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. 

A robust ecosystem for small and medium enterprises is one of the key indicators of economic vibrancy and entrepreneurial energy in a nation. India’s entrepreneurial spirit was given a massive boost after liberalisation in the early 90s. These norms ended the draconian ‘Licence Raj’ that kept the lid on the business aspirations of the average Indian. With improving access to capital, heightened ease of doing business and a galvanised ecosystem that provides mentorship and guidance to fledgling startup founders, the small and medium enterprise (SME) sector has been riding high over the past couple of decades. Now with Artificial Intelligence (AI) in the mix, SMEs will be given another boost – through reduced cost and improved efficiencies in how they run, operate and succeed.

There is no doubt that AI will be an important gamechanger for the SME sector. Startups today are much more data-rich than before and understand the value that can be unlocked through intelligent deployment of advanced analytics. They understand that a data-driven understanding of their business landscape will far outweigh heuristic methods in the dynamic environment in which their enterprises operate. Further, with lowering cost of adoption, increased focus on the SME industry by incumbent analytics/AI vendors and partners, we have a perfect storm of sorts for the sector to derive the exponential benefits of this technology. Let us look at the areas where AI can deliver a strong, demonstrable impact on the sector and how such businesses can get started on their AI journey.

Galvanising SME Operations

When run on the right data set, AI can work its magic in providing untold operational benefits to SMEs. The case for AI in the startup sector is much stronger than it is for their larger corporate counterparts. The reasons for that are two-fold. First, startups typically operate on smaller budgets – which means that they need to automate as much as they can to reduce costs associated with a higher headcount.

Secondly, startups by their very nature are extremely nimble, allowing them to experiment rapidly with new, innovative technologies. This twofold advantage means that AI vendors as SMEs need to have a robust strategy in place to work together and uncover the latent advantages offered by this technology. Here are a few areas where AI can specifically help startups galvanise their operations.

• Predictive maintenance: SMEs, especially in the manufacturing segment, can unlock huge benefits in the production process using AI. With sophisticated algorithms monitoring machine health, AI can help reduce the downtime in production schedules by accurately modelling when a critical machine is likely to go down, allowing businesses to better plan demand fulfilment.

• Supply chain and logistics: A major drain on the revenue of nascent businesses is the cost associated with procurement of raw materials and delivery of finished products. By using AI and third-party location data, SMEs can plug this drain by powering faster and leaner delivery schedules. Similarly, demand planning and order fulfilment will get a big boost as SMEs learn how to forecast accurately through machine learning models, thus reducing the waste that entails unused, unsold and unutilised inventory.

• Marketing and sales: Multiple SMEs tend to go under because they take on much large corporations with massive sales and marketing budgets. AI can help these startups level the playing field. By using data from each prospect interactions as well as leveraging emerging breakthroughs in the field of programmatic advertising, AI can help fine-tune the marketing programs of startups and help deliver better ROI on their spend. Similarly, through an improved understanding of their territory, AI can provide laser-guided focus to sales people on which prospects to focus on and what approaches can deliver the best results.

• Customer service: Where large enterprises can afford to outsource customer service operations or even bring them in-house, SMEs do not have these advantages. In today’s environment, customers are shown to be more loyal when provided with a superior customer experience. AI can bridge the gap between customer expectations and the constrained budgets available to provide those. With intelligent assistants, SMEs can navigate common questions and complaints put forth by customers and provide a superior customer service at much lowered costs of delivery.

• Talent acquisition: SMEs often have vastly varying needs for talent. For instance, those that are on a strong growth trajectory need to staff their companies rapidly before the competitive advantage they offer slips. Those that are on a slower curve also need to make sure that they hire candidates with the right mix of experience and attitudinal attributes to ensure the smooth functioning of their business.

AI can help reduce the time taken to identify the right candidates by rapidly screening resumes to identify the best fit for the needs of the business. Further, with the right data and training, AI can also administer relevant tests to candidates and grade their performance, thus reducing the requirement of human intervention and time taken to screen good candidates.

Getting Started

Let us look at some of the key factors that business leaders need to keep in mind as they get started on their AI journey.

• AI starts with data: The first consideration before planning an AI intervention is to understand whether high quality data is available for AI to work its magic. Without the right data sets, even the best algorithms can go awry. It is essential that business leaders ensure that their data repositories are sufficiently rich to get started on the AI journey.

• Identify the right problems: SMEs tend to be inundated with multiple issues of burning importance. It can be very enticing for business leaders to assume that AI is the panacea for all problems. That is not the case. Business leaders need to identify the right problem statements where AI can make a demonstrable impact and prioritise use cases that can be solved through AI. Scan the market for best practices and learn from peers to better understand what AI can do and what measurable benefit you can derive from AI-led interventions

• Set success benchmarks: For AI leaders, it is important to set a marker for the right expectations with business leaders. Hence, for the business to see continuous improvement in the results delivered by AI, it is critical to identify the right set of business metrics and expected performance against each of those.

Artificial Intelligence today has gone well beyond experimentation to now becoming a real game-changer in how businesses operate. AI can bring significant benefits to startups with improved efficiencies and faster operations. SME leaders looking for strong competitive advantages with respect to their peers would do well to harness the power of this technology and infuse it into their key business process to accelerate outcomes and grow their businesses.

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.