The Artificial Intelligence revolution in the enterprise is well under way. According to Gartner’s 2018 CEO and Senior Business Executive Survey, 65% of respondents think that AI will have a ‘material impact on an area of their business’. Due to the combination of three critical factors – improved data availability and machine learning techniques, increased computing power and storage, and a strong enterprise thrust on data-driven decision-making – AI has taken a strong foothold in some of the largest corporations in the world today, commanding executive-level interest, attention and urgency.

Beyond simple automation, AI is powering complex, critical decisions in several areas from Renaissance Technologies’ Medallion Fund, which uses statistical probabilities and quantitative models and has become one of the startling successes in the hedge fund industry, to complex image annotation and deep learning that helps radiologists detect cancer in MRI scans. Here is a look at some of the critical areas where AI is augmenting human decision-making:

Healthy Healthcare

As multiple countries grapple problems from an ageing population, rising healthcare costs and low doctor-to-patient ratios, AI can help improve healthcare outcomes in a variety of ways. For instance, AI is being leveraged for public health studies – from detection of potential physical or psychological pandemics to epidemiology – by mining social media and other data sources.

Further, startups and conglomerates are working on AI for diagnostics – from detection of early warning signals to identifying and quantifying abnormalities/tumours. In the pharma industry, AI is helping improve site studies, drug development and clinical trials through analysis of meaningful data.

Financial Services

A common AI use case for financial services is in the domain of fraud detection and anti-money laundering. AI can help surface bad actors by quickly scanning data for anomalous behaviour. Similarly, AI is also powering customer interaction decisions through intelligent chatbots that can address common concerns, thus reducing the need for human intervention in repetitive, menial tasks. We’re also seeing increased proliferation of robo-advisers – which are advanced AI tools that help make investment decisions by matching investible capital and returns expected.

Managing Media

The media and entertainment industry is going through an AI and digital disruption due to the combination of huge datasets and success of torchbearers like Netflix and Spotify. Content recommendation and personalisation are decisions that are autonomously delivered by AI, which can quickly scan a user’s history and match it with the preferences of similar users.

The industry is also relying on AI to make decisions around content creation, again taking a leaf out of Netflix to make content more engaging and sticky. There is also a strong use case of AI helping identify and attract customers by surfacing tailored content and promotions to increase subscriptions, loyalty and share-of-wallet.

Retail Rejig

Retail was one of the first industries to witness the rise of a data-powered competitor that eventually decimated incumbents. The brick-and-mortar retail industry is now incorporating AI in its decision-making process to replicate the customer experience expectations set by Amazon and the like.

Retailers leverage user purchase to identify next-best product and create tailored loyalty programmes. It is also being increasingly used for rapid experimentation to define store location, layouts and product-shelf decisions. Retailers can better anticipate demand, leading to leaner supply chains and warehouses, optimised inventory and fewer stockouts.

Efficient Manufacturing

Manufacturing companies are bringing in AI interventions to run leaner supply chains to cut the cost of transportation and wastage. AI also enables them to better anticipate demand by looking at historical sales, current uptake and other business environment factors to run on-demand production.

Some AI-led decisions are pervasive across multiple industries. For instance, digital personalisation, ie, serving targeted promotions to customers based on their key purchase drivers, is a multi-industry example of AI in action.

The other is for detecting security threats through anomaly detection and video analytics to identify unauthorised entry. Human Resources is another function that is rapidly changing, with companies using AI to speed up talent acquisition by scanning resumes for relevancy and reducing attrition by identifying key drivers that lead to employees leaving.

Successful AI-led Decisions

The business value of AI is significantly lowered when performed ad hoc, without a strong foundational strategy. It is important that the organisation clearly defines the decisions that should be powered by AI to maintain a high standard of outcomes. The responses will differ from company to company and from industry to industry, but it is important that corporations establish transparent standards for fair use.

