Enterprises put AI in supply chain to streamline processes

There's no area of the enterprise seeing greater positive value from AI investment than business process operations. A recent survey by BI Intelligence revealed that supply chain and operations together are the third most active area implementing AI technology, with over 42% of enterprises that responded already seeing revenue gains from AI investments. In another McKinsey study, enterprises that have invested in AI technology for transportation and logistics increased profit margins by more than 5%. Where is all this positive return coming from with AI?

Automating and streamlining operations functions

The biggest area where AI is making its presence felt is in automating many of the previously manual and time-consuming processes that, while necessary, are a big drag on corporate bottom lines. For companies that have large supply chains with millions of orders or purchases to process, handling invoicing and procurement processes can be a significant drag. Increasingly, enterprises are putting AI in supply chain processes, using tools like computer vision to handle invoices and process automation tools to handle moving information across disparate systems. Many of these systems can also perform regular audits of data, catching bad actors, as well as improper or mistaken information, before they cause larger economic impact. In addition, AI plays a role in inventory management by using image recognition to perform inventory analysis and constant inventory audits.

Part of what's driving all this activity is the sheer amount of data generated throughout supply chain operations. AI and machine learning excel in handling large volumes of data, spotting patterns and anomalies, and otherwise providing intelligence and context from the mountain of data available. AI systems can spot when orders are trending the wrong way, reroute shipments when exceptions happen and handle customer support-related issues in an automated manner, reducing the need for human involvement. These systems also help improve forecasting and planning, especially around inventory, which can generate significant benefits, as well as positive ROI, for organizations that are inventory-sensitive.

In a recent report from shipping and logistics company DHL, the company said it is using AI across the board in its logistics operations. For example, DHL is using computer vision to handle labeling- and tracking-related processes, as well as inspect the condition of packages. The company is also using cognitive technologies to increase the development of autonomous transportation systems and to predict fluctuations in global shipment volumes before they occur. The company sees AI playing an augmentative role, not replacing humans but rather eliminating routine work so as to shift the labor force to higher-value work.

Improving inventory forecasting

AI systems have proven to be particularly good at spotting patterns in data that are not immediately identifiable with traditional data analytics or statistics methods. In the areas of logistics and supply chain, machine learning algorithms have been applied to identify which products are selling faster or slower than anticipated and predict more accurate inventory forecasts. Putting AI in supply chain processes can improve the accuracy of inventory forecasting, thereby reducing the understocking or overstocking of goods. These inventory forecasts, in turn, help product-driven organizations become a lot more efficient, reducing warehouse- and inventory-related costs, increasing just-in-time delivery of goods and generally improving overall customer satisfaction.

Similarly, companies are applying AI to address supply chain-related problems, such as equipment failures or unexpected issues relating to weather or regional disruptions. Machine learning algorithms have been applied to find optimal shipping routes, use intermediate warehouse locations to store goods en route to customers and even make predictions of potential service disruptions.

Automating fulfillment

Of course, one of the biggest areas where AI is gearing up to potentially disrupt the supply chain is in warehouses and the entire fulfillment side of the supply-side equation. Companies are making increasing use of automation, robotics and autonomous capabilities to complete the whole cycle of receiving inventory, stocking warehouse shelves, picking and packing products, and delivering to customers. In the not-too-distant future, few humans will be involved in this process.

Amazon has proven that its Kiva bots are capable of automating most of the pick, pack and stock functions. Baidu and Alibaba have made similar advancements to their own warehouses and stocking points. In a sign of more advancement, Siemens is currently operating a "lights-out factory," which has automated so much of the production process that it can operate autonomously in near-dark conditions without any human involvement for weeks at a time. Other companies, including Fanuc and Philips,  operate lights-out factories that need no human involvement, even eliminating the need for heating or cooling for the facilities.

On the transportation side, technology firms are working hard to create autonomous delivery vehicles from trucks to delivery cars to drones. The last mile of delivery is usually the most complicated, and as such, many companies are working on technologies to help make this easier. Amazon Prime has experimented with drones, Google's Waymo is working on delivery vehicles and Uber's Otto is working on autonomous trucking.

Reducing fraud and waste

The leader in using AI to reduce fraud and waste is Amazon, which remarkably started applying AI to its supply chain as early as 2004. Since then, the company has seen a huge increase in supply chain reliability, reduction of shipping errors, reduction in supply- and order-related fraud and bad debts, and improvements in operational efficiency. Following in Amazon's footsteps, similar organizations are rushing to add AI in supply chain processes to realize the same benefits.

Additionally, AI systems are being applied to help with sourcing of products and using interest in products to assist with negotiating favorable pricing. In this way, companies that take advantage of AI can use their data to gain a strategic advantage over other companies that are simply using historical information and long-term pricing contracts that lock them into inefficient inventory allocations and poor cash flow. Companies that optimize their supply chains in this way don't need to have end-of-season clearance sales since there won't be anything to clear.

