Analytics and AI: Humans and Machines are Good at Different Aspects of Prediction

Mostly driven by growth in the IoT, and the widespread use of internet, social media and mobile devices to perform search, send text, email and capture images and videos, the amount of data that we are producing on a daily basis is startling.

Consequently, companies are turning to data analytics and AI technologies to help them make sense of all the data at their disposal, predict the future, and make informed decisions that drive enterprise performance.

Although adoption of analytics and AI systems is increasingly extending in more mission-critical business processes, the implications of these emerging technologies on busines strategy, management, talent and decisions is still poorly understood.

For example, the single most common question in the AI debate is: “Will adoption of AI by businesses lead to massive human job cuts?”

Borrowing lessons from historical technological advances, yes, certain jobs will be lost, and new ones also created. However, machines are not taking over the world, nor are they eliminating the need for humans in the workplace.

Jobs will still be there albeit different from the traditional roles many are accustomed to. The majority of these new roles will require a new range of education, training, and experience.

For instance, nonroutine cognitive tasks demanding high levels of flexibility, creativity, critical thinking, problem-solving, leadership, and emotional intelligence do not yet lend themselves to wholesale automation.

Analytics and AI rely on data to make predictions

As more and better data is continually fed to the machine learning algorithms, the more they learn, and improve at making predictions.

Given these applications search for patterns in data, any inaccuracies or biases in the training data will be reflected in subsequent analyses.

But how much data do you need? The variety, quality and quantity of input, training and feedback data required depends on how accurate the prediction or business outcome must be to be useful.

Training data is used to train the predictive algorithms to predict the target variable, while the feedback data is used to assess and improve the algorithm’s prediction performance.

Undoubtedly, advanced analytics and AI systems are only as good as the data they are trained on. The data used to train these learning algorithms must be free of any noise or hidden biases.

You therefore need to understand how predictive technologies learn from data to perform sophisticated tasks such as customer lifetime value modeling and profitability forecasting.

This helps guide important decisions around the scale, scope and frequency of data acquisition. It’s about striking a balance between the benefits of more data and the cost of acquiring it.

Humans and machines both have shortcomings

In the context of prediction, humans and machines both have recognizable strengths and weaknesses.

Unless we identify and differentiate which tasks humans and machines are best suited for, all analytics and AI investments will come to naught.

For instance, faced with complex information with intricate interactions between different indicators, humans perform worse than machines. Heuristics and biases often get in the way of making accurate predictions.

Instead of accounting for statistical properties and data-driven predictions, more emphasis is often placed on salient information unavailable to prediction systems.

And, most of the times, the information is deceiving, hence the poor performance.

Although machines are better than humans at analyzing huge data sets with complex interactions amidst disparate variables, it’s very crucial to be cognizant of situations where machines are substandard at predicting the future.

The key to unlocking valuable insights from predictive analytics investments involves first and foremost understanding the definite business question that the data needs to answer.

This dictates your analysis plan and the data collection approaches that you will choose. Get the business question wrong, conclusively expect the insights and recommendations from the analysis to also be wrong.

Recall, with plentiful data, machine predictions can work well.

But, in situations where there is limited data to inform future decision making, machine predictions are relatively poor.

To quote Donald Rumsfeld, former US Secretary of Defense:

There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.

Donald Rumsfeld, former US Secretary of Defense

Thus, for known knowns, abundant data is readily available. Accordingly, humans trust machines to do a better than them. Even so, the level of trust changes the moment we start talking about known unknowns and unknown unknowns.

With these situations, machine predictions are relatively poor because we do not have a lot of data to ingest into the prediction model.

Think of infrequent events (known unknowns) that occur once in a while, or something that has never happened before (unknown unknowns).

At least for infrequent events or happenings, humans are occasionally better at predicting with little data.

Generally so because we are good at comparison and applying prudent judgement, examining new situations and identifying other settings that are comparable to be useful in a new setting.

We are naturally wired to remember key pieces of information from the little data available or the limited associations we have had in the past.

Rather than be precise, our prediction comes with a confidence range highlighting its lack of accuracy.

Faced with unknown unknowns, both humans and machines are relatively bad at predicting their arrival.

The simple truth is that we cannot predict truly new events from past data. Look no further than the current Brexit conundrum.

