Question

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Watch the interview with PWC’s Gerard Verweij at the Bloomberg website and answer the following questions:

1. According to Gerard Verweij, global data and analytics leader at PriceWaterhouseCoopers (PwC), what impact will AI have on jobs and productivity and consumption? Where is talent needed to advance and leverage data analytics and AI in business?

2. what other challenges do you see for the advancement of audit data analytics (ADA) through AI?

Answer 1

AI (Artificial Intelligence):

In simple words we can say that Artificial Intelligence refers to making the technology capable of doing the human works. It targets at mechanising the works by introducing more and more technology in the place of those works which were previously carried on by the human beings.  AI is a term that encompasses multiple types of computerized programs. In fact, someone talking about AI can be discussing anything from facial recognition tools powered by neural networks to machine learning that predicts the best word to use in a subject line to improve email open rates. According to Talent Tech Labs, "To be true AI, the technology must first learn from known inputs, then derive additional layers of abstraction to reach predictions that refines itself as the machine learns." The two types of AI technology most applicable to the business field today include:

• Machine Learning — a type of AI that provides computers with the ability to learn without being explicitly programmed. It works by examining large volumes of data and uses patterns in that data to improve a program's understanding and resulting predictions. An example of this is your favorite video streaming service being able to predict what movies you will enjoy based on your previous views and activity.
• Natural Language Processing — technology that reads, understands and responds to conversational language. An example is a chat bot. It's valuable because it can extract data from ambiguous, unstructured data sets like conversations. Elements such as context and tone can also be interpreted by the computer. Common applications of this software today include translation and speech recognition.

Impact of AI on Jobs, Productivity and Consumption:

On a day by day basis, we come to hear news about introduction of robots in different jobs, assigning them different roles and duties and replacing humans with them.  However, while there may be some jobs that are changed or eliminated, others will be created or enhanced.

Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.

"Artificial intelligence is kind of the second coming of software," said Amir Husain, founder and CEO of machine learning company SparkCognition. "It's a form of software that makes decisions on its own, that's able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software." Those traits make artificial intelligence highly valuable throughout many industries, whether it's simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs. Machine learning is used often in systems that capture vast amounts of data. For example, smart energy management systems collect data from sensors affixed to various assets. The troves of data are then contextualized by machine learning algorithms and delivered to human decision-makers to better understand energy usage and maintenance demands.

Artificial intelligence is even an indispensable ally when it comes to looking for holes in computer network defenses, Husain said.

"You really can't have enough cybersecurity experts to look at these problems, because of scale and increasing complexity," he said. "Artificial intelligence is playing an increasing role here as well."

Artificial intelligence is also changing customer relationship management (CRM) systems. Software like Salesforce or Zoho requires heavy human intervention to remain up to date and accurate. But when you apply artificial intelligence to these platforms, a normal CRM system transforms into a self-updating, auto-correcting system that stays on top of your relationship management for you. [For those in brand-new companies, read our report on CRM tools for startups.]

Another example of artificial intelligence's versatility is within the financial sector. Dr. Hossein Rahnama, founder and CEO of artificial intelligence concierge company Flybits and visiting professor at the Massachusetts Institute of Technology, worked with TD Bank to integrate artificial intelligence into regular banking operations, such as mortgage loans.

"Using this technology, if you have a mortgage with the bank and it's up for renewal in 90 days or less … if you're walking by a branch, you get a personalized message inviting you to go to the branch and renew purchase," Rahnama said. "If you're looking at a property for sale and you spend more than 10 minutes there, it will send you a possible mortgage offer.

"We're no longer expecting the user to constantly be on a search box Googling what they need," he added. "The paradigm is shifting as to how the right information finds the right user at the right time."

Skills That Matter in an Al-Driven Workplace

According to Pew Research Center, nearly two-thirds of Americans say computers will take over the work of humans, but paradoxically, 80 percent of Americans say it wouldn't affect their own jobs. People think their work is immune to this technological advance, but it simply can't be true that all jobs are going to remain unchanged by these new tools. Hiring, engaging and retaining a skilled workforce is an essentially human process. While there is incredible value to having tools — whether artificially intelligent or not — involved in the workflow, there's also value to personal connection and the human touch. For example, when you have a problem with your power, cell service or home internet connection, would you rather be directed to an automated message or speak to a human? The same is true for candidates and employees — they need human interaction, even if it's augmented by technology. Historically, changes in technologies lead to changes in skills and competencies. The following five skills could become increasingly valuable as more and more work is automated:

• Dealing with ambiguity
• Understanding the emotions of others
• Leveraging credible expertise
• Determining reliability of information sources
• Influencing others

Overall, the important message to gather is while the changes that AI could bring are not yet fully understood, we know it has the capacity to change work as the world knows it. By understanding some of the areas of impact and ensuring the right skills are prioritized in your employees, you can maintain a proper balance of relying on machines and algorithms to handle some tasks while keeping the "human" element alive in your workforce.