Augmenting and improving worker productivity and safety

AI is also being used to improve the productivity of workers that are involved in the supply chain. Machine learning systems can learn the proper and optimal behavior patterns of workers and monitor how employees execute tasks, providing augmented assistance and coaching to do it the best way. In addition, AI-powered chatbots and virtual agents are helping workers more easily pull information from ERP systems and efficiently work with large volumes of data. These AI systems can learn over time about how specific supply chain issues were resolved in the past, giving human workers the tools needed to respond to future events with greater speed and accuracy.

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Machine learning is revolutionising supply chain management

Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

The ten ways machine learning is revolutionising supply chain management include:

• Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy. One of the most challenging aspects of managing a supply chain is predicting the future demands for production. Existing techniques range from baseline statistical analysis techniques including moving averages to advanced simulation modeling. Machine learning is proving to be very effective at taking into account factors existing methods have no way of tracking or quantifying over time. 
• Reducing freight costs, improving supplier delivery performance, and minimizing supplier risk are three of the many benefits machine learning is providing in collaborative supply chain networks. 
• Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies. Combining the strengths of unsupervised learning, supervised learning and reinforcement learning, machine learning is proving to be a very effective technology that continually seeks to find key factors most affecting supply chain performance. 
• Machine learning excels at visual pattern recognition, opening up many potential applications in physical inspection and maintenance of physical assets across an entire supply chain network. Designed using algorithms that quickly seek put comparable patterns in multiple data sets, machine learning is also proving to be very effective at automating inbound quality inspection throughout logistics hubs, isolating product shipments with damage and wear.
• Gaining greater contextual intelligence using machine learning combined with related technologies across supply chain operations translates into lower inventory and operations costs and quicker response times to customers. Machine learning is gaining adoption in Logistics Control Tower operations to provide new insights into how every aspect of supply chain management, collaboration, logistics and warehouse management can be improved.
• Forecasting demand for new products including the causal factors that most drive new sales is an area machine learning is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. Machine learning is proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
• Companies are extending the life of key supply chain assets including machinery, engines, transportation and warehouse equipment by finding new patterns in usage data collected via IoT sensors. The manufacturing industry leads all others in the volume of data it produces on a yearly basis. Machine learning is proving to be invaluable in analyzing machine-derived data to determine which causal factors most influence machinery performance. Also, machine learning is leading to more accurate measures of Overall Equipment Effectiveness (OEE), a key metric many manufacturers and supply chain operations rely on.
• Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted. On average, a typical company relies on external suppliers for over 80% of the components that are assembled into a given product. Supplier quality, compliance and the need for track-and-trace hierarchies are essential in regulated industries including Aerospace and Defense, Food & Beverage, and Medical Products. Machine learning applications are being introduced that can independently define product hierarchies and streamline track-and-trace reporting, saving thousands of manual hours a year a typical manufacturer invests in these areas.
• Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each. In manufacturers who rely on build-to-order and make-to-stock production workflows, machine learning is making it possible to balance the constraints of each more effectively than had been manually in the past. Manufacturers are reducing supply chain latency for components and parts used in their most heavily customized products using machine learning as a result.
• Combining machine learning with advanced analytics, IoT sensors, and real-time monitoring is providing end-to-end visibility across many supply chains for the first time. What’s needed in many supply chains today is an entirely new operating platform or architecture predicated on real-time data, enriched with patterns and insights not visible with previous analytics tools in the past. Machine learning is an essential element in future supply chain platforms that will revolutionize every aspect of supply chain management.

Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.

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A few Ways To Streamline The Supply Chain Using AI

AI can significantly improve business operations by leveraging the tremendous amount of data generated by sensors monitoring the production and movement of products using IoT. The end result is AIIOT, which is the merging of AI and IoT to manage inventory, logistics, and suppliers with a higher level of awareness and precision.

The supply chain is one area that can benefit the most from streamlining since it has a direct influence on profitability and customer satisfaction. There are already several implementations where supply chain efficiency is improved due to AI and machine learning, such as:

1. Predictive maintenance

By utilizing sensors to monitor operational conditions, technicians can be alerted in advance of potential equipment problems and service machines based on real-time wear and tear instead of scheduled service visits based on general manufacturers’ recommendations.

Siemens has successfully implemented predictive maintenance for NASA’s cooling systems at Edwards US Air Force Base in California by monitoring the performance of fans, pumps, air handlers, and cooling towers to gain insights into potential reductions for maintenance and operating costs. Deutsche Bahn (DB) and Siemens have launched a pilot application for the predictive servicing and maintenance of high-speed trains.

2. Smarter shipping

Algorithms are being used to manage last minute changes including selecting the best alternative port when the original port is blocked, estimating times-of-arrival, and even gauging the likelihood that a carrier will cancel a booking.

AI is also being used to calculate the influence of extreme weather on shipping schedules. IBM and its subsidiary, The Weather Company, utilize 100 terabytes of weather data a day to produce location specific weather forecasts that measure potential delays due to storms, hurricanes and typhoons.