Nobody precisely knew the unintended consequences of the UK leaving the EU. Leavers and Remainers both speculated as to what the benefits and disadvantages of leaving the EU maybe.

Of course, nobody knows what will happen in the future but that doesn’t mean we can’t be prepared, even for the unknown unknowns.

In their book Prediction Machines: The Simple Economics of Artificial Intelligence, Ajay Agrawal, Joshua Gans, and Avi Goldfarb present an additional category of scenarios under which machines also fail to predict precisely – Unknown Knowns.

Per the trio:

Unknown knowns is when an association that appears to be strong in the past is the result of some unknown or unobserved factor that changes over time and makes predictions we thought we could make unreliable.

PREDICTION MACHINES: THE SIMPLE ECONOMICS OF ARTIFICIAL INTELLIGENCE

With unknown knowns, predictive tools appear to provide a very accurate answer, but that answer can be very incorrect, especially if the algorithms have little grasp of the decision process that created the data.

To support their point of view, the authors make reference to pricing and revenue analysis in the hotel industry, although the same viewpoint is applicable elsewhere.

In many industries, higher prices are analogous to higher sales, and vice versa.

For example, in the airline industry, airfares are low outside the peak season, and high during peak seasons (summer and festive) when travel demand is highest.

Presented with this data, and without an understanding that price movements are often a function of demand and supply factors, a simple prediction model might advocate raising different route airfares to sell more empty seats and increase revenues. Evidence of causal inference problems.

But, a human being with a solid understanding of economics concepts will immediately call attention to the fact that increasing airfares is unlikely to increase flight ticket sales.

To the machine, this is an unknown known. But to a human with knowledge of pricing and profitability analysis, this is a known unknown or maybe even a known known provided the human is able to properly model the pricing decision.

Thus, to address such shortcomings, humans should work with machines to identify the right data and appropriate data analysis models that take into consideration seasonality and other demand and supply factors to better predict revenues at different prices.

As data analytics and AI systems become more advanced and spread across industries, and up and down the value chain, companies that will progress further are those that are continually thinking of creative ways for machines to integrate and amplify human capabilities.

In contrast, those companies that are using technology simply to cut costs and displace humans will eventually stop making progress, and cease to exist.

Thanks for sharing:

Leave a Reply

Your email address will not be published.

Subscribe to get notified of new posts by email

Recent Posts

Categories

Leading in Uncertain Times

One of the biggest challenges facing business leaders today is making the right decisions that will ensure their organizations succeed, survive, and remain competitive in an increasingly uncertain and complex environment.

A recent post, The best way to lead in uncertain times may be to throw out the playbook, by Strategy+Business has several good points.

The article is about the COVID-19 pandemic, how global companies navigated through the crisis, and how best to prepare for future disruptions. Here are some key points and my comments.