We see enough examples of hastily implemented AI, leading to calamitous consequences and companies can no longer hide by saying, ‘The AI made me do it’. To demarcate the clear go and no-go zones for AI, here’s a handy questionnaire to ask yourself:

– Do we have enough superior quality data now and in the future for AI to make the best decision?
– Do we need to bring in insights from multiple sources to contribute to the decision-making process at a speed and scale, which cannot be efficiently handled by human cognition?
– Is human decision-fatigue or bias currently creating a sub-optimal outcome in this area?
– Could there be ethical or moral implications to an AI-led decision that might lead to disastrous consequences?

We also need to address the confidence issues. For instance, a lot of executives look down upon some of the black-box processes performed by AI algorithms. We need to find a way to address these issues by creating a transparent trail of AI decisions and the reasons why AI took a decision. Even in unsupervised learning scenarios, a trail of decisions will not only boost confidence but will also help build better AI and better businesses.

Re-imagined AI-powered decisions will become de rigueur only by the quality of the outcomes they deliver. According to Dr John Kelly, SVP — IBM Research and Solutions portfolio, “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.” This is a crucial way to look at and measure the impact of AI on our businesses, society and lives.

When it comes to AI, businesses think ambitiously. Nearly 85% of executives believe AI will allow their company to obtain or sustain a competitive advantage in the marketplace. Contrastingly, just one in five companies have incorporated AI into their organization and less than 39% of companies have an AI strategy.

Exactly why is AI so disruptive to traditional business models and traditional notions of industry competition? A useful way to analyse the situation is by looking at Porter’s model of the five forces of industry competition and exploring how artificial intelligence is impacting each of the various forces.

According to Michael E. Porter, in one of his landmark books, titled Competitive Strategy, “In any industry, whether it is domestic or international or produces a product or a service, the rules of competition are embodied in five competitive forces: the entry of new competitors, the threat of substitutes, the bargaining power of buyers, the bargaining power of suppliers, and the rivalry among the existing competitors.”

Figure 1: Porter’s Five Forces

Let’s look at each of these five forces and examine the role and impact of AI:

The entry of new competitors

There’s no doubt that AI is changing the nature of competition. Today, it’s not just traditional industry competitors you need to worry about, but new entrants from outside your industry, equipped with new AI based business models and value propositions.

This is often tech giants and startups that have envisioned and built a new business model from the ground up, powered by a new platform ecosystem for AI. They’re leveraging the familiar social, mobile, analytics and cloud technologies, but are often adding in personas and context, intelligent automation, chatbots and the Internet of Things, to further enhance the value proposition of their platform.

Why can new entrants move in so easily? Digital business changes the rules by lowering the traditional barriers to entry. A digitally based business model requires far less capital and can bring large economies of scale for example. Read more about how AI Startups are creating disruptive competition here.

The threat of substitutes

The threat of substitutes is high in many industries since switching costs are low and buyer propensity to substitute is high. For example, In the taxi services, customers can easily switch from traditional models to the new digital app based taxi services, employing AI routines to create differential pricing and intelligent route mapping to increase margin as well as decrease price for the customers. Propensity to switch from the traditional model is high due to consumer wait times for taxis, lack of visibility into taxi location and so on.

In case of BPO industry, the advent of AI has been extremely disruptive, with their clients completely substituting their services with building in-house automation offerings and circumventing their need, sometimes completely. Read more in detail about the disruption of BPO/BPM by AI here.

The bargaining power of buyers

Perhaps the strongest of the five forces impacting industry competition is the bargaining power of buyers since the biggest driver of AI and digital business comes from the needs and expectations of consumers and customers themselves.

This bargaining power lays out a new set of expectations for the AI and digital customer experience and necessitates continual corporate innovation across business models, processes, operations, products and services.

For example, the most used instances of chatbots are through customer support, and now they are heading in the direction of changing the retail sector altogether. The expectations of the Millennials are directing the course of this new technology. This is why chatbots have the burden to exceed the expectations in the retail sector.

Also, in another example, in the customer facing marketing aspect, AI is causing circular rise in customer expectations as rise of expectations, mostly from millennials, has forced the companies to adopt an AI solution to the problem, which further has emboldened their expectations. Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases. Read more about how AI is accentuating customer experience to address rising expectations Here.