3. Warehouse management

AI has the ability to identify inventory and order patterns to reveal which items are selling and should be restocked first. Voice recognition can also be used for increasing product picking efficiency and accuracy by enabling a Warehouse Management System to tell workers through a headset which item to pick and where it’s located.

Once the item is found, the worker reads the item’s number and, using speech recognition, the system recognizes the worker’s voice and confirms the picked item. The more it’s used, the system is “trained” to learn the worker’s tone and speech patterns, allowing the associate to work hands free and more safely.

In addition, when implementing IoT, every single component of a given product can be tracked from when it’s first manufactured to when it’s assembled and shipped to an end customer. BMW follows a part from the point it was manufactured to when the vehicle is sold from all of its 31 assembly facilities located in over 15 countries utilizing machine learning to optimize logistics.

4. Delivery

The last mile is essential, and shipping companies are competing to have the most advanced and efficient delivery services. DHL is investing in intelligent robotic workers in its warehouses and air freight centres, semi-autonomous trucks that drive independently with minimal human intervention for the long haul and “follow-me” robots that can carry loads for delivery people in urban settings.

At UPS, predictive analytics are used for planning constantly-changing driver routes and delivery schedules including predicting outcomes given a certain set of route conditions. Six-wheeled robots are making food deliveries across London launched by Starship Technologies, a company set up by the co-founders of Skype. The self-driving machines are packed with nine cameras and GPS and are monitored by real people who can immediately step in and take control remotely when necessary.

In addition to being more cost efficient and kinder to the environment, they can monitor and maintain the proper temperature for improved customer satisfaction.

5. Supplier management

The data aggregated by tracking the supply chain is also important, because it lets companies tailor their own production schedules, as well as pinpoint vendors that may be costing them unnecessary resources.

According to IBM, up to 65% of the value of a company’s products or services is derived from its suppliers. This high percentage provides companies with a huge incentive to manage the relationship more efficiently.

Algorithms can ensure supplier selection is objective by incorporating qualitative and quantitative performance measures. By creating decision models based on several different factors, AI enables procurement to become strategic, moving beyond the traditional role as price reducers.
AI pays for itself quickly

Using AI to streamline the supply chain has many financial benefits, but it does require an investment. There are certain practical requirements for a working AI system, including a flexible infrastructure for complying with regulations, scalability to manage huge data volumes, as well as seamless data integration to feed machine learning algorithms. For AI to be used effectively, several different systems need to be integrated including Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems.

Regardless of infrastructure costs, due to increased efficiency for every stage of product delivery, AI pays for itself quickly. With all the recent innovations in machine learning algorithms, it’s only a matter of time before AI is an expected and necessary part of managing the supply chain.

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AI in Supply Chain

Supply chain AI is helping enterprises that rely on the movement of physical parts and products streamline their operations and automate tricky last-mile problems.

There's no area of the enterprise seeing greater positive value from AI investment than business process operations. A recent survey by BI Intelligence revealed that supply chain and operations together are the third most active area implementing AI technology, with over 42% of enterprises that responded already seeing revenue gains from AI investments. In another McKinsey study, enterprises that have invested in AI technology for transportation and logistics increased profit margins by more than 5%. Where is all this positive return coming from with AI?

Automating and streamlining operations functions

The biggest area where AI is making its presence felt is in automating many of the previously manual and time-consuming processes that, while necessary, are a big drag on corporate bottom lines. For companies that have large supply chains with millions of orders or purchases to process, handling invoicing and procurement processes can be a significant drag. Increasingly, enterprises are putting AI in supply chain processes, using tools like computer vision to handle invoices and process automation tools to handle moving information across disparate systems. Many of these systems can also perform regular audits of data, catching bad actors, as well as improper or mistaken information, before they cause larger economic impact. In addition, AI plays a role in inventory management by using image recognition to perform inventory analysis and constant inventory audits.

Part of what's driving all this activity is the sheer amount of data generated throughout supply chain operations. AI and machine learning excel in handling large volumes of data, spotting patterns and anomalies, and otherwise providing intelligence and context from the mountain of data available. AI systems can spot when orders are trending the wrong way, reroute shipments when exceptions happen and handle customer support-related issues in an automated manner, reducing the need for human involvement. These systems also help improve forecasting and planning, especially around inventory, which can generate significant benefits, as well as positive ROI, for organizations that are inventory-sensitive.

In a recent report from shipping and logistics company DHL, the company said it is using AI across the board in its logistics operations. For example, DHL is using computer vision to handle labeling- and tracking-related processes, as well as inspect the condition of packages. The company is also using cognitive technologies to increase the development of autonomous transportation systems and to predict fluctuations in global shipment volumes before they occur. The company sees AI playing an augmentative role, not replacing humans but rather eliminating routine work so as to shift the labor force to higher-value work.

Improving inventory forecasting

AI systems have proven to be particularly good at spotting patterns in data that are not immediately identifiable with traditional data analytics or statistics methods. In the areas of logistics and supply chain, machine learning algorithms have been applied to identify which products are selling faster or slower than anticipated and predict more accurate inventory forecasts. Putting AI in supply chain processes can improve the accuracy of inventory forecasting, thereby reducing the understocking or overstocking of goods. These inventory forecasts, in turn, help product-driven organizations become a lot more efficient, reducing warehouse- and inventory-related costs, increasing just-in-time delivery of goods and generally improving overall customer satisfaction.

