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.

As is the case with investments in any other area of technology, AI needs to deliver demonstrable impact to business top line and bottom line. In today’s competitive landscape of business, enterprises are expected to measure the incremental ROI for every expense and every investment made – technology or otherwise. The case of Artificial Intelligence is no different. It is critical that technology and business leaders demand ROI impact for this technology in order to foster its growth and justify its proliferation in business.

To be sure, there are two key areas where Artificial Intelligence can contribute immense value; Increasing top line figures by unlocking new revenue streams and improving the bottom line through efficiencies in operations. Needless to say, top line gains eventually percolate their way into showcasing bottom line improvement – but for the purpose of this post, we’ll refer to bottom line impact as areas where AI brings in cost efficiencies by helping organizations reduce their overall cost of operations.

Artificial Intelligence driven applications can have a discernible impact on business top lines and bottom lines and help organizations unlock ROI from their implementation.

AI-Powered Topline Growth

Artificial Intelligence-led applications have huge potential to add to top line revenue growth for any organization. Typical AI interventions for this purpose range from improving the effectiveness of marketing and sales functions, improving customer loyalty through laser-guided customer experience initiatives and direct and indirect data monetization.

New Revenue Streams Enabled by Data Monetization:

Business leaders need to realize AI’s potential to unlock new sources of revenue in addition to improving customer targeting and loyalty. One of these ways is data monetization. What is data monetization? Simply put, data monetization refers to the act of generating measurable economic benefits from available data resources. According to Gartner, there are two distinct ways in which business leaders can monetize data. The most commonly seen method from the two is Direct Monetization. The way to realize value from this avenue involves directly adding AI as a feature to existing offerings. Companies like Nielsen, D&B, TransUnion, Equifax, Acxiom, Bloomberg and IMS run their business on licensing their data in a raw format or as part of their application infrastructure. With emerging Data-as-a-Service models and the application for direct insight delivery through intelligent application of AI, direct data monetization is simpler than ever. By wrapping insights alongside the data source, vendors can create a symbiotically powerful exchange of information for both the buyers and sellers of data. On the other hand, Indirect Monetization involves embedding AI into traditional business processes with a focus on driving increased revenue. A popular example of this is corporations who come out with branded, paid-for reports based on the data they own. For instance, professional services companies such as Aon, Deloitte, McKinsey, etc., regularly bring forward insightful industry and function-specific reports based on the data they collect as part of their consulting assignments.

Enabling Intelligent Marketing and Sales

Many of the most prominently cited successes of AI-enabled business transformation comes from the marketing and sales arena. Sales and marketing are constantly on the forefront for exciting inventions in AI since they contribute directly to top line growth. Use cases discovered in this arena span social media sentiment mining, programmatic selection of advertising properties, measuring effectiveness of marketing programs, ensuring customer loyalty and intelligent sales recommendations. AI also has huge potential to drive businesses to explore and exploit eCommerce platforms as a credible channel for sales and to help drive the digital agenda forward. Available tools are helping drive better customer conversions on eCommerce properties – by analysing the digital footprints (clickstream, etc.) of prospective customers, persuading them into making a purchase. In such use cases, AI helps improve personalization at the point-of-purchase, improve conversions and reduce cart abandonment. Marketing and sales use cases today are pretty much at the epicentre of an AI disruption and business leaders need to uncover more use cases that can help drive effective top line growth.

AI Redefining Customer Experience

Customers are the epicentre of every successful organization. Today, we live in times where customers have numerous competitor options to choose from while the switching costs for customers are increasingly lower. Given this scenario, for businesses to win with their customers they need to have a smarter approach to customer experience management.

We have progressed well beyond pre-programmed bots addressing frequently asked questions. AI-enabled systems today go further and provide customers with personalized guidance. The travel and hospitality industries, for instance, are ripe for such disruptive innovations. In many cases, we see chatbots that help customers identify and recommend interesting activities and events that tourists can avail. When applied with human creativity, AI can ensure this redefined understanding of customer experience, while maintaining a lower cost of delivering that experience.