The bargaining power of suppliers

Suppliers can accelerate or slow down the adoption of a AI based business model based upon how it impacts their own situation. Those pursuing AI models themselves, such as the use of APIs to streamline their ability to form new partnerships and manage existing ones, may help accelerate your own model.

Those who are suppliers to the traditional models, and who question or are still determining their new role in the digital equivalent, may use their bargaining power to slow down or dispute the validity or legality of the new model.

Good examples are the legal and business issues surfacing around the digital-sharing economy (i.e. ride-sharing, room-sharing etc.) where suppliers and other constituents work to ensure the AI based business model and process innovations (like route optimization, or deep customer behaviour analysis using private data) still adhere to established rules, regulations, privacy, security and safety. This is a positive and needed development since, coupled with bargaining power of buyers, it can help to keep new models “honest” in terms of how they operate.

The rivalry among the existing competitors

A lot of organisations are in exploratory stages as they realise that their strategy and customer engagement needs to get smarter. The combination of optimism and fear that clients today have shows that for them it is a competitive necessity to adopt AI and digital technologies.

In 20 years, probably every job will be touched by AI. The technology is growing universally. WhatsApp and Facebook — everything is driven by AI. And what this means is that on the job front, there may be blood. Once AI, ML, and virtual and augmented reality go mainstream, these technologies will prove to be a huge job creator.

But currently, the most competitive space in AI adoption is in the implementation of chatbots across industries and functions. While we might see chatbots starting to appear through the likes of Facebook Messenger and WhatsApp platforms in the coming 12 months, and will be dedicating teams of engineers to train the platforms, rather than relying on the general public. Read more about the competitive atmosphere and underlying need to better customer experience using chatbot here.

How AI will transform Strategic Planning Process

How can managers — from the front lines to the C-suite — thrive in the age of AI? In many ways, the lack of understanding when it comes to AI is due to the variety of ways AI can be implemented as a part of strategic planning for a business. Different industries, or even different companies within the same industry, may use AI in different ways. Ping An, which employs 110 data scientists, has launched about 30 CEO-sponsored AI initiatives that support, in part, its vision – that technology will be the key driver to deliver top-line growth for the company in the years to come. Yet in sharp contrast, elsewhere in the insurance industry, other large companies’ AI initiatives are limited to experimenting with chatbots. Obviously, integrating AI is not going to be simple. There will be a massive learning curve for organizations before they’re able to start implementing AI effectively. But the core shift in strategic planning will happen in the following ways:

AI will take over almost all Administrative Tasks

According to an HBR report, managers across all levels spend more than half of their time on administrative coordination and control tasks. (For instance, a typical store manager or a lead nurse at a nursing home must constantly juggle shift schedules because of staff members’ illnesses, vacations, or sudden departures.) These are the very responsibilities that the same managers expect to see AI affecting the most. And they are correct: AI will automate many of these tasks.

Figure 2: Source –  HBR (How Artificial Intelligence Will Redefine Management)

For example, in case of report writing The Associated Press expanded its quarterly earnings reporting from approximately 300 stories to 4,400 with the help of AI-powered software robots. In doing so, technology freed up journalists to conduct more investigative and interpretive reporting.

Strategy Managers will focus more on Judgement-oriented Creative Thinking Work

The human factor, which AI still cannot permeate – the application of experience, expertise and a capacity to improvise, to critical business decisions and practices – need to be focused on by strategy managers. Many decisions require insight beyond what artificial intelligence can squeeze from data alone. Managers use their knowledge of organizational history and culture, as well as empathy and ethical reflection. Managers we surveyed have a sense of a shift in this direction and identify the creative thinking skills and experimentation, data analysis and interpretation, and strategy development as three of the four top new skills that will be required to succeed in the future. And since the potential of machine learning is the ability to help make decisions, the AI technology would be better placed as an assisting hand than administrative mind.