Similarly, companies are applying AI to address supply chain-related problems, such as equipment failures or unexpected issues relating to weather or regional disruptions. Machine learning algorithms have been applied to find optimal shipping routes, use intermediate warehouse locations to store goods en route to customers and even make predictions of potential service disruptions.

Automating fulfillment

Of course, one of the biggest areas where AI is gearing up to potentially disrupt the supply chain is in warehouses and the entire fulfillment side of the supply-side equation. Companies are making increasing use of automation, robotics and autonomous capabilities to complete the whole cycle of receiving inventory, stocking warehouse shelves, picking and packing products, and delivering to customers. In the not-too-distant future, few humans will be involved in this process.

Amazon has proven that its Kiva bots are capable of automating most of the pick, pack and stock functions. Baidu and Alibaba have made similar advancements to their own warehouses and stocking points. In a sign of more advancement, Siemens is currently operating a "lights-out factory," which has automated so much of the production process that it can operate autonomously in near-dark conditions without any human involvement for weeks at a time. Other companies, including Fanuc and Philips,  operate lights-out factories that need no human involvement, even eliminating the need for heating or cooling for the facilities.

On the transportation side, technology firms are working hard to create autonomous delivery vehicles from trucks to delivery cars to drones. The last mile of delivery is usually the most complicated, and as such, many companies are working on technologies to help make this easier. Amazon Prime has experimented with drones, Google's Waymo is working on delivery vehicles and Uber's Otto is working on autonomous trucking.

Reducing fraud and waste

The leader in using AI to reduce fraud and waste is Amazon, which remarkably started applying AI to its supply chain as early as 2004. Since then, the company has seen a huge increase in supply chain reliability, reduction of shipping errors, reduction in supply- and order-related fraud and bad debts, and improvements in operational efficiency. Following in Amazon's footsteps, similar organizations are rushing to add AI in supply chain processes to realize the same benefits.

Additionally, AI systems are being applied to help with sourcing of products and using interest in products to assist with negotiating favorable pricing. In this way, companies that take advantage of AI can use their data to gain a strategic advantage over other companies that are simply using historical information and long-term pricing contracts that lock them into inefficient inventory allocations and poor cash flow. Companies that optimize their supply chains in this way don't need to have end-of-season clearance sales since there won't be anything to clear.

Augmenting and improving worker productivity and safety

AI is also being used to improve the productivity of workers that are involved in the supply chain. Machine learning systems can learn the proper and optimal behavior patterns of workers and monitor how employees execute tasks, providing augmented assistance and coaching to do it the best way. In addition, AI-powered chatbots and virtual agents are helping workers more easily pull information from ERP systems and efficiently work with large volumes of data. These AI systems can learn over time about how specific supply chain issues were resolved in the past, giving human workers the tools needed to respond to future events with greater speed and accuracy.

Given all the ways in which AI in supply chain is helping, it's no surprise how far e-commerce and B2B supply chains have come with the use of cognitive technologies. Companies that are adopting AI are seeing dramatic improvements in their ability to respond to customers, suppliers and the changing environment. Those that aren't are quickly being left behind.

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AI and Digital Marketing

Doing marketing prior to the commercialization of AI involved using complex tracking methods, measuring every possible metric and extensively number-crunching these metrics, analytics and KPIs in an effort to evaluate the effectiveness of each activity, channel and partner. Being a digital marketer had become synonymous with being a data nerd. The creative aspect of marketing had become less important, and the technical side of it had been prevailing. Then AI came along and changed the game.



We could say that the relationship between AI and digital marketing resembles that of an old wizard and his young and willing apprentice. The old wizard can reiterate the rules and principles, but the young apprentice can put them into practice, test them and see if they work.

Here are some of the ways in which AI is revolutionizing digital marketing.

Deeper customer insights

Thanks to social media outreach and big data, brands now know their customers better than they ever did. AI is helping collect and interpret consumer insights from all platforms and points of interaction: websites, e-commerce purchases, support and contact forms, chatbots, email newsletters, social media engagement (shares, follows, likes, comments), forum comments, customer reviews, and more. The data footprints of internet users today are substantial and continue to grow, as people go about their lives online.

Personalized user experiences

The volumes of data generated online make it easy for corporations to build sophisticated profiles on who we are, what our beliefs, tastes and preferences are. As we have recently seen, data scientists can even guess and influence our political convictions based on data willingly provided on social platforms. Whether this is a positive development is up for debate, though, one thing is for sure – users will no longer need to be subjected to generic ads of little relevance or interest to them.

Image recognition for ROI maximization

The early days of AI saw mostly textual and numerical data being processed and analysed. Today’s machine-learning algorithms are becoming smarter and more sophisticated, however, to the point where they can determine what’s in an image and which types of images are most engaging to a specific user.

