The Most Complex Business Management Task Ever

Algorithmic BrAInAI Insights - The AI Imperative: The Most Complex Task Ever

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The Most Complex Business Management Task Ever

Despite the great promise that AI holds for companies looking to create better, faster experiences for customers, adopting these technologies is certainly one of the most complex management tasks of a generation, and probably the most complex management task ever.

AI technology is constantly evolving and changing, which makes it difficult for companies who are lukewarm towards AI or who have a more traditional IT legacy setup to keep up with the latest trends and to manage the human-centred issues that AI brings about. In addition, AI technology is also costly and requires a significant amount of upfront capital investment to implement properly. As a result, many companies are hesitant to adopt AI technologies due to the high cost and risk involved, as many misunderstand AI and are unable to see either the benefits or the risks that it can bring about.

In today’s business world, if planned and implemented well, AI technologies can bring about a critical competitive advantage. They bring tangible benefits in processing speed, accuracy, and consistency, which is why many professionals now rely on it. AI is especially helpful during high-volume decision-making, when putting human employees to the task results in tiredness, boredom and distraction that can all have a heavy toll on a business. AI is changing the way businesses operate and is ushering in new ways of thinking about strategy. Even once a business gets a sense of how it could serve its market with AI, adopting and scaling these technologies within the organisation is a Herculean undertaking that many managers and executives end up struggling with.

Organisations should contemplate AI as a function of business and commercial capabilities rather than technologies. In general, AI may help businesses meet four key objectives, namely:

  • 1. Automating Business Processes.

    Many business processes can be automated using Robotic Process Automation (RPA) and where cognitive capabilities are required, by deploying AI technologies. This includes tasks such as data entry that requires some form of cognitive processing capabilities, customer service, and even decision-making. Automating these processes can help companies improve efficiency and accuracy while freeing up employees to focus on higher value-added tasks.

  • 2. Getting Insight through Data Analysis.

    AI can be used to analyse large amounts of data to identify trends and patterns that would otherwise be difficult to detect. These insights can help businesses make better decisions about products, services, and strategies.

  • 3. Engaging with Customers and Employees.

    AI can be used to create more personalised and engaging experiences for customers and employees alike. By way of illustration, AI-powered chatbots can provide quick and convenient customer service, while virtual assistants can help employees with tasks such as technical troubleshooting, scheduling appointments or booking travel arrangements.

  • 4. Driving Innovation.

    AI can help businesses drive innovation in a number of ways, from developing new products and services to improving existing ones. By way of practical examples, generative AI can be used to create product designs that are optimised for given parameters and constraints that a human designer could never match as the human mind acts heuristically whereas AI is usually programmed to act in an optimising manner. AI can also help test prototype designs of new products or potential improvements to existing ones before they are physically prototyped and thereafter released to the market.

The conventional wisdom – to start small, pick the low-hanging fruit and then scale up – has given way to an acquiescence that organisations need a fundamental shift in their approach to data to do even that well. Moreover, at times, starting small makes scaling up significantly costlier and more difficult. This makes scaling-up without a prior holistic AI plan either less probable or altogether impossible. Even though AI onboarding has become a critical task, most organisations are still not sure where to start, and seeking professional help might make a huge difference.

With the accelerated automation brought about by the Covid-19 pandemic, the big question on everyone’s mind remains when AI onboarding will eventually mean that the bots are going to take all human jobs and how this will happen. While most experts agree that for some considerable time to come there will always be roles for humans, even in a machine-powered future until AI has managed to surpass all human capabilities, the immediate questions remain what those jobs will be and what they will look like, what skills they will require, how companies can best prepare their workforce to acquire the skills to do such jobs, and how humans and AI will eventually work together. This begs the even more fundamental question of whether humans are managing the machines, or whether the machines are going to be managing the humans. The answer to this question would usually dictate the managerial approach to AI onboarding, as well as the technological and leadership pathways adopted by the organisation, and will eventually have regulatory, management and process-related implications on the firm’s AI growth pathway and the firm’s growth more generally. It stands to reason that the greater the degree of organisational focus on people helping AI, and AI helping people, the greater the value that can be unlocked by the AI transformation for the foreseeable future.

