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Artificial intelligence (AI) is a divisive topic, and many myths have emerged in the media, on social platforms and even in academia. Some people fear that implementing AI in business would end the need for any human workforce, while others see nothing more than a buzzword. It can be hard to separate fear mongering from fact.

Many business leaders are still struggling to distinguish the difference between deep learning, machine learning and AI, let alone this technology’s ability to help or hurt their organisation. It’s understandable really. There are many definitions floating around, but we want to make it simple.
  • AI - Any technique that enables computers to mimic human intelligence, using logic, it-then rules, decision tree, and machine learning (including deep learning).
  • Machine learning - A subset of AI that includes abstruse statistical techniques that enable machines to improve at tasks with experience. The category includes deep learning. For example, Netflix uses machine learning to suggest shows and learn from viewing habits.
  • Deep learning - The subset of machine learning composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data. For example, virtual assistants use deep learning to understand language, speech patterns and accents.

To reap the benefits of AI in business, subject matter experts (that’s you) first need to know what it is. “Business leaders are often confused about what AI can do for their enterprise,” explains Alexander Linden, Gartner’s vice president analyst.

"With AI technology making its way into the organization, it is crucial that you fully understand how AI can create value for your business — and where it cannot.”

Myth No. 1: AI means robots

AI isn’t an autonomous force, out to take over the world and establish a robotic reign. While it is true that AI-enabled prosthetics are in development, these robotic limbs aren’t about to run off of their own accord.

Facebook engineers and computer scientists from New York University have recently succeeded in using AI to teach a robotic arm to grasp objects within tens, rather than hundreds or thousands, of attempts. This brings with it plenty of valuable real-world applications, but it’s hardly I, Robot.

While walking, talking robots are still very much an emerging trend in AI research and development, data science professionals are already using AI in business, with huge potential for virtually every industry sector.

Myth No. 2: AI thinks like humans

Although some elements of AI have been inspired by the way the human brain works, it’s far from equivalent. The roles of AI in business are virtually limitless when it comes to data analysis, but where human intelligence involves many subtle nuances, AI cannot see beyond statistics.

For instance, say a retail brand builds a machine learning model to learn more about product returns. A range of customer order data is inputted, and the output suggests that a customer is statistically more likely to return a product if they have printed out a returns label. While this is technically correct, it doesn’t provide any predictive power. 

For this reason, organizations still need human intelligence even if they are adopting AI in business models. Without subject matter experts to input the right information, monitor the model’s outputs and guide its learning process, AI won’t get very far at all.

Myth No. 3: AI can teach itself

A good machine learning model can give the impression of autonomy, but this is only the case after expert data scientists have laid a lot of foundations. Subject matter experts are needed to outline the problem, prepare data, determine data sets, remove potential bias and more.

Even after all this is done, humans are still needed to continually guide and correct the machine learning model as it takes in new data and moves onto the next learning cycle. In order to successfully use AI in business processes, organizations must have a data science team in place to take this role.

Myth No. 4: AI are entirely objective

Arguably, all humans are intrinsically biased. We can get all philosophical about the personal experience always impacting on our understanding of the world. Surely one of the biggest benefits of AI in business is its entirely objective approach, right?

Wrong. AI relies on data, guidance and other input from human experts, and so is just as prone to bias as we are. Systems that are frequently retrained, or use variable inputs such as data from social media, are especially vulnerable both to unintentional influences and malevolent forces.

There’s no proven path to completely banish bias, though human teams can employ strategies to ensure it is minimised, such as ensuring diversity and internally reviewing the work of other members.

Myth No. 5: AI in business will take everyone's jobs

Human intelligence is key to organizations successfully using AI in business, and many expect to see only a minor impact on staff headcount in the future. In fact, far from robots replacing employees, the most digitized companies are more likely to expect their human workforce to increase in size.

However, these humans will need a new skill set to interpret and utilise the information provided by AI. Data scientists are in high demand, making recruitment and retention the biggest barriers to AI adoption even for industry-leading organizations. Upskilling current employees is another option, but this still makes heavy demands on business resources.

Mind Foundry believes this gap could be filled by citizen data scientists, whose primary job functions lie outside of statistics. By giving individuals the tools and technology to use AI in business processes within their usual remit, companies could solve workforce shortages.

Myth No. 6: AI will only impact the most mundane jobs

On the other hand, the potential benefits of AI in business go far beyond simply replacing the most mundane jobs. On the contrary, AI can enable organisations to augment all manner of complex processes and better support their workforce. 

Mind Foundry has proven its value across multiple sectors including medicine, marketing and finance. We used open source data to train machine learning models that could predict adverse drug reactions, prioritise cold calls to customers and forecast stock price swings. These test cases revealed some interesting results, but would likely prove far more insightful in the hands of subject matter experts.

AI can process data, detect patterns and identify trends faster than humans, that’s just a fact. It’s how you utilise AI in business and act on its outputs that will determine the benefits you see.

Myth No. 7: AI is only relevant for cutting-edge technology companies

Far from being a niche technology organizations across every industry sector can use AI in business. The potential impact of AI on core business problems can’t be understated, and adoption is growing fast. In the near future, avoiding AI could actually leave organizations at a competitive disadvantage.

If your organization is ready to adopt AI, get in touch with Mind Foundry to learn more about our automated machine learning platform.

Achieve a competitive edge with machine learning: A downloadable guide

Paul Reader

Written by Paul Reader

Paul Reader is the CEO of Mind Foundry and a tech veteran with more than 25 years of experience in industry. Prior to his position as CEO at Mind Foundry, Paul set the strategic direction focused on ensuring sustainable growth and long-term shareholder value as Chief Strategy Officer and EMEA GM for Silicon Valley VC-backed digital marketing platform Wayin. Before that he was Global Head of Strategy and Organisation Development for $6BN of start-up technology assets that he integrated into the Marketing Cloud business unit at Oracle. He joined Oracle as part of the $871M acquisition of marketing automation platform start-up Eloqua where he was responsible for growth strategies as Global Senior Director of Strategic Initiatives (Corporate Development).