They do this by recognising image patterns and gradually learning about the emotions and reactions certain images invoke in people by measuring their social engagement.

Simpler decision-making with predictive marketing

The entire premise of collecting terabytes of user data from all possible channels is to use it for smart decision-making. Future user behaviour can be predicted more reliably by analysing historical data and drawing patterns – something machine-learning algorithms are adept at doing. This allows organizations to carefully plan their marketing and advertising budgets and to invest only in channels, campaigns and messaging that produces the desired results. Marketers no longer need to wait for months to gather and analyse data on consumer behaviour – AI does this for them quickly, efficiently and inexpensively.

Future-proof skills

With the proliferation of AI technology and the companies offering it as a service, many digital marketers are becoming fearful of losing their jobs, which is not dissimilar to what numerous other sectors and professions are experiencing.

What are humans to do in a world where machines are capable of making smart decisions more accurately, rapidly and efficiently? The good news is that marketing has the potential of going back to its roots and becoming more creative again. Once the focus is no longer on analyzing data, marketing will have the space and time to become its true self again, where creating the most engaging and sticky experiences for consumers is the priority.

5 Essential Benefits of AI for Digital Marketers

IMPROVED PERSONALIZATION

Personalization was definitely the buzzword in the world of marketing in 2018 and we’re going to see this trend become even more important over the next 12 months and beyond.

The way that consumers respond to and interact with marketing messages is changing. Traditional marketing methods like media advertising and direct mail are no longer as effective as they once were.

One of the reasons for this is, today’s consumers expect brands to tailor messages to their location, demographics, or interests. Many will not engage with or even may ignore non-personalized marketing.

A report by management consulting firm Accenture found that over 40% of consumers switched brands due to a lack of trust and poor personalization in 2017. 43% are more likely to make purchases from companies that personalize the customer experience.

At the same time, Gartner predicts that while 90% of brands will use some form of marketing personalization by 2020, most will fail to produce optimally personalized content.

The answer to both improving personalization and producing more and better content is in AI. By analyzing customer data, machine-learning algorithms enable marketers to offer a hyper-personalized customer experience and optimize the content production process, as we’ll expand on in the next point.

EFFICIENT SCALING OF CONTENT CREATION AND CURATION

Content marketing offers an impressive return on investment. But it can also be resource intensive. As mentioned in the Gartner predictions, most brands struggle, not with collecting sufficient data, but with producing enough content to ensure a personalized experience for everyone.

AI can help to speed up and optimize your content marketing in several ways.

AI-powered content strategy tools like Concured enable marketers to quickly and easily identify their best performing content, efficiently plan future content, repurpose existing content, and distribute it on several channels, with everything scheduled for optimal visibility.

You may not be thinking about replacing your copywriter with AI software just yet but we may be closer to this than you think. Several global brands, including Forbes, are now publishing content that’s at least partly generated by AI.

This use of AI makes content production much faster and more efficient and enables marketers to scale up their content marketing – something that 47% of marketers say is their biggest challenge.
Curated content is yet another way to scale up without using your own resources. AI is highly efficient at finding and selecting the right content for your audience, enabling you to automate the curation process.

AUTOMATED MARKETING PROCESSES SAVE TIME AND MONEY

Marketing automation has been around for quite some time. You don’t copy and paste content into thousands of emails, manually changing the name each time – email marketing software can do this for you in seconds.

AI-powered automation software enables you to ramp things up a notch and takes away some of the burden of decision making.

You can use AI-powered software to help you to decide not only what content to create, but also when and how to publish and distribute it. The whole process can be automated with a single click.

By turning over these repetitive tasks to marketing software, you can increase your productivity and focus your efforts on strategic marketing planning, talking face to face with customers, and other areas where humans excel over computers.

REDUCED DATA ERRORS

Humans are better than machines at doing many things but they are also prone to making errors. This is particularly true when it comes to using data, especially large quantities of data.

Intelligent machine algorithms can process vast quantities of data without ever getting tired or making a single mistake. They’ll never call your customer the wrong name (unless you’ve made a mistake in instructing them!) because they’re looking at the wrong record in the database.

You can also use AI to reduce errors due to duplicated or out-of-date data. Software can parse and merge several databases, combining intelligence from many different sources without resulting in duplicate data.

AI is also effective at analyzing data, spotting patterns, and making accurate predictions. Human predictions and analysis tend to be biased and based on limited past experience or arbitrary decisions. Machines process data and calculate results based on statistics and prediction analysis rather than intuition.

PREDICTING CUSTOMER BEHAVIOR IMPROVES ROI

Because machines are so good at spotting patterns in data, they can often tell what a customer is going to do before he’s even decided himself.

AI-software uses data and statistical models to predict future behavior based on past behavior and characteristics. It does this with startling accuracy.

When you can anticipate the actions and buying behavior of a particular customer, you can send them highly targeted marketing messages and nurture them through a unique buying funnel that’s constructed to optimize sales.