  • Rather than follow a rigid blueprint, executives must help organizations focus on sensing and responding to unpredictable market conditions.
    • Comment: Senior leaders play a vital role in providing clarity about the organization’s strategic direction, creating alignment on key priorities to ensure the achievement of enterprise objectives, and ensuring the business model is continuously evolving to create and capture value in the face of uncertainty. They must not rest on their laurels and stick to the beliefs and paradigms that got them to where they are today and hope they will carry them through tomorrow. Regulatory changes, new products, competition, markets, technologies, and shifts in customer behavior are upending many outdated assumptions about business success. Thus, the businesses you have today are different from the ones you will need in the future hence the importance of continuously sensing changes in the global economy. Employees and teams often feed off the energy of their leaders and tend to focus their attention where the leader focuses attention. If the leader is comfortable with current business practices and rarely embraces the future or challenges the status quo, then the team is highly likely to follow suit.
  • When it became clear that supply chains and other operations would fracture, organizations began scenario planning to shift production sources, relocate employees, and secure key supplies.
    • Comment: Instead of using scenario planning to anticipate the future and prepare for different outcomes, it seems most of the surveyed organizations used scenario planning as a reactionary tool. Don’t wait for a crisis or a shift in the market to start thinking about the future. The world is always changing. As I wrote in The Resilient Organization, acknowledge that the future is a range of possible outcomes, learn and develop capabilities to map out multiple future scenarios, develop an optimal strategy for each of those scenarios, then continually test the effectiveness of these strategies. This does not necessarily mean that every change in the market will impact your business. Identify early warnings of what might be important and pay closer attention to those signals. In other words, learn to separate the signals from the noise.
  • The pandemic forced the organization’s senior management team to re-examine how all decisions were made.
    • Comment: Bureaucracy has for a very long time stood in the way of innovation and agility. To remain innovative and adapt quickly in a fast-changing world, the organization must have nimble leadership and an empowered workforce where employees at all levels can dream up new ideas and bring them to life. Identifying and acting on emerging threats and potential opportunities is not the job of the leader alone but every team member. To quote Rita McGrath, in her book Seeing Around Corners, she writes, “Being able to detect weak signals that things are changing requires more eyes and ears throughout the organization. The critical information that informs decision-making is often locked in individual brains.” In addition to the internal environment, the leader must also connect with the external environment (customers, competitors, regulators, and other stakeholders), looking for what is changing and how.
  • It’s worthwhile for leaders of any team to absorb the lessons of sense-respond-adapt, even if there is no emergency at hand.
  • Sensing: Treat the far-flung parts of your enterprise as listening stations. The question leaders must ask is, “What are we learning from our interactions beyond the usual information about costs and sales?” Train your people to listen for potentially significant anomalies and ensure that important information is not trapped in organizational silos.
    • Comment: Cost and sales data are lagging indicators that reveal the consequences or outcomes of past activities and decisions. Although this information can help leaders spot trends by looking at patterns over time, it doesn’t help understand the future and inform what needs to be done for the numbers to tell a different story. In addition to lagging indicators, pay attention to current and leading indicators and understand the relationship between these indicators and outcomes.
  • Responding: Improve communication across intra- and inter-organizational boundaries. Leaders should view business continuity as an essential function that acts as connective tissue for the enterprise.
    • Comment: In addition to creating mechanisms that allow the free flow of information both inside and outside the organization, decision-makers should also be comfortable receiving information that challenges their personal view of the world, even if it’s not what they want to hear. Create a culture of psychological safety where people are not afraid to share bad news for fear of getting punished, but rather are acknowledged and rewarded for speaking up. Leveraging the diversity of thought enables leaders to anticipate the future as an organization, decide what to do about it collectively, and then mobilize the organization to do what’s necessary.
  • Adapting: Challenge assumptions, and question orthodoxies. There’s always the temptation to mitigate threats simply by applying existing practices harder and faster. One way to get at those deeper issues and encourage double-loop learning is to ask, “What needs to be true for this to be the right approach?”
    • Comment: In an increasingly uncertain environment, it’s difficult to survive and thrive with an old business model or outdated technologies. Many businesses fail because they continue doing the same thing for too long, and they don’t respond quickly enough and effectively when conditions change. As a leader, stay curious and connected to the external environment, look for market shifts, understand what needs to be regularly refreshed and reimagined, adopt new technologies and capabilities, and adapt in ordinary times but also during times of transition. Unfortunately for many leaders, it’s just more convenient for them to continually downplay the fact that conditions are changing than take the appropriate course of action that drives business success.

How are you preparing your organization for potential future disruptions?

Thanks for sharing:

The Collaborative Organization

These days the term collaboration has become synonymous with organizational culture, creativity, innovation, increased productivity, and success.

Let’s look at the COVID-19 pandemic as an example. At the peak of the crisis, several companies instructed their workers to adopt remote working as a health and safety precautionary measure.

Two years into the pandemic, they are now asking their employees back to the office full time or are planning to adopt a hybrid model.

The need to preserve our collaborative culture and accelerate innovation are two of the top benefits being cited by organizational and team leaders for bringing workers back.

Collaboration is indeed essential for the achievement of team goals, functional objectives, and the overall success of the organization.

Today’s breakthrough innovations are emerging from many interacting teams and collaborative relationships.

When teams, functions, and organizations collaborate, the whole is greater than the sum of its parts; group genius emerges, and creativity unfolds.

But, what makes a successful collaboration? What are the key enabling conditions?

