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SEE ALL OUR THINKING LIBRARY ALL NEWS CASE STUDIES MARKET REPORTS INSIGHTS INSIGHTS Why financial accounting is the ideal environment for artificial intelligence Artificial intelligence (AI) is revolutionising the way countless industries operate. Financial accounting is no different, with many firms utilising AI technology to aid the transition to new standards. Here is a quick rundown of the key benefits of AI, and why accounting is the ideal environment for the technology. Efficiency Undoubtedly one of the biggest benefits of AI is that mundane tasks become automated, freeing up accountants to focus on providing those higher-level insights. MRI software company, Leverton, showed this when they assisted a leading accounting firm during the transition to IFRS 16. Their AI-enabled platform was enormously helpful in determining population and importing data, capturing the relevant data, extracting and validating data, and for ongoing data management and auditor communication. Not only was the project a success, but the transition was completed ahead of schedule. Traditionally, data had to be manually extracted from lengthy contracts by accountants and auditors. Then this had to be interpreted, analysed and checked for compliance with IFRS standards. It is an arduous process that could easily take several hours to complete. With AI, much of this can be automated. Many AI-enabled platforms, such as Trullion, also offer a reporting function, which checks relevant accounting periods against the terms of the specific lease contract. In a further benefit, AI is less prone to human error, making it preferable for tasks such as data extraction. New insights AI, particularly machine learning (ML – a subset of AI), can be used to generate predictive models. Over time and with accurate data, these models can forecast revenues and other financial information. In addition, much of AI can now analyse unstructured data such as contracts and emails. When combined with industry specific knowledge, this could stimulate new insights to make financial accounting more accurate than ever. Transparency AI is also promising in terms of anti-fraud protection. Many platforms, such as Trullion, use continuous tracking so that any document changes are logged. The modification history can be viewed at a later date. ML can also be used to model expected ‘normal’ activity, and therefore flag potentially fraudulent cases. Training Education company, Tishadz provide specialist training to help accountants learn the new IFRS standards. They first use AI to assess the learner’s understanding of IFRS content. Then, this AI-assisted training gives immediate and regular feedback, showing the learner problem areas they need to target and providing personalised feedback on how to improve. Challenges While the benefits of AI are compelling, there is still some resistance to uptake. In a 2019 survey, 60% of respondents said they did not trust AI-produced financial data to the same extent as traditionally-produced data. The key reasons are concerns over data privacy, security and poor data management. Some of the AI advantages, such as providing insights from unstructured data (e.g., emails sent by employees) would clearly cause ethical and legal concerns. In addition, the success of AI depends upon large quantities of data, so smaller firms may be wise to be wary of adopting AI in their business. AI adoption requires considerable time and investment and should not be undertaken without a full risk assessment. Why accounting is the ideal environment for AI Despite the potential challenges, many believe financial accounting is the ideal environment for the adoption of AI. Those working in the field already have the high-level mathematical skill and business knowledge, making them perfectly placed to reap the benefits of AI. In addition, technology in finance is a familiar concept. Accountants need high-quality financial data to assist with decision making and have been using technology to assist in this process for years. Therefore, they are likely to intuitively understand when and how to rely on such technology, and when to trust their human instincts. Finally, much of the compliance process focuses on rule-based logic, making the field ideally suited to AI technology. Given these factors and the number of firms successfully using AI to assist the transition to new standards, we should be seeing a higher adoption of AI in the future. INSIGHTS The most exciting growth sectors in AgTech In order to keep up with population growth over the next thirty years, the world’s food systems will have to produce 70% more than they currently do. The solution is agricultural technology (AgTech) — an industry that utilises technology to produce food more efficiently in the face of climate change. The UAE has led investment into AgTech, announcing a plan for 50% of all food in the emirates to be produced locally by 2051. This is ambitious considering that currently only 20% is produced locally, and the region suffers from a shortage of arable land. However, this is where technology can help. Let’s take a look at some of the most exciting AgTech developments, and how these fields could progress going forward. Vertical farming Vertical farming refers to growing produce in stacked layers, usually in combination with other high tech solutions such as automated LED lighting. It is a great way to repurpose old skyscrapers, warehouses, shipping containers and more — making it ideal for locations short on space. However, vertical farming really comes into its own in difficult weather conditions. Recently, construction began on what is set to become the world’s largest indoor vertical farming unit, located in Abu Dhabi. The ability of these indoor systems to withstand extreme climate conditions makes them highly valuable, especially as pressure to produce locally increases (thereby reducing the carbon cost of production and reliance upon imports). The biggest barrier to vertical farming is the cost. Not only do firms in this area bear the expense of the premises, but it can take time to fine-tune automation and watering processes. However, this doesn’t seem to have dissuaded the UAE, who invested $100 million in vertical farming in 2020. The global vertical farming market was reported to be worth $3 billion in 2020 and is estimated to grow to £18 billion by 2030. While initially expensive, the promise of weather-resistant vertical farming in areas with non-arable