This sounds complicated but artificial intelligence does all the hard work for you. With the help of intelligent software, you can not only gain new insights about your customers but also automatically deliver marketing messages at exactly the right time for the best chance of a sale.

AI helps to identify your most valuable leads so your sales team can concentrate on them, rather than wasting time on leads that aren’t ready to buy.

This streamlines your whole marketing strategy and helps to increase sales while reducing the time and resources you spend on tasks like manual lead scoring, sales page optimization, and retargeting leads.

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Digital Beyond the Transformation

“Digital Beyond the Transformation” is about bringing digital to the core of processes, people, and products, so that enterprises evolve into change agents, fully equipped to keep pace with emerging technologies and the disruptions that may ensue.

In the early years of digital transformation, value shifted significantly from the infrastructure to the application layer with the rise of mobile, front-end experiences becoming all the rage, and the back-end moving to the API economy. The infrastructure layers were considered a “commodity” after the wave of commoditization of hardware and the lower levels of software that ran on it.

However, a dramatic shift happened with the rise of two entirely new waves of infrastructure modernization:

• the public cloud and complete reimagining of core back-end middleware 
• the surge of shared memory and isolated application infrastructure with containers

Couple these with the third wave of DevOps, and now you have infrastructure that is CODE.

This shift has required digital enterprises to master new skills pertaining to cloud engineering. This also led to an unprecedented increase in the pace of digital disruption, subsequently driving enterprises in various industries to embrace digital transformation. These are the identified five specific technological capabilities and business best practices which are foundations for making Digital Beyond the Transformation a reality:

1. Cloud and Microservices transformation

Cloud and Microservices have moved from geeksville to boardroom conversation as enterprises across a wide range of industries—from Finserve to CPG and retail to hospitality—are learning of the myriad benefits that these technologies have to offer. The cloud has evolved into the beating heart of the digital ecosystem, as enterprises are increasingly turning to a cloud-focused business approach to innovate their offerings, scale their business, and satisfy customers.

91% of enterprises will move their core business operations to a Cloud-first environment by 2019 

The cloud reduces capital expenditure on IT infrastructure, on-premise resources, and software licenses. It allows for infinite scalability and delivers the speed and agility for new capabilities to better cater to changing customer demands.

Microservices, on the other hand, enable enterprises to adopt a decentralized approach to software development, wherein each service can be deployed, rebuilt, and managed independently without compromising the integrity of the application. They provide immense scalability and support a wide range of devices and platforms such as mobile, wearables, and the Internet of Things.

APIs are the foundation of digital experiences. APIs play a critical role in driving digital business transformation by enabling enterprises to go to market faster. A business strategy based on APIs and Microservices architectural frameworks—coupled with cloud infrastructure—gives businesses the ability to facilitate innovation and move at the accelerated speed of digital.

An API framework removes the complexities of underlying systems through the use of a Microservices architecture. The results are seamless and scalable functions acting as separate services and then connecting through a middleware API layer versus many point-to-point connections.

2. Global digital platforms

A global digital platform operates seamlessly across the globe, with customer experience being the center of the universe and technology complexity very much present but hidden from the customer. Despite the differences in the laws, regulations, services offered, and payment methods across different countries, a global platform is capable of working flawlessly to deliver relevant, timely, and convenient consumer experiences.

For instance, the world’s largest beauty leader needed a feasible, cost-effective, and scalable approach for launching and managing hundreds of marketing websites across different countries worldwide. Hiring multiple agencies and deploying localized websites on different platforms was not a sustainable approach from an operations and financial standpoint. The company, therefore, worked with a Vendor to create a singular global platform app—which serves as a framework for localizing master websites and set up a central team to manage and support master sites and each localization.

All integrations were done at the global platform app level and, therefore, the cost and effort required to deploy the localized versions of the master site were reduced significantly. The company used the larger portion of its budget to build a master site for their primary market. For the local markets only localiztion costs are billed on the build, and then the run cost of any integration are incurred as the setup cost was already covered on the master site. Therefore, with a centralized team to build and support the sites and the sharing of IT resources, the company was empowered to scale, catering to diverse customers across the world while realizing significant cost savings and continuity in brand expansion.

3. Organizational structure for the digitally transformed enterprise

Enterprises are making incessant efforts to figure out the right digital operating model. However, it takes time as well as experience to perfect an operating model at scale. Merely throwing together buzzwords—like MVP, POD, Squad, Tribe, Agile, Sprint, and Stand Up—does not ensure value in terms of digital transformation. And agile does not mean no discipline. On the contrary, teams must plan, stage gate, and measure plan to performance.

As for digital initiatives, it’s good to start small. Show what “good” looks like before scaling or investing further time and capital in the idea. Further, it is a good practice to measure time to value in 12-week blocks.

By integrating digital at the core of process, people, and products, enterprises can evolve into nimble organizations and facilitate digital innovation.

Creating an organizational structure for digital transformation requires high organizational change, a cultural shift as big as the tech shift. Change management and leadership buy-in often pose challenges in terms of restructuring an organization; however, the return on investment is massive. The efficiency of a well-oiled digital operating model is significantly greater than any traditional model out there. 