  • It extends beyond the boundaries of the organization. Business success is a function of internal and external relationships. Instead of viewing your business in vacuo, understand that you are part of an ecosystem. External to your organization, who do you need to partner with to enhance your value creation processes, achieve/exceed your objectives, or successfully execute your strategy?
  • Ensure the objectives are clear and there is shared understanding by everyone. Unclear objectives are one of the topmost barriers to team and organizational performance.
  • Foster a culture that encourages opinions and ideas that challenge the consensus. People should feel free to share their ideas and not hold back for fear of others penalizing them or thinking less of them. Collaboration is hindered when one or two people dominate the discussion, are arrogant, or don’t think they can learn anything from others.
  • Groups perform more effective under certain circumstances, and less effective under others. There is a tendency to fixate on certain topics of discussion amongst groups which often leaves members distracted from their ideas. To reduce the negative effects of topic fixation, members of the group should be given periods to work alone and switch constantly between individual activity and group interaction.
  • Effective collaboration can happen if the people involved come from diverse backgrounds and possess complementary skills to prevent conformity. The best collective decisions or creative ideas are often a product of different bodies of knowledge, multiple opinions, disagreement, and divergent thought processes, not consensus or compromise.
  • New technologies are making collaboration easier than ever, enabling us to increase our reach and broaden our network. Although new technology helps, it will not make your organization collaborative without the right culture and values in place. First, define what you want to achieve through collaboration then use these tools to promote creative collaboration.

How else are you championing collaboration within your organization to create value and succeed?

Thanks for sharing:

Preparing for Geopolitical Shocks

Geopolitical instability has steadily increased over the past years, and uncertainty in the global economy is at an all-time high. Thanks to globalization and advances in technologies, we now live and work in a tightly interconnected world, one in which the boundaries that previously separated domestic from global issues have disappeared.

Threats are no longer confined to traditional political borders, social structures, and geographic boundaries. Geopolitical shifts have dramatically altered the global economic landscape and brought politics and business together.

The rise of China as an economic and politically influential power has threatened the dominance of the United States as the world’s largest economy. Although the opening of China and a market of 1.4 billion people have benefited both countries, it has also intensified competition and sparked U.S. economic and technological espionage accusations against China, leading to strained relations between the two giants.

U.S. companies operating from China have felt the impact of this tense relationship. The opposite is true for Chinese companies in the U.S.

Across Europe, national populism is on the rise and now a serious force. In 2016, the United Kingdom shocked the world when it voted to leave the European Union, generating reverberating effects across markets.

Banks and financial services companies that once benefited from the EU passporting system have had their cross-border banking and investment services to customers and counterparties in the many EU Member States impacted, causing them to reimagine their value proposition models.

The recent invasion of Ukraine by Russia is another example of a geopolitical event that has had devastating effects on human livelihood and businesses. Although the conflict between the two countries has risen over the years, I think it’s fair to say that few political analysts, governments, and businesses predicted a war to happen.

The war has created a humanitarian crisis, rattled global commodity and energy markets, caused prices to soar, and forced many international companies to temporarily suspend their Russian activities or completely cut ties with the country.

Global supply chains which are already fragile and sensitive due to the COVID-19 pandemic are now facing new challenges in the aftermath of the Russia-Ukraine crisis. Multilateral economic sanctions have been imposed on Russia. A state of affairs that was unthinkable months ago and is now threatening to derail the nascent global economic recovery from the COVID-19 pandemic.

Given the global domino effect of geopolitical events and the shrinking of the distance between markets and politics, the need to better understand and more effectively mitigate geopolitical risk has become more urgent. The business impacts, whether direct or indirect, vary by company type and industry sector.

Your company may not be able to prevent wars between nations, but you can anticipate and better prepare for geopolitical shocks:

  • Integrate strategy, risk, and performance decision-making. Consideration of risks to business success is an important part of the strategy selection and execution process, not an afterthought.
  • Develop a better understanding of geopolitical trends and how they are changing. For example, what are the megatrends in business, politics, and technology that are making geopolitical risks more diverse, prevalent, and consequential?
  • Assess the links between these geopolitical events and business performance. What are the events that matter most to your business? For example, how might current global political trends pose physical, business, and reputational risks to your parent organization?
  • Anticipate how these trends are likely to play out in the short, medium, and long terms, and develop mitigation strategies for each geopolitical scenario. Proactively anticipate and plan for radically different worlds, instead of reacting to problems as they arise
  • Review your mitigation strategies as the world changes. Are they effective enough in case of a major shock?
  • Develop capabilities for continuous learning to anticipate, address, and recover from geopolitical crises.

What do you think?

Thanks for sharing: