If your business knows AI can bring value but is unsure how to begin applying it to your situation Wu provides an excellent stepping-in point for you. After all, if you want to maximise profit you need to cut costs or increase revenue and when it comes to pricing you no longer need to roll the dice; instead, you can apply a wealth of AI experience to your own data to monetise more efficiently and maximise your profits.

PROS is a cloud software company powering the shift to modern commerce by helping companies create personalised and frictionless buying experiences for their customers. PROS applies machine learning to dynamic pricing science and makes it possible for companies to price, configure, and sell their products in an omnichannel environment with speed, precision, and consistency.

Michael Wu, its chief AI strategist, is one of the world's premier authorities on AI, machine learning (ML), data science, and behavioural economics. He explains, “AI can ingest vast data sets to get a comprehensive understanding of the market landscape, the competitive environment, and buyers’ willingness to pay.”

"AI has the ability to consider a huge number of attributes in the input data and make a price recommendation that is truly optimal among multiple dimensions,” Wu said. “When AI is trained using user behaviour data, it gains a comprehensive understanding of user preferences and is able to recommend contents, products, or services that are optimised for the individual. The use of AI also supports sales efficiency because AI typically automates mundane, repetitive, and error-prone tasks that often take a long time for humans to complete. Using AI can automate these tasks with speed, scale, and precision, leading to an acceleration of the slow processes in long sales cycles and therefore more efficiency.”

In fact, not only is pricing optimisation something you can apply to your data right now, it will be the most impactful type of technology investment in 2022, Wu says.

"Pricing optimisation, personalisation, and sales efficacy tools like configure, price, quote (CPQ) have direct impacts on revenue, customer experience (CX), and operational efficiency respectively,” He said.

“When they are properly adopted, these solutions can add value and make a direct impact very quickly - that is, they have very fast time-to-value. If the sales team adopts the price recommendation from pricing optimisation solutions, the impact on revenue and margin is immediate. When a personalisation tool is adopted by consumers, users can feel the improved customer experience right away. And when sales efficacy tools are adopted, the sales team will immediately operate more efficiently,” Wu said.

Of course, while AI can process mass volumes of data, there are other things to consider. One of these is alleviating any sort of bias, and drawing upon aspects of “human intelligence” to keep your AI on track.

"Bias in AI and ML models typically arises when the data used to train AI is biased,” Wu said. “If we can ‘fix’ the training data, we can fix the majority of the bias problems. Human analysts must examine the training data and analyse the entire population of training data to identify potential biases in any of the protected attributes, such as race, or gender, before the training data is used to train the AI.”

“Examining a small sample of the training data won’t reveal the inherent biases, as they are unobservable on an individual basis. After examining the full data population, if biases occur, analysts must work with data engineers to re-engineer an unbiased training data set for training AI. Since AI learns and is often re-trained periodically, these analytics report serves as a monitoring system to ensure the training data stay within the predefined threshold of being unbiased,” Wu said.

Additionally, businesses must uphold ethical standards and responsibility when it comes to AI.

“It is absolutely crucial to uphold the ethical standard and responsibility to the use of AI because that builds trust among users and business leaders,” Wu said.

“Although AI can drive tremendous value in their respective return on investment - like margin improvement, CX, and sales efficiency - it will only do so if humans adopt and use these AI tools. And humans will adopt these tools only when they feel that they can trust them. Without trust, humans won’t use these tools, and there will be no return on investment or business impact felt at all,” Wu explained.

Along with ethics and eliminating bias, it's also important to have the right teams of people who will uphold ethical AI values from end-to-end integration.

"Deploying teams who are dedicated to upholding ethical standards and responsible use of AI will help build trust, combat a fear of AI, and provide more transparency to how AI makes its decisions,” Wu said.

"Ultimately, the right team of people will ensure that AI not only functions as intended but also mitigate the potential negative side effects, which will eventually build trust in AI for its intended users,” he explained.

Business and IT leaders around the world know the opportunity exists to unlock business value by harnessing the power of AI. If your company is still looking for a way to make your start, pricing optimisation is a great area to start. Wu has advice on how you can begin.

"AI adoption is a very long, multi-year journey. I’ve developed a six-stage, vendor-agnostic, and use case-agnostic AI adoption maturity model that outlines this process,” he said.

"Depending on the maturity level of the company, they will need to focus on something different. For beginners who haven’t embarked on this journey, I would recommend they start collecting data on their business operations.”

"AI is essentially fueled by data, so having big data about their business captured and stored will allow these companies to have the raw materials needed to feed the AI, to use as training data to train the AI. Due to the recent pandemic, most organisations have accelerated their digital transformation.”

"This is good news," Wu said, "because this is precisely the first stage of the AI adoption maturity journey, which involves the digitisation of business processes.”