land, high temperatures and water scarcity offers long-term benefits likely to outweigh this investment. Smart greenhouses An average kilogram of tomatoes takes 214 litres of water to grow, but this can be reduced to just four litres in a smart greenhouse. These high-tech buildings utilise technology such as sensors to automatically regulate heat, humidity and light. Luna by iUNU uses robots to scan crops several times a day, and then uses artificial intelligence (AI) to respond to any status changes. In another example, Bold Robotic Solutions uses AI to monitor equipment, and respond to inefficiencies in real time. Smart greenhouses are often considered less exciting than vertical farming. It is true that much of the technology overlaps, as AI solutions are often used in vertical farming. However, it can sometimes be as expensive to build a single layer of vertical farming as it is to build an entire greenhouse. Where there isn’t the need for space and to grow completely without sunlight, smart greenhouses can be a more cost-effective option. Research and development in this area is particularly strong in the Netherlands, as the government poured investment into smart greenhouses after the famine following World War Two. However, growth is slower than with vertical farming: the market was reported to be worth £1.37 billion in 2019, and is estimated to reach £3.23 billion in 2027. Alternative proteins On a slightly different track, alternative proteins refers to plant-based (fungi, bacteria and yeast-based proteins) and animal-cell derived meat products. According to Boston Cosulting Group, these alternatives will make up at least 11% of the protein market by 2035 — up from 2% in 2020. This shift will have significant environmental impacts, such as reducing carbon dioxide production and water consumption. Some of the main barriers to alternative proteins include the quality — uptake is still low due to taste and texture of some products. In addition, many alternatives are expensive to produce, particularly the animal-cell derived products. However, with a potential market of $290 billion by 2035, the opportunities for agricultural technologists are promising. AgTech is a relatively new but quickly growing industry. The pressure of climate change will only increase the need for technological solutions. Those with skills in AI, data science, manufacturing and more will find themselves in high demand for years to come. INSIGHTS The future of careers in data science The nature of data science is changing. More aspects of the role are becoming automated, and this shift opens up data science to a wider audience. What do these changes mean for the field, and what will data science careers look like in the future? Automation Traditionally, data scientists have needed extensive programming knowledge, obtained by years of experience and, in most cases, a university degree in computer science or a related field. You would be hard pushed to find a current job listing that doesn’t mention something like Python, Java or R as an essential requirement — because that’s what a data scientist is, someone who writes code and constructs complicated neural networks by hand — right? Not quite, because automation has already changed the nature of the job. Automated tools mean the average data scientist spends less time on mundane tasks such as data preparation, data cleaning and writing code from scratch. Now automation is becoming incredibly sophisticated. For example, Google’s AutoML allows customers to train machine learning models and use them to gain insights from their own data. It is an extremely user-friendly platform requiring minimal data science knowledge. A changing role So, will data scientists soon be replaced by super-efficient automated tools? This seems unlikely. It is notoriously difficult to replicate the human element of data science. Automated tools are efficient, but still prone to error — particularly when models make their own assumptions — and lack the business knowledge to return relevant insights. What automation should do is make data science more productive and allow those employed in this area to focus on higher value projects. This could also mean ‘higher-level’ data scientists specialise in narrower areas. So instead of being a data scientist, you might be an NLP expert or a recommendations scientist. Wider access While qualified data scientists will still be in demand for higher level projects, automation will make the field more accessible than ever before. This means companies without a budget for a dedicated data science team can utilise automated tools to better understand their business. It is possible that future data science roles could become two tier, with non-data science jobs requiring some experience with automated tools, and specialist data scientist jobs specifically geared towards providing those top-level insights. Regardless, automation should increase demand for data scientists rather than decreasing it, by making the field accessible to the general population. New roles Not only will automation change the nature of existing roles, but it could create new jobs, particularly to help solve the additional problems that automation will create. For example, most data scientists use a wide range of open source tools, such as Python and R, which contain hundreds of add-on packages. This results in what a recent Forbes article referred to as a ‘wild west’ collection of systems and tools, which increases both operational and security risks to a business. Therefore, with increased reliance on automated tools, there will be greater need for organisational roles, such as Chief Information Officers (CIOs), to help mitigate this risk. This will be similarly important for businesses utilising data science tools for the first time (as they may lack the knowledge to foresee operational issues, such as a tool going out of date) and those with large data science teams (who are likely to rely on numerous tools). The future of data science is bright. As mentioned by Techcrunch, we have already seen what happens to a field that becomes more accessible — increased automation in software engineering has only increased demand for programmers. The nature of the role might change, but we ultimately live in a data-driven world, and the need for talented individuals in this area is likely to grow for many years to come. SEE ALL INSIGHTS TO YOUR INBOX Keep in touch with the latest from McGregor Boyall. 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