How To Use AI For Smarter Resource Allocation

In a dynamic technological and economic ecosystem, businesses in every industry face the pressure to maximise their returns. A crucial yet often overlooked element in this quest is resource allocation.

Efficient resource allocation involves the strategic distribution of physical resources, personnel, finances, and time. It also means crunching a lot of data, making it a prime target for the use of artificial intelligence (AI).

Many people have heard of using ChatGPT to search for a job, brainstorm ideas, or summarise meeting notes, etc., but they may not have considered the ways AI can increase the efficiency of their current projects. Below, we’ll discuss practical steps for transforming your organisation’s resource allocation from intuitive guesswork to data-driven decision-making.

Making The Best Use of Physical Resources


Most industries rely on some level of physical resources, but manufacturing companies are especially dependent on raw materials and pre-assembled components. Traditionally, resource allocation has relied on the past experiences of the decision-makers as well as intuition; a “gut feeling” divorced from conscious reasoning.

While this method is certainly more effective than not planning for market fluctuations, guesswork is often incorrect or subject to biases. AI, on the other hand, can help those same decision-makers come to data-driven decisions.

For example, an AI algorithm can analyse years of data on demand for a product, looking for repeatable patterns. It can also crunch numbers related to current events, viral videos, Google searches, or celebrity endorsement of a product, for example. It can then make specific, information-based predictions and recommendations. This can help prevent the under-utilisation or over-allocation of physical materials.

Further, AI can be used to find and reduce “leaks,” inefficiencies that result in wasted time or materials, such as unused equipment or excessive scrap materials, for example. This can reduce overall resource requirements.


People and Man Hours Are Resources Too


In the same manner described above, AI can analyse data, including team skillsets and project complexities, to make suggestions on the allocation of human resources. This can optimise schedules and help ensure that the people with the right skills are assigned to the right tasks.

AI can also be used to assist with mundane, repetitive tasks so that talented employees can focus on the work that matters. Such routine tasks may include managing project budgets, screening new hires, composing emails or other communications, matching personnel with projects, or creating schedules. AI assistants can shift many man-hours from low-level tasks to highly skilled ones, driving projects forward more efficiently.

The use of AI can also reduce human error in some circumstances (though it should be noted that responses from AI should be checked for errors, too).  For example, AI can identify typos, missing values, formatting errors, defects in manufacturing products, and even positive medical lab results that might have been missed by a human observer. Correcting errors early can lead to greater overall efficiency and the conservation of resources needed to remedy errors later on.

Planning for Factors You Can’t Control


For some industries, disruptive and unpredictable challenges are par for the course. Take, for example, agricultural food production. Some resources are relatively stable—it takes a certain amount of seed for a given amount of land, and those plants will need certain soil conditions, hours of sunlight, fertilisers, and moisture.

But other factors are outside of man’s control. Rain levels vary, and long droughts can affect water available for irrigation. Pest or disease outbreaks might make some crops inefficient in a given year. Supply and demand can cause financial returns on investment (ROIs) to fluctuate wildly.

More agritech startups are using AI to help compensate for these uncontrolled factors and maximise yields. Smart programmes monitor the temperature and weather patterns as well as crop and livestock health. AI crunches the numbers and makes detailed suggestions, enabling farmers to conserve resources (for example, by precisely adapting fertiliser application to soil composition) and move resources as needed, even predicting the need, as in the case of weather events.

Similar application of AI can be made in various industries dealing with unknowns ranging from climate change to viral trends. Algorithms can analyse real-time data, market trends, and historical data to forecast future demand.


Real-Time Management


Perhaps the greatest benefit of using AI for resource management is that it can be utilised in real-time, 24 hours per day. AI never sleeps. It can analyse data and monitor trends constantly. This type of agility is especially useful in fast-paced environments that require adaptation to changing situations throughout a project’s life cycle.

AI can take the guesswork out of resource allocation. AI algorithms are capable of analysing massive amounts of data, both historical and real-time. They can make unbiased suggestions on the use of materials and the workforce, combined with predictions of market fluctuations and trends.

In the end, AI resource allocation can produce better personnel/project matchups, market fluctuation responses, and bottom lines.