As technology improves, robotic automation projects are likely to lead to some job losses in the very near future, particularly in the offshore business-process outsourcing industry. As a general rule, if you can outsource a task, you can probably automate it with relative ease.

While AI is in itself a complex and costly management task to implement, once implemented it can increase the value and efficiency of management. Indeed, there is no aspect of management, from planning, decision-making, control, execution and monitoring that AI has not impacted, oftentimes drastically, for those organisations who have decided to invest in it.

When it comes to planning, AI is being used to create predictive analytics and to provide real-time alerts. It can help identify emerging trends, manage and optimise stock-keeping units, assist with supply chain planning, as well as plant and facilities maintenance management. It can also optimise resource allocation plans and help with project management throughout the entire project management lifecycle by ensuring realistic resource allocations commensurate with the tasks and by identifying tasks that are likely to slip and by calculating the additional resources required to get those tasks back on track.

In terms of decision-making, AI is being used to automate the entire decision-making process while providing managers with recommendations on what actions to take, based on data and analytics, an area of AI referred to as AI-based Decision-Support Systems (AI-DSS). By way of example, AI can be used to automatically approve or reject loan applications, grant access to buildings or computer systems, or to make hiring decisions. It can also be used to identify and recommend the best course of action in complex situations where there are multiple options and outcomes, as well as with pricing decisions, product placement, target market identification and customer segmentation. AI can also assist with fraud detection, risk management, compliance, supply chain optimisation, identification of opportunities for cost savings, sourcing decisions, pricing strategies and the compilation of profit-maximising new product offerings.

In terms of control, AI is being used to monitor and control tasks, processes and systems in real time or quasi real time. It can be used to identify deviations from expected behaviour and take corrective action on predetermined or dynamically-determined rules and criteria, and has thus been prolifically put to use in myriad sectors ranging from transport and logistics all the way to agriculture, manufacturing and services. More specifically, AI can be used to optimise process performance by constantly adjusting set points and operating conditions. As such, AI can help with quality control, process optimisation, yield management, the monitoring of equipment performance, asset utilisation and employee productivity. AI is also employed in warehouses to ensure proper stock levels and to provide automated inventory management. Amazon warehouses, for instance, use robots that can identify empty shelves, optimise order queues and move inventory around.

In terms of execution, AI is being used to automate tasks that are repetitive or require a high degree of accuracy and that would normally prove to be distracting nuisances for human beings managing projects. In this ambit, AI can be used to fill out forms, extract data from documents, or generate reports. It can also be used for more complex tasks such as scheduling meeting rooms, preparing financial reports, or managing inventory levels. In terms of execution and monitoring, AI can be used to track shipments in real time, monitor production lines for quality control issues and even predict when a machine might break down.

In terms of monitoring, AI is being used to create dashboards and reports that provide real-time visibility into operations, as well as to detect anomalies and track Key Performance Indicators (KPIs) in real time. It can provide alerts when thresholds are breached or when unexpected behaviour that might require corrective action is observed. It can also help organisations understand how their processes are performing by providing insights into process flows and identifying bottlenecks and areas for improvement. It can help identify issues early on, track KPIs and compliance with Service-Level Agreement (SLAs) and monitor compliance.

It is important to keep in mind that while investments in AI are commendable, they cannot happen quickly unless the building blocks are already there. Apart from the very obvious building block of capital availability to be dedicated to AI investment, which is the sine qua non of any AI project, data availability is also important. This can usually serve purposes other than AI and is therefore an investment worth undertaking in its own right and independently of any potential future AI investment.

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Do you believe that AI can be of help to your organisation?

At Algorithmic BrAIn, one of the Equinox Group companies, we have developed a comprehensive staged checklist to ensure that you leave no one of your important considerations out when planning your AI journey. We’d love to be able to help you get this right and if you think we can help you in this, we’d be thrilled to hear from you.