In recent meetings and events with CEO’s and business leadership teams, we’ve noticed a certain amount of confusion around the benefits of Artificial Intelligence, and who in the business should be championing it. As a result, many companies are not fully utilising the valuable data they hold. With confusing terminology and the perceived need for complex technology, many businesses shy away from adopting AI into their practices, not fully understanding the benefits that they are missing out on. With greater knowledge and understanding as well as the support of a skilled driving force within a business, the rewards of Data Science can be reached relatively easily.
Businesses can develop, expand and secure their successes through the use of AI and through better understanding of Data Science. By establishing a sound foundation of knowledge within the company, the benefits of AI and Data Science are significant and often lead to competitive advantages for the firm.
The Main Business Benefits
There are essentially three key benefits of businesses of adopting AI and Data Science:
1.Increased Productivity
A recent article in Computerweekly detailed that Artificial Intelligence could Increase productivity within a business. Research suggests that software robots are likely to automate 80% of repetitive tasks that are currently being carried out by human workers in the near future. By freeing up these human employees, businesses benefit from AI efficiency and the ability to utilise the brainpower of their people. It was reiterated by Computerweekly that “Businesses will need to develop a balance of artificial and human intelligence as different roles require a mix of the two,” found the academic study by Goldsmiths, University of London and Artificial Intelligence (AI) supplier IPsoft. It said, “By automating and redeploying humans away from repetitive jobs to tasks that require creativity and innovation, organisations can increase productivity three times over.”
A business that recognises the wealth of benefits in embracing AI in its practices is one that will evolve structure to maximise the effectiveness of humans working alongside technology. CEO at IPsoft, Chetan Dube, has detailed that in order for businesses to capitalise on the productivity potential of AI, they must be prepared to reconfigure their structures and this is achieved, he says “with fundamental change to organisation structure, who they hire for which roles, and how they use the new relationship between humans and machines to maximise efficiency and innovation”.
2.Process Efficiencies
In the recruitment sector, a number of software vendors have already begun to incorporate AI algorithms into their tools, which allows them to automate some tasks including examining CVs, sending follow up communications or searching for potential candidates for a company’s job vacancy.
In a recent report by TNW, information was provided relating to the processes used by candidate relationship management software, Beamery. The programs benefit from machine learning software, which enables them to establish stronger applicant tracking systems with their clients whilst building relationships with potential candidates. Beamery’s AI conducts searches across social media platforms to find information and fill in data gaps in their candidates’ profiles. The company, who is used by Facebook amongst others, makes use of data mining algorithms to follow interactions between candidates and employers which enables them to engage with the best individuals. This allows businesses to maximise their recruitment methods without the need to hire costly and large teams.
3.Optimisation of Activity
One of the areas that is benefitted most significantly by AI in business is marketing. There are a multitude of ways in which Artificial Intelligence is being deployed in marketing and the advantages are huge. Some companies choose to utilise AI in marketing as and when needed and as a support mechanism for the physical marketing team. Cheryl Chavez of Marketoexplained at the Marketing Nation Summit that for their firm, “With the flip of a switch, I can decide if I want to make the content, channel and the cadence adaptive, then the adaptive engine will select the best content to send at the right cadence and to the channel best to reach the audience”.
Another prime example of AI incorporated into business is the use of Telematics in the insurance sector. With an expected 250 million connected cars on the roads by 2020, the opportunities for the automotive industry when incorporating data science into practices are mammoth. Many car insurers offer clients the opportunity to reduce their premium costs by committing to using a ‘black box’ that is placed in the customer’s car to track their driving habits. The insurance provider is able to access a wealth of valuable data from these boxes, which helps them optimise their operations. The data retrieved is crunched by data scientists and fed into AI models which in turn allows the insurance company the opportunity to improve their risk modelling, pricing and consumer profiling.
Knocking Down the Barriers to AI Adoption
In some cases, despite recognising the numerous benefits of implementing AI use in their company, businesses will still resist immediate adoption of AI, but why? There is still confusion amongst many about AI technology and terminology and this leads to delays and barriers to accessing and introducing new technology. Other barriers to adoption include expense, technology and the ability to integrate AI into the workplace.
The majority of concerns that seem to create the barriers to adoption are caused by a simple lack of understanding about AI, it’s use and it’s limitations. When this is coupled with a significant lack of talent in most firms to be able to drive AI projects, several businesses shy away. As explored in our blog ‘Business and Data Science — Managing Expectations’, businesses need to maintain realistic expectations about what can be achieved with AI, Data Science and the human resources that they have available. We detailed that realism is key to successful implementation of AI within business as “data scientists are there to not only uncover insights, but to also identify and address problem areas. Their role is complex, pressing and challenging — and you must provide the business support structures they need to thrive in their role”.
Data Scientists and machine learning experts are in high demand and having access to them is key to instrumental in implementing AI in any business. Once a company recognises that the cost and time investments required to adopt AI are surprisingly low, most companies jump at the opportunity to improve efficiency, performance and results.
The Way Forward
AI, Data Science and Machine Learning offer huge advantages to businesses, but often the lack of clarity around of what can be achieved, and how data science facilitates these achievements, means that several firms are still reluctant to get on board. Once a business recognises that the driving force for adoption comes from the top, with enthusiasm propelled by CEOs, CMOs and CFOs, a company can swiftly begin to assess and access the potential that lies within its data and software.
With surveys of business leaders highlighting that 35% of firms are not using AI because they had insufficient understanding, and 48% believing that the implementation costs would be too high, it’s time that each business invested in their knowledge and understood how quickly and simply they can be befitting from AI and data science rewards.
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