically. Low-impact alerts are expected to be resolved by the AI system, where only serious issues will be escalated to humans. Feedback is essential for similar AI solutions and which keep the AI solution refined and consequently decrease false positives. An AI solution for risk detection allows IT operations managers to confidently identify, respond to, and mitigate risks proactively, improving operational efficiency with little need for resources.

In my role, I am the operations manager of a leading recycling company with almost 1300 employees. Our tasks are primarily focused on managing IT infrastructure for waste processing, supply end-to-end services for all users, data analytics and systems administration. At my end, workforce scheduling involves IT support teams (e.g., helpdesk, network admins), supporting developers for our business software, and cybersecurity specialists who support the IT security issues. Primarily, we are working onsite and partially remotely; we don’t have shifts, but we ensure 24/7 uptime for critical systems like IoT systems. If we injected an AI agent into the system, we would expect the following changes: IT Team readiness: we expect AI to tailor the IT teams into different categories (networking, support, system administration) based on their learnt technical capacity Trending and seasonal changes: we expect AI to be able to predict the seasonal issues (e.g. switch patches) based on learnt experience and prediction, and predict the demand of hardware and linked logistics (e.g., all purchases slow down before the end of the financial year and pick up in one month after two months from that period Compliance with local rules and regulations: we expect the ability to predict issues based on loads dumped by different customers at the recycling centre, and the effect of that on local rules and how we should interact with possible concerns coming from the EPA (Environment Protection Agency). This will save the IT team big time on referring to the issue and get back to the related video footage. Better collaboration with other IT teams: considering the dependencies between the different IT teams and conflicts on some occasions, AI shall bring Balancing ability between different IT teams, shape the different responsibilities and diffuse any possible conflict Cost management: We expect AI should be able to predict usage, optimal purchase lots, procurement frequencies, and detect any extra usage by users on the different platforms (mobile phone bills, credits used in some systems). Incident Prediction: Using learnt data from our ticketing system (Jira) will be able to predict failures and general trending issues. The challenge is how to measure the efficiency of the system before and after AI, and how this affects the current and later required technical capacity in the IT operations task force o In our domain as a recycling company, where we have operations 24/7 in more than one location, the minimum service disruption is better, where we can easily extract Jira reports in this regard o The ability to schedule scenarios and prioritise different options and even ticket on our ITSM, should reflect in a lower number of unresolved severity 1 tickets. * Tracking incident resolution rates or the number of phone calls triaged into the IT helpdesk centre Having AI in IT operations could transform IT scheduling from reactive to proactive, enhancing both operational resilience and end-user satisfaction across the company.

One of the AI approaches to deliver personalised chatbot services without breaching user privacy is by using the user interaction history with the system, with which system will interact with user needs and preferences and offer user alternatives based on knowledge base, this will give user better personalized experience and better results and longer times of chat with customer before triaging ticket to agent to add him to chat. This standard process comes as part of the following outline: · Understand the situation: after the user typed in his request, the system will analyse the need based on the knowledge base and the user's history, then. · Evaluating the user request: based on the user's KB, the AI chatbot shall anticipate the real need and differentiate if the request is a valid one or not. · Personalised service by proposing a tailored solution based on the user's KB, preferences and additional new requirements of the current request Ø Let’s take an example from my domain (IT Helpdesk) serving around 1300 local staff: · Relevant Scenario: A user from the transport team where he is expecting the onboarding of a new driver ASAP, for that he jumped into chatbot and listed his problem, the chatbot got back to same user chat history and asked him several question (after confirming the reception of the new request ); is the new Driver based in Sheldon site in QLD, does the user need access to system and requires a M.S teams license, do you think you can temporarily spare a phone for the started, and so on, this level of chat enhances the service for this users and others without the need to get connect you to one of the IT Helpdesk agents. Practicality of the personalisation method The practicality will be affected by two main factors, the first one is the depth and readiness of the knowledge base, meaning the more the KB is organised, and the level of data that exists is enough for the AI tool to use and build proper scenarios The second factor is by the user himself, where the more precisely the user can illustrate the issue, the better results they will get from the chatbot Other factors may also affect this, such as the complexity of the system, the maturity of the IT helpdesk FAQ, and the Knowledge base. Balance between adding value and maintaining trust Building trust between user and AI in general and Chatbot in specific is an ongoing concern and considered part of the modern AI discussion. The following are guidelines to build a trusted Chatbot: · Ongoing enhancements, at the level of design, agility and maturity of the KB · Make the chatbot privacy policy available for users as part of a transparent approach · AI is meant to think like a human, but not necessarily act like a human, meaning that we need to build a chatbot that is as friendly as a human, but when it comes to a human chatbot, precise replies and questions play a better role in building a trust relationship between the user and the chatbot · When sharing sensitive data, it should go through a secure/encrypted channel, which will increase the trust of the end user

