AI is the future for data in government and education

From automating expense policies and projecting budgets to analysing large-scale datasets, AI and machine learning are gaining momentum.

Artificial intelligence and its subset machine learning (ML) are often met with some workforce resistance, particularly in the public and higher education sectors. This is fuelled by the fear that the technology may present data security risks and could replace jobs.

Task automation is indeed a key benefit, with more than 30% of tasks able to be automated in 60% of jobs.

But less than 5% of jobs are predicted to be fully replaced. Instead, automation will replace the mundane, repetitive tasks, giving employees more capacity to focus on value-adding and creative tasks.

Monotonous jobs lack stimulation and often result in inaccuracies as the educated, creative employees responsible for these tasks become tired or disinterested, leading to shortcuts or mistakes being made, which can also lead to data security issues.

AI and ML are designed to automate these aspects of the job to reduce errors and cost, while also improving job satisfaction. While the automation technology continues to run constantly without requiring any breaks, workers can focus on tasks that require their creativity and attention.

This lets them add more value via strategic tasks while the organisation achieves productivity gains when it comes to the automated tasks.

This is especially valuable when it comes to big data. Public sector and tertiary organisations potentially have access to more data than at any time in history, and this data can be used to deliver significant value to business operations.

However, without the right systems to acquire, organise and apply big data, that value can go unrealised.

By using AI, public and private institutions can more efficiently make sense of the huge datasets that contain structured and unstructured data. The sheer volume and complexity of this data means it simply can’t be processed by humans. Automating this process via AI provides the ability to make the raw data meaningful and can help data analysts exploit the available datasets to find value.

Public sector organisations are a prime example. They generate and store large volumes of data, such as census data, that can yield significant value by offering insights into what specific groups value, by looking at where, when and how cash is being spent, for example.

AI can capture streaming data, determine valuable attributes and provide real-time analytics that can inform efficient and effective decisions.

AI can also assist in cost containment for public sector and higher education institutions by increasing employee efficiency and providing better spend visibility and analysis.

Additionally, by leveraging ML capabilities such as automating departmental expense policies, organisations can ensure employees and transactions are compliant in real time, reducing the risk of fraudulent activities, regardless of intention.

In the current global climate, public sector and higher education organisations need to cut costs while simultaneously pivoting to meet rapid market changes. Real-time data insights let these organisations more easily adapt as needed.

Using AI and ML algorithms to evaluate data, public sector and higher education organisations can better model and address real-time scenarios while also forecasting probable data patterns that impact business operations.

This visibility lets teams make more informed, strategic decisions and align the business response to keep one step ahead of market developments.

From automating expense policies and projecting budgets to analysing large-scale research datasets, AI and ML are gaining momentum in helping the public and higher education sectors make strategic decisions to pivot and scale their operations while improving employee satisfaction.

Matt Goss is Managing Director, ANZ, for SAP Concur.

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