AI for Improving Bottom Line Performance

At an operational level as well, AI can help organizations run a more efficient business. For instance, corporations across industries need to find innovative and fail-safe ways to reduce the cost of manufacturing as well as capping their outlay on the supply chain network. AI-centric solutions can drive down the turnaround time for talent acquisition and transform other facets of the Human Capital function too.

AI Driving Operational Efficiencies

Traditional manufacturing processes are now increasingly augmented by robotics and AI. These technologies are bringing increasing sophistication to the manufacturing process. The successes combine human and machine intelligence making AI-augmented manufacturing a pervasive phenomenon. Today, business leaders in the Industry 4.0 generation need to seriously consider planning a hybrid labour force powered by human and artificial intelligence – and ensure that the two coexist by implementing the right policies and plans in place.

Smarter Supply Chains Powered by AI

Orchestrating a leaner, more predictable supply chain is ripe for an AI-led disruption. We are witnessing not just new products and categories but also new formats of retailers proliferating the industry. This varied portfolio of offerings and channels requires corporations to manage their outlay efficiently on the overall network responsible for the network that manages the entire process from procurement and assembly to stocking and last mile delivery. Multiple use cases exist that leverage multi-source data from internal and external repositories, combining them with information from IOT sensors. AI algorithms are then applied over this combined data infrastructure with the objective of helping business users quickly identify possible weaknesses/flaws in the process such as delays and possible shortages. Business leaders are constantly on the lookout for solutions that can directly lift their bottom line by bringing in more intelligence and automation to their supply chain networks – thus unlocking savings for their businesses.

An Artificial Facelift for the Human Resources Function

The human resources function has historically been considered a cost-center in organizations. In addition to bringing down the costs associated with talent acquisition and management – AI would also help HR teams become leaner, more organized and reduce the turnaround time for talent acquisition. AI interventions are being seen in the areas of employee engagement and attrition management, but some of the most exciting use cases come from the talent acquisition area within the HR function. Multiple organizations are already working on solutions that can eliminate the need for HR staff to scan through each job application individually. By using AI intelligently, talent acquisition teams can determine the framework conditions for a job on offer and leave the creation of assessment tasks to Artificial Intelligence-powered systems. The AI-empowered system can then communicate the evaluation results and recommend the most suitable candidates for further interview rounds.

One of the key reasons why AI is in vogue today is the demonstrable ROI impact that it promises to bring to business processes. With greater computational power and more data, AI has become more practicable than before, but what will sustain its growth is how much incremental value it can eventually unlock for businesses across the globe and power new revenue models for businesses to tap into. It is critical that business and technology leaders earnestly kick off discussions around how to justify the impact of AI and mark down the key metrics that will be used to measure it. Partners and service providers too need to stay on top of finding ways to showcase measurable improvements that their software or services can bring to technology buyers. This will enable the entire AI ecosystem to flourish. 

The adoption and benefit realisation from cognitive technologies is gaining increasing momentum. According to a PwC report, 72% of business executives surveyed believe that artificial intelligence (AI) will be a strong business advantage and 67% believe that a combination of human and machine intelligence is a more powerful entity than each one on its own.

Another survey conducted by Deloitte reports that on an average, 83% of respondents who have actively deployed AI in the enterprise see moderate to substantial benefits through AI – a number that goes further up with the number of AI deployments.

These studies make it abundantly clear that AI is occupying a high and increasing mindshare among business executives – who have a strong appreciation of the bottom line impact delivered by cognitive systems, through improved efficiencies.

AI-first Mindset

Having said that, with AI becoming more and more mainstream in an organisational setup, piecemeal implementations will deliver a lower marginal impact to organisations’ competitive advantage. While once early adopters were able to realise transformational benefits through siloed AI deployments, now that it is fast maturing as a must-have in the enterprise and we will need a different approach.