Think of AI not as Machines, but Colleagues

Managers who view AI as a kind of colleague will recognize that there’s no need to “race against a machine.” While human judgment is unlikely to be automated, intelligent machines can add enormously to this type of work, assisting in decision support and data-driven simulations as well as search and discovery activities. In fact, 78% of the surveyed managers believe that they will trust the advice of intelligent systems in making business decisions in the future.

Not only will AI augment managers’ work, but it will also enable managers to interact with intelligent machines in collegial ways, through conversation or other intuitive interfaces.

For example, Kensho Technologies, a provider of next-generation investment analytics, allows investment managers to ask investment-related questions in plain English, such as, “What sectors and industries perform best three months before and after a rate hike?” and get answers within minutes.

Design Thinking needs to be adopted both ways – Managers & AI

While managers’ own creative abilities are vital, perhaps even more important is their ability to harness others’ creativity. Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. Creative thinking and experimentation is a key skill area that managers need to learn to stay successful as AI increasingly takes over administrative work. ‘Collaborative Creativity’ is the operating word here.

But this doesn’t mean that design thinking necessarily need to become a forte exclusive to managers. Even though AI engines may not have reached radical thinking and improvisation as humans, AI algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. This calls for Divergence from More Powerful Intelligence To More Creative Intelligence in AI.

To make design thinking meaningful for consumers, companies can benefit from carefully selecting use cases and the information they feed into AI technologies. In determining which available data is likely to generate desired results, enterprises can start by focusing on their individual problems and business cases, create cognitive centres of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowdsource ideas from employees and customers alike. Read more about Design Thinking in AI here.

Create New Business Processes manifested from Augmented Working Strategy

Simply put, my recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment. If the current shortage of analytical talent is any indication, organizations can ill afford to wait and see whether their managers are equipped to work alongside AI. This calls for change in business processes, and the way they are implemented itself. To navigate in an uncertain future, managers must explore early, and experiment with AI and apply their insights to the next cycle of experiments.

AI augmentation will drive the adoption of new key performance indicators. AI will bring new criteria for success: collaboration capabilities, information sharing, experimentation, learning and decision-making effectiveness, and the ability to reach beyond the organization for insights.

Accordingly, organizations need to develop training and recruitment strategies for creativity, collaboration, empathy, and judgment skills. Leaders should develop a diverse workforce and team of managers that balance experience with creative and social intelligence — each side complementing the other to support sound collective judgment.

Final Word

While oncoming AI disruptions in Management Principles and Strategic Planning space won’t arrive all at once, the pace of development is faster and the implications more far-reaching than most executives and managers realize. Those managers capable of assessing what the workforce of the future will look like can prepare themselves for the arrival of AI.

Rapidly evolving technology and a digitally focused world have opened the door for a new wave of automation to enter the workforce. Robots already stand side-by-side with their human counterparts on many manufacturing floors, adding efficiency, capacity (robots don’t need to sleep!) and dependability. Add in drones and self-driving vehicles and it’s no wonder many are questioning the role of humans going forward.

Supply chains, although automated to a degree, still face challenges brought about by the amount of slow, manual tasks required, and the daily management of a complex web of interdependent parts. The next generation of process efficiency gains and visibility could be on your doorstep with artificial intelligence in supply chain management, if only you’d let the robots automatically open it for you.

Robotic Process Automation

RPA works by automating the end-to-end supply chain, enabling the management of all tasks and sections in tandem. It allows you to spend less time on low value, high frequency activities like managing day-to-day processes, and provides more time to work on high value, exception-based requirements, which ultimately drives value for the entire business.

PwC estimates businesses could automate up to 45% of current work, saving $2 trillion in annual wages. “In addition to the cost and efficiency advantages, RPA can take a business to the next level of productivity optimization,” the firm says. Those ‘lights out’ factories and warehouses are becoming closer to a reality.

Four key elements need to be in place for you to take full advantage of robotic process automation in your supply chain:

• robots for picking orders and moving them through the facility;
• sensors to ensure product quality and stock;
• cognitive learning systems;
• and, artificial intelligence to turn processes into algorithms to guide the entire operation.