4. Disruptive technologies for digital innovation

The digital space is witnessing an exciting time with growing innovations around disruptive technologies such as artificial intelligence, Machine Learning, Big Data, virtual or augmented reality, and blockchain. These technologies have been providing tangible proof of their value but are still in the early stages of their lifecycle with a potential for generating astronomical value in the long term.

Smart organizations are moving to an AI-first world wherein businesses are using tools like chatbots to take their customer experience and engagement to the next level.

For instance, if a Fortune 100 insurance company wanted to accelerate its digital journey through innovation to engage prospects and customers across new emerging channels. A vendor helped the company build their innovation roadmap and implemented an integration across chatbots, natural language processing, and voice-enabled platforms to enable customer service in the users’ channel of choice, including:

• Intelligent assistants (voice-enabled product search) to help navigate the app and give the most suitable product recommendations
• Amazon Echo integration allows customers to make use of the self-service capabilities, through voice, and effectively use Alexa skills to enable simple insurance service offerings like “Make a Claim,” or “Pay my Bill” concepts
• Utilization of chatbots for product marketing, increasing customer interaction, and improving overall conversions of new product launches

Cloud and open source have made the adoption of disruptive technologies easier and faster than ever. However, we recommend first defining the business problem that you are trying to solve. Then assess whether or not the problem can be solved by traditional computational methods. If not, the problem could be a great use case to apply an AI/Machine Learning or a Big Data stack.

1. By 2022, at least 40% of new application development projects will have artificial intelligence co-developers on the team
2. By 2022, more than 50% of all people collaborating in Industry 4.0 ecosystems will use virtual assistants or intelligent agents to interact more naturally with their surroundings and with people

5. Design thinking for the future of the connected consumer

In today’s hyper-connected, consumer-driven world, enterprises are increasingly realizing the true value and potential of design thinking.

Below are the main principles of design thinking:

• Empathize: Understand the path of the customer
• Analyze: Identify friction and pain points
• Ideate: Brainstorm solutions and look for cross-industry inspiration
• Prototype: Prioritize ideas and bring definition to the solution

In our view, there are two salient parts of mastering design thinking. The first is empathy—designing your product, features, and processes with the customer in mind. Even though it seems simple, this is a non-trivial task. In our experience, the change in the mindset of putting yourself in the shoes of the customer and then re-envisioning a product is the central pivot to getting it right. We believe that the design thinking mindset will evolve to encompass not only the product design process but an organizational design methodology as well.

The second is the number of channels across which brand experiences are to be designed. The channels may be right from wearables to connected devices or personal computing to VR experiences. The blurring of the lines between physical and virtual is not a vision anymore, it is already a reality today.

Plus 1: How to avoid being “Amazoned”

As Amazon continues its pursuit of aggressively disrupting a wide range of industries—including eCommerce, IT, logistics, entertainment/media, and consumer electronics—the threat of getting “Amazoned” is real indeed. Or is it?

A digital-first business strategy entails:

• Creating frictionless customer experiences across touchpoints
• Enhancing mobile experience with personalized services
• Implementing a fully integrated digital user experience framework
• Enabling cloud migration towards achieving greater agility
• Providing infrastructure support
• Undertaking application development or offering application support (managed services)

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ADVERTISING & ARTIFICIAL INTELLIGENCE

Artificial intelligence is no longer a phenomenon of the future – it’s here, right now. AI is present in our daily lives and nearly every industry. In the world of advertising, developments in AI are molding the work we produce, how we’re doing that work, and the way we analyze and improve on it.

Creativity is more important than ever, working hand in hand with AI to leverage strategic insights from ever-increasing amounts of data; creative turns information into action.

Facebook uses AI to show users content that they care about, both organically and through paid advertising. Their algorithm is constantly learning to serve users ads based on the way they interact with Facebook apps. We utilize Facebook’s machine learning to show users relevant and actionable ads.

For example, if we optimize an ad campaign for traffic or landing page views, we are telling FB to show ads to users that normally click through to websites from their News Feed, Instagram, etc. AI provides access to Facebook user’s behaviors, so we can provide more relevant creative and targeting.

AI is what allows Programmatic to similarly reach and target specific audiences, encompassing recipients from dozens of exchanges and thousands of websites.

In the year ahead, Dynamic Creative Optimization (DCO) is expected to make digital ads more difficult than ever for consumers to resist. DCO is a form of Programmatic and Facebook advertising that allows us to alter creative elements of the ads we’re running in real-time to optimize their performance. We can update graphics, colours, copy, and calls-to-action to personalise messages to what is or is not resonating with the target consumer.

AI enables these unprecedented levels of personalization by factoring in location, consumer preference, and demographic information to re-target consumers when they actually need a product or service.

Artificial Intelligence enables newfound levels of security, and could essentially eradicate brand safety concerns in 2019 by helping brands serve ads more securely online. View ability fraud is a type of display fraud where ads are not visible to a user because of shady techniques like ad stacking or ad masking.

Ad stacking is when multiple ads are layered on top of each other in a single ad placement. In this scenario, when the user clicks on the visible ad, it drives up clicks registered for all ads in the stack. Ad masking is when an ad is hidden behind page content, similarly registering false impression and click activity.

Display and view ability fraud have traditionally been major concerns for brands, but because programmatic is continually learning, we can utilise its brand safety segmentation capabilities to ensure that ads are not served fraudulently, or anywhere that may conflict with a brand’s image and goals.

Fraud and view ability feeds within programmatic ensure brands avoid display fraud or impression fraud by providing them real-time protection, blocking IP addresses it has learned to be fraudulent, and filtering out media sources or campaigns with high numbers of impressions and low numbers of actual clicks (likely the result of ad masking).

In regard to brand safety on social media, Facebook’s machine learning algorithms have text and speech recognition capabilities that enable it to rank ads and search results, keeping spam and misleading content like “Fake News” from feeds. These brand safety controls prevent ads from being delivered alongside any content that isn’t conducive to a brand, by enforcing community standards for what is shareable on the platform, allowing relevant targeting capabilities, and providing controls for where ads are shown.

Overall, as AI becomes increasingly sophisticated it’s helping brands serve safer and more secure ads, resulting in a more transparent industry.

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Using Matplotlib Built-in Styles to customize plots

Matplotlib has a number of predefined styles available, with good starting settings for background colors, gridlines, line widths, fonts, font sizes, and more that will make your visualizations appealing without requiring much customization. To see the styles available on your system, run the following lines in a terminal session:

C:\Users\python>python
Python 3.6.5rc1 (v3.6.5rc1:f03c5148cf, Mar 14 2018, 03:12:11) [MSC v.1913 64 bit
 (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import matplotlib.pyplot as plt
>>> plt.style.available

['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'gray
scale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn
-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook',
 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-t
icks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tabl
eau-colorblind10', '_classic_test']
>>>

To use any of these styles, add one line of code before starting to generate the plot. For example to use seaborn we can use plt.style.use('seaborn') as shown in the code below:

import matplotlib.pyplot as plt

input_values = [1, 2, 3, 4, 5]
cubic_values = [1,8,27,64,125]
plt.style.use('seaborn')
fig,ax = plt.subplots()
ax.plot(input_values,cubic_values, linewidth=3)

# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)
plt.show()

The output of the program is shown below:

If we use the classic style we can use plt.style.use('seaborn') in our code and our output should change to the figure shown below:

A wide variety of styles is available; use these styles in your programs as per the desired look and feel.

Scatter() method

Sometimes, it’s useful to plot and style individual points based on certain characteristics. For example, you might plot small values in one color and larger values in a different color. You could also plot a large data set with one set of styling options and then emphasize individual points by replotting them with different options.

To plot a single point, use the scatter() method. Pass the single (x, y) values of the point of interest to scatter() to plot those values:



Now let's style our output by adding a title, label the axes, and make sure all the text is large enough to read. See the code below:

import matplotlib.pyplot as plt

plt.style.use('seaborn')
fig, ax = plt.subplots()
ax.scatter(2, 4, s=200)
# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)
plt.show()

In the above program we call scatter() and use the s argument to set the size of the dots used to draw the graph. When we run the above program we see a single dot in the middle of the chart as shown below:


To plot a series of points, we can pass scatter() separate lists of x- and y values as shown in the following program:

import matplotlib.pyplot as plt

x_values = [1, 2, 3, 4, 5]
y_values = [1, 4, 9, 16, 25]

plt.style.use('seaborn')
fig, ax = plt.subplots()
ax.scatter(x_values, y_values, s=100)
# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)
plt.show()

The x_values list contains the numbers to be cubed, and y_values contains the cubic value of each number. When these lists are passed to scatter(), Matplotlib reads one value from each list as it plots each point. The points to be plotted are (1, 1), (2, 4), (3, 9), (4, 16), and (5, 25); Figure below shows
the result:



Now let's modify our program so that we use a loop in Python to do the calculations for us rather than passing our points in a list . See the program below:

import matplotlib.pyplot as plt

x_values = range(1,101)
y_values = [x**3 for x in x_values]
plt.style.use('seaborn')
fig, ax = plt.subplots()
ax.scatter(x_values, y_values, s=10)
# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)

# Set the range for each axis.
ax.axis([0, 110, 0, 110000])
plt.show()

We start with a range of x-values containing the numbers 1 through 1000. Then a list comprehension generates the y-values by looping through the x-values (for x in x_values), cubing each number (x**3) and storing the results in y_values. We then pass the input and output lists to scatter(). As this is a large data set, we use a smaller point size. Finally we use the axis() method to specify the range of each axis. The axis() method requires four values: the minimum and maximum values for the x-axis and the y-axis. Here, we run the x-axis from 0 to 110 and the y-axis from 0 to 110000. When we run the program, the output will be as shown below:



In the next post we'll see how to change the color of the points. Till we meet next keep practicing and learning Python as Python is easy to learn!.

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