To realise true competitive advantage, organisations need to have an AI-first mindset. It is the new normal in accelerating business decisions. It was once said that every company is a technology company – meaning that all companies were expected to have mature technology backbones to deliver business impact and customer satisfaction. That dictum is now being amended to say – every company is a cognitive company.

To deliver on this promise, companies need to weave AI into the very fabric of their strategy. To realise competitive advantage tomorrow, we need to embed AI across the organisation today, with a strong, stable and scalable foundation. Here are three building blocks that are needed to create that robust foundation.

1. Enrich Data & Algorithm Repositories

If data is indeed the new oil (which it is), organisations that hold the deepest reserves and the most advanced refinery will be the ones that win in this new landscape. Companies having the most meaningful repository of data, along with fit-for-purpose proprietary algorithms will most likely enjoy a sizeable competitive advantage.

So, companies need to improve and re-invent their data generation and collection mechanisms. Data generation will help reduce their reliance on external data providers and help them own the data for conducting meaningful, real-time analysis by continuously enriching the data set.
Alongside, corporations also need to build an ‘algorithm factory’ – to speed up the development of accurate, fit-for-purpose and meaningful algorithms. The algorithm factory would need to push out data models in an iterative process in a way that improves the speed and accuracy.

This would enable the data and analysis capabilities of companies to grow in a scalable manner. While this task would largely fall under the aegis of data science teams, business teams would be required to provide timely interventions and feedback – to validate impact delivered by these models, and suggest course-corrections where necessary.

Another key aspect of this process is to enable a transparent cross-organisation view into these repositories. This will allow employees to collaborate and innovate rapidly by learning what is already been done and will reduce needless time and effort spent in developing something that’s already there.

2. AI Education for Workforce

Operationalising AI requires a convergence of different skill sets. According to the above-cited Deloitte survey, 37% of respondents felt that their managers didn’t understand cognitive technology – which was a hindrance to their AI deployments.

We need to mix different streams of people to build a scalable AI-centric organisation. For instance, business teams need to be continuously trained on the operational aspects of AI, its various types, use cases and benefits – to appreciate how AI can impact their area of business.
Technology teams need to be re-skilled around the development and deployment of AI applications. Data processing and analyst teams need to better understand how to build scalable computational models, which can run more autonomously and improve fast.
Unlike a typical technology transformation, AI transformation is a business reengineering exercise and requires cross-functional teams to collaborate and enrich their understanding of AI and how it impacts their functions, while building a scalable AI programme.

The implicit advantage of developing topical training programmes and involving a larger set of the workforce is to mitigate the FUD that is typically associated with automation initiatives. By giving employees the opportunity to learn and contribute in a meaningful way, we can eliminate bottlenecks, change-aversion and enable a successful AI transformation.

3. Ethical and Security Measures

The 4th Industrial Revolution will require a re-assessment of ethical and security practices around data, algorithms and applications that use the former two.

By introducing renewed standards and ethical codes, enterprises can address two important concerns people typically raise – how much power can/should AI exercise and how can we stay protected in cases of overreach.

We are already witnessing teething trouble – with accidents involving self-driving cars resulting in pedestrian deaths, and the continuing Facebook-Cambridge Analytica saga.

Building a strong grounding for AI systems will go a long way in improving customer and social confidence – that personal data is in safe hands and is protected from abuse – enabling them to provide an informed consent to their data. To that end, we need to continue refining our understanding around the ethical standards of AI implementations

AI and other cyber-physical systems are key components of the next generation of business. According to a report by semiconductor manufacturer, ARM, 61% of respondents believe that AI can make the world a better place. To increase that sentiment even further, and to make AI business-as-usual, and power the cognitive enterprise, it is critical that we subject machine intelligence to the same level of governance, scrutiny and ethical standards that we would apply to any core business process.