With AI tools, there are great opportunities to add a high level of order to messy documents all across the organisation. Typically, AI tools provide the opportunity to generate recommendations that lead to improved knowledge outcomes and enhance the way the outcome is organised. It is important to understand the burden and overhead the non-organised knowledge management would lead to, including but not limited to extra effort required, increased error rate, and eventually affecting overall productivity Let’s imagine a law firm that has an organised knowledge base. This will create a huge loss of staff time for data search; the same task might be repeated several times by the same user or multiple users at the same time, the probability of error happening is higher, and eventually, many opportunities are lost due to a lack of organised knowledge Using a prompt and flow-based approach makes knowledge management more efficient as it turns out to be faster to retrieve, gives users the opportunity for smarter decisions based on smartly organised knowledge, triggers updates, projects and other critical events as part of the flow-based approach, and further it detects gaps in the organisation and provides recommendations

For leaders to trust AI, we need to consider the following concerns : * Can we build an AI agent with not enough data about the case to make tough and timely critical decisions by AI? We expect with limited data to make biased and inappropriate decisions; however, we might be able to build an AI model that mitigates the data shortage issue, but this will be risky. Therefore, it is rather advised to build a selective automated system by keeping the critical decision to the leader, considering the output of the AI agent * What are the possibilities when data is incomplete, or options are conflicting to overlapping, to get a trusted critical decision by an AI agent? There is an opportunity to build an AI agent that works on different techniques to eliminate data shortage and conflicting data, probabilistic reasoning, uncertainty quantification, or contextual analysis. Bases on the nature of the business and criticality of decision; leaders can work on segmenting the decision making approach into three different categories, level one we trust to AI agent to take decision where options are limited and the risk of which is controlled and supervised, level 2 where leaders can delegate the final decision making based on AI agents to reporting managers, and level 3 which is concerned with the real costly decision where tolerance toward errors is almost zero; in this category leader should avoid trust AI agent decisions but still they can consult it * What are the working environment constraints that might limit the correct decision by the AI agent? The level of working environments' complexity plays a critical role in AI agents’ rational decisions, and it is important to consider that there are deterministic and dynamic environments where transparency, social context, and ethical context need to be addressed when we deploy AI agents * How far can the decision taken by AI be in favour of the business goals? Any decision taken by an AI agent should be for sure in favour of the business goals, as this is part of the context and data set the AI agent is using to make decisions; however, “How far" is based on the robustness of the system setup in the business, the Business AI strategy, and proper ethical governance * How long does AI need to be part of the working environment to get the correct sentiment for any required decision? Sentiment is probably the next of AI machine learning, as it requires a bit of complex AI setup, the competencies of data AI is learning from, therefore, in our case, where the data dataset is incomplete and there are conflicting parameters, the sentiment will be biased even if we tried to enhance the dataset's integrity level * Can we build a tailored AI agent to be able to detect the leader's tone in decisions? Technically, it is possible, and this turns out to be an AI digital assistant. This would require sentiment analysis, NLP dedicated to the leader's personal data set. Now, how far will this agent be trusted for a critical decision? It all depends on a healthy relationship between the leader and the agent itself, in which, over time, the leader will be able to decide when to delegate the decision to the agent and when to keep it for himself Conclusion: An AI agent can be considered as an advisor in the case of a critical decision for a leader; however, we need to consider the problems and effects of having incomplete data or conflicting data, and based on that, we shall fine-tune our own ability to trust the AI Agent to make critical decisions