Challenges for Artificial intelligence in the coming future:

1. Data quality and quantity : the quality of the system relies heavily on the data that’s fed into it. AI systems require massive training datasets. Artificial intelligence learns from available information in a way similar to humans, but in order to identify patterns, it needs much more data than we do. The missing parts may be some publicly available information that the system will have easy access to, or you may have to buy data from third parties. Some types of data may are still difficult to obtain, e.g. clinical data that would allow more accurate treatment outcomes predictions.
2. Data labeling : Nowadays, with the Internet of Things (IoT) a large share of the data is made up of images and videos. There’s nothing wrong with that, and it may seem like there’s no problem here, but the thing is that many of the systems utilizing machine learning or deep learning are trained in a supervised way, so they require the data to be labeled. Incase the data is wrongly labelled, the system might wrongly take that information and makes analysis and interpretation with the helo of the same.
3. Explainability : If AI decides that a patient has the flu, it will also show which pieces of data led to this decision: sneezing and headaches, but not the patient’s age or weight, for example. When we’re given the rationale behind the decision, it’s easier for us to assess to what extent we can trust the model.
4. Case-specific learning : Our intelligence allows us to use the experience from one field to a different one. That’s called the transfer of learning – humans can transfer learning in one context to another, similar context. Artificial intelligence continues to have difficulties carrying its experiences from one set of circumstances to another.
5. Bias : AI makes decisions based on the available data only. It doesn’t have opinions, but it learns from the opinions of others. And that’s where bias happens. Bias can occur as a result of a number of factors, starting with the way of collecting data. If the data is collected by means of a survey published in a magazine, we have to be aware of the fact that the answers (data) come only from those reading said magazine, which is a limited social group. In such a case, we can’t say that the dataset is representative of the entire population.
6. Lack of understanding of AI among non-technical employees : AI implementation requires the management to have a deeper understanding of current AI technologies, their possibilities and limitations. The lack of AI know-how hinders AI adoption in many fields. Another common mistake that is caused by the lack of understanding is working towards impossible goals.
7. Scarcity of field specialists : In order to develop a successful AI solution, you need both the technical knowledge and business understanding. Unfortunately, it’s often one or the other. CEOs and managers lack the technical know-how necessary for AI adoption, while many data scientists aren’t very interested in how the models they develop will be used in real life. The number of AI experts that will know how to apply the tech to a given business problem is very limited. So is the number of good data scientists in general.
8. Lack of business alignment : Company culture not recognizing needs for AI and difficulties in identifying business use cases are among the top barriers to AI implementation. Identifying AI business cases requires the managers to have a deep understanding of AI technologies, their possibilities and limitations. The lack of AI know-how may hinder adoption in many organizations. Some companies jump on the AI bandwagon with too much optimism and no clear strategy. AI implementation requires a strategic approach, setting objectives, identifying KPIs, and tracking ROI. Otherwise, you won’t be able to assess the results brought by AI and compare them with your assumptions to measure the success (or failure) of this investment.
9. Difficulty assessing vendors : Just as in the case of hiring data scientists, when you’re lacking the technical know-how, you can be easily fooled. AI for business is an emerging field and it’s especially vulnerable as a large number of companies exaggerate their experience and in reality, they may not know how to use AI to solve actual business problems.
10. Integration challenges : Integrating AI into your existing systems is a process that is more complicated than adding a plugin to your browser. The interface and elements to address your business needs have to be set up. Some rules are hard-coded. We need to consider data infrastructure needs, data storage, labeling, feeding the data into the system. Then, there’s model training and testing the effectiveness of the developed AI, creating a feedback loop to continuously improve models based on people’s actions, and data sampling to reduce the amount of data stored and run models more quickly while still producing accurate outcomes.