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For companies yet to adopt AI in their business, a lack of understanding appears to be one of the biggest barriers.
Datacom Managing Director – Professional Services, Sunny Katira, says customers are increasingly reaching out to his team looking for advice and insights into how AI can be used in their organisation.
“Our approach is to help people understand the different components of AI, such as machine learning (ML), modelling, large language models (LLM) and natural language processing (NLP), and then to focus on identifying use cases that could be a good fit with their business.”
Research commissioned by Datacom shows there are gaps in identifying clear use cases, even among organisations that are already using some form of AI. Among companies using AI, less than half (just 42%) have commercial and financial targets for the use of AI.
Katira says using chatbots and virtual assistants to help handle customer enquiries is one of the use cases that people are most familiar with, but there is less familiarity with other use cases that offer equally tangible ROI.
“Using AI to automate repetitive tasks with robotic process automation (RPA) or streamline data entry and document processing is an easy way to free-up your team to work on other parts of the business. Automating processes like this should result in the work being completed faster and with fewer errors, and your team is likely to get greater satisfaction from working on more meaningful tasks.”
Manufacturing is another area where AI can be used to take over repetitive and often tedious tasks, such as quality assurance checks. AI paired with sensors and video and image recognition software can be used to automatically detect equipment damage and product defects.
Leveraging AI for predictive analytics has multiple use cases, from predicting customer behaviours and demand, to optimising inventory management and pricing, or anomaly detection and failure prediction – to flag potential issues before they escalate.
“Security is another area where AI can be employed to keep pace with increasingly sophisticated attacks and be used to sound the alarm, for example MLM is being used to detect fraudulent transactions and suspicious activity.”
Not everyone views the adoption of AI as a positive and Katira says organisations have a responsibility to communicate clearly about their plans for AI usage and to bring people along on the AI journey.
“Reticence is natural if people don’t understand what AI is or how an organisation is intending to use it. Companies need to be upfront about AI usage and they should have policies in place to govern responsible, secure, ethical use.”
A survey of 318 senior business leaders, commissioned by Datacom, looking at AI adoption in Australia showed that only 52% of organisations had staff policies around AI use.
“People need to understand what acceptable use is for their organisation, what data are they allowed to use, what are the security considerations, and what is acceptable use within their role, for example is AI-generated code okay for a software developer and, if so, to what extent? People need to know which of the widely available AI tools they are allowed to use too, because there are security and confidentiality considerations.”
Katira says a good way to build confidence and understanding around AI is for the business to trial a tool like Microsoft Copilot, available as an add-on to Microsoft 365.
Copilot uses cases – such as summarising meetings in real time, capturing action items or prioritising emails and summarising email threads – are simple for people to trial and provide an introduction to how effective AI can be and how much time it can save on daily tasks.
“Once people see those tangible results they are going to be more open to trialling other uses, for example using Copilot in Excel to analyse raw data and provide insights and identify trends, and they will naturally become more confident over time. It is also shows people that AI is not ‘coming for their job’, it’s a useful tool that can help them work more efficiently.”
Another reason Katira suggests starting with Copilot is that this generative AI tool runs on a user’s Microsoft tenancy, so people’s data is as secure and private as the documents held in Sharepoint and Teams.
Microsoft Copilot will not surface data from other tenants and, unlike some other tools, it does not use any of the business data you feed into it to train its foundational LLMs. The upshot is that people should not have to worry that their proprietary data will show up in response to another user’s request.
Ultimately, Katira says AI has massive potential to increase productivity and change the way people work, but organisations need to spend some time identifying the right use cases for their business – or getting expert help to identify use cases – and then bring their team along on the AI journey.
This article was first published on the Australian Financial Review website: Use cases and ‘safe tools’ a smart starting point for AI (afr.com)
