According to the recently published PwC’s 22nd Annual Global CEO Survey, 85% of the surveyed CEOs overwhelmingly agree artificial intelligence (AI) will have a significant impact on their business within the next five years. For this reason, they have plans to pursue AI investments.
This is despite the fact that the information gap between the data CEOs are requiring to make informed decisions and what they are getting from their teams has not closed. Lack of analytical talent, data siloing and poor data reliability are the primary reasons the data they receive is inadequate.
Nonetheless, the application of AI and the underlying cognitive technologies such as machine learning, computer vision, natural language processing (NLP), audio and signal processing, speech recognition, predictive systems and robotics are wide-ranging, with the potential to improve performance in nearly any activity that generates large amounts of data.
Highly-powered algorithms which are the basis of these computer systems are presented with large amounts of data and subjected to supervised, semi-supervised, unsupervised, reinforced and deep learning.
The goal is to train the algorithms to identify relationships or patterns between the inputs and the outputs and use those rules to predict future outcomes with input data alone.
For example, in healthcare, AI is being used to study patient clinical data and recommend diagnoses. In finance, machine learning algorithms are being used to analyze transactions and uncover fraud and money laundering.
In the retail industry, predictive algorithms are being trained to automatically group customers into various categories based on their needs or buying patterns. These insights are then used to prioritize sales efforts and tailor promotions.
In other cases, companies have piloted NLP technology to monitor social media sentiment. The technology automatically identifies conspicuous topics of consumer conversations and sentiment surrounding those topics.
The generated insights are being used to influence decisions on improving marketing and customer service.
It is no surprise then that CEOs are now exploring how to implement these new technologies in their business.
Hype-driven or well-informed investments?
As much as AI is a source of significant business opportunities, the same technology is also a source of significant threats that must be evaluated. This is essential for helping leaders make informed and intelligent investment and risk decisions.
It is foolhardy for leaders to jump on the AI bandwagon and expect to capture the promises of AI and cognitive technologies if they lack an understanding of whether, how, and where to invest in applying these technologies.
When almost everyone is talking about the opportunities of AI and cognitive systems, it’s easy to cave in to hype-driven or ill-informed investments and overlook the fact that AI and cognitive technologies are not the solution to every business problem or situation.
That is why it is critical to evaluate the business case for investing in these technologies and assess the potential impact on your company’s business model, culture, strategy and sector.
Take a holistic view of your business processes, products and markets to weigh where the use of AI maybe be practical, profitable and crucial.
Algorithms are only as good as the data they learn from
Given that AI capabilities are data-driven, closing the information and talent gaps is key to unlocking AI’s potential. AI-powered algorithms improve over time through their experience of using data.
They learn relationships between variables in historical data sets and their outcomes. The relationships are used to develop models, which in turn are used to used to predict future outcomes without needing to be explicitly programmed by a programmer.
The systems change and evolve depending on the data that is fed to the algorithms. This therefore requires the data that is fed to the systems to be accurate, complete, diverse, and free from errors and bias. If the data is incomplete, error-prone or contains innate bias, the algorithms are likely to display false patterns as well as magnify the bias leading to misleading outcomes that have far-reaching repercussions.
Since AI and cognitive technologies deliver outcomes based on historical or existing data presented to them, leaders need to acknowledge that these systems will not necessarily provide flawless outcomes.
That is why it is critical to have appropriate data governance structures and talent in place to monitor where and how these technologies are deployed across the organization.
Skilled personnel play the critical role of overseeing biases and risks emanating from algorithms. For example, these people help identify and mitigate risks associated with programming errors.
Understand the black box of AI
As business leaders lay the foundation to pursue AI investments and entrust key decision making processes to intelligent machines, it is worthy to demystify the ‘black box’ of AI.
This is the notion that we can understand the inputs and outputs of an AI-powered system, but don’t understand what happens inside.
Accountability is an important element of decision making, and in order to make AI systems accountable for their decisions, AI-based decisions need to be explainable in order to be trusted.
Rather than blindly entrust machines to make important decisions, leaders therefore need to develop an understanding of how the technology works and how it makes decisions.
Thus, business leaders must be able to identify and explain the layers of decision making which underpin the operation of the systems and influence the final outcomes.
For example, are you able to identify and explain which connections have predictive value in the multilayered deep neural networks? Although it’s impossible to analyze all the connections in a deep neaural network, it’s important to prioritize what you need to know, what you want to understand, and why.
Over time, through testing and measuring, or trial and error, you will be able to understand the thought process behind algorithms, trust the decisions they make and ensure a robust governance structure is in place to monitor these technologies as they mature.
Surge in Cyber Attacks
Big data has been a boon to the development of AI and cognitive technologies. Thanks to advances in technology, our digital lives are producing staggering amounts of data each day.
As a result, interest in AI application is surging as decision makers try to make sense of all the data at their disposal.
Nevertheless, leaders need to be aware that the more data is generated the higher the probability of cyber criminals or hackers targeting the company’s AI systems to steal personal data or business confidential information.
A major data breach can have unintended consequences that can create legal, brand and public relations issues for the business.
Therefore, as leaders seek to capture the opportunities of AI and cognitive technologies, they mustn’t turn a blind eye to the limitations of these systems.
They must also consider the various ethical, moral, and legal issues associated with the AI systems that their organizations deploy.
To be highly effective the implementation of AI is a multi-discipline exercise that will require resources from many parts of the business. A good example of this can be illustrated when using AI to assist in auto invoicing and payment applications. The sales department, manufacturing and shipping departments will provide data that allows these two functions to operate autonomously. The data from these departments will be incorporated into algorithms that function to determine how much, when and to whom to send an invoice; and, how to apply payments when the bank reports them as received.
In addition to the speed factor, AI now is being used to replace repetitive, linear tasks and increase our information output capacity. Both uses have wide implications for the CFO, including;