In addition, you’ll need strong collaboration internally and among suppliers and customers to tie all management systems back to order management and enterprise resource planning platforms.

Artificial Intelligence In Supply Chain Automation

AI is changing the traditional way in which companies are operating. Siemens in its “lights out” manufacturing plant, has automated some of its production lines to a point where they are run unsupervised for several weeks.

Siemens is also taking a step towards a larger goal of creating Industrie 4.0 or a fully self-organizing factory which will automate the entire supply chain. Here, the demand and order information would automatically get converted into work orders and be incorporated into the production process.

This would streamline manufacturing of highly customized products.

 Artificial Intelligence In Supplier Management And Customer Service

Organizations are also increasingly leveraging AI for supplier management and customer management. IPsoft’s AI platform, Amelia automates work knowledge and is able to speak to the customers in more than 20 languages. A global oil and gas company has trained Amelia to help provide prompt and more efficient ways of answering invoicing queries from its suppliers. A large US-based media services organization taught Amelia how to support first line agents in order to raise the bar for customer service.

 Artificial Intelligence In Logistics & Warehousing

Logistics function will undergo a fundamental change as artificial intelligence gets deployed to handle domestic and international movement of goods. DHL has stated that its use of autonomous fork lifts is “reaching a level of maturity” in warehouse operations. The next step would be driver less autonomous vehicles undertaking goods delivery operations.

 Artificial Intelligence In Procurement

AI is helping drive cost reduction and compliance agenda through procurement by generating real time visibility of the spend data. The spend data is automatically classified by AI software and is checked for compliance and any exceptions in real time. Singapore government is carrying out trials of using artificial intelligence to identify and prevent cases of procurement fraud.

The AI algorithm analyzes HR and finance data, procurement requests, tender approvals, workflows, non-financial data like government employee’s family details and vendor employee to identify potentially corrupt or negligent practices. AI will also take up basic procurement activities in the near future thereby helping improve the procurement productivity.

Artificial Intelligence in new product development

AI has totally overhauled the new product development process.by reducing the time to market for new products. Instead of developing physical prototypes and testing the same, innovators are now creating 3D digital models of the product. AI facilitates interaction of the product developers in the digital space by recognizing the gestures and position of hand. For example, the act of switching on a button of a digital prototype can be accomplished by a gesture.

 AI In Demand Planning And Forecasting

Getting the demand planning right is a pain point for many companies. A leading health food company leveraged analytics with machine learning capabilities to analyze their demand variations and trends during promotions.

The outcome of this exercise was a reliable, detailed model highlighting expected results of the trade promotion for the sales and marketing department. Gains included a rapid 20 percent reduction in forecast error and a 30 percent reduction in lost sales.

 AI in Smart Logistics

The impact of data-driven and autonomous supply chains provides an opportunity for previously unimaginable levels of optimization in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade despite high set-up costs deterring early adoption in logistics.

Changing consumer behavior and the desire for personalization are behind two other top trends Batch Size One and On-demand Delivery: Set to have a big impact on logistics, on-demand delivery will enable consumers to have their purchases delivered where and when they need them by using flexible courier services.

A study by MHI and Deloitte found more than half (51%) of supply chain and logistics professionals believe robotics and automation will provide a competitive advantage. That’s up from 39% last year. While only 35% of the respondents said they’ve already adopted robotics, 74% plan to do so within the next 10 years. And that’s likely in part to keep up with key players like Amazon, who have been leading the robotics charge for the past few years.

 What is the mantra ?

These examples showcase that in today’s dynamic world, AI embedded supply chains offer a competitive advantage. AI armed with predictive analytics can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management.

Thus, in this digital age where the mantra is “evolve or be disrupted”, companies are leveraging AI to reinvent themselves and scale their businesses quickly. AI is becoming a key enabler of the changes that businesses need to make and is helping them manage complexity of the constant digital change.