#ArtificialIntelligence Archives

Here’s how AI is transforming business processes

Posted on 16/06/202005/06/2021 by Blogs

Business process are transformed through AI

The rise of artificial intelligence (AI) to drive business value has been truly incredible in recent years. Enterprises that recognize the power of AI and know how to effectively apply it to their business can reap significant rewards in a quickly evolving and hyper-competitive marketplace.

Just a few years ago, analytics was all about gaining insights from data to help make better business decisions. More recently, enterprises have been seeing massive benefits from adding AI to the analytics mix that is designed to transform and strategically influence business processes. The effective application of AI within business process transformation can produce many benefits including more efficient operations, faster delivery and reduced costs.

But how is AI actually helping companies transform business processes?

There are two primary ways AI is doing this today. Some enterprises are actively implementing AI programs company-wide as part of their core functionality. And there are other companies that are building AI into their business in a sequential, controlled way via managed proof-of-concepts (POCs) to address particular aspects of their operations.

Artificial Intelligence Is Transforming Business

AI is specifically used as a tool to speed up the corporate buying process. It does this in two ways: by making recommendations of suitable suppliers who should be invited to a tendering process and by quickly sifting through dozens of supplier submissions and creating a ranking system to identify the best supplier agencies for bespoke projects. This type of accelerated procurement can shave weeks off the selection process and additionally save up to 20% on project budgets.

On the other side of the spectrum is a large UK retail chain with thousands of stores across the country. This company is implementing AI in a controlled way through their corporate transformation department and rolling it out to each store. The retailer is conducting live trials of the system and can conduct A/B testing for different approaches for 10% or 20% of their stores from the get-go, which did not happen before.

This retailer is seeing success by implementing AI to assist with critical business functions such as setting prices and managing stock. AI is taking on the work previously performed by humans, including analyzing pertinent purchasing data to set prices intended to keep products flying off the shelves and boost profit margins.

From the two cases above, we’ve seen that currently the best performance in AI applications is achieved when AI is combined with humans; where AI does the core number crunching and recommendations and humans oversee the process. This helps humans concentrate on fine-tuning and making improvements on those initial recommendations.

Time and budget savings are achieved during the corporate buying process due to the presence of AI. And for the retailer, cost savings are clearly achieved as AI does the routine processing work that was previously performed manually by humans.

These uses of AI yield fewer mistakes and demonstrate how AI can support efforts to optimize spend, ultimately impacting the bottom line. For both companies, AI is the go-to solution for solving business problems.

Humans will always have a place in transforming business processes, that goes without saying. But AI is quickly becoming an invaluable automation tool to drive efficiencies and reduce costs.

Five essential pillars of AI-enabled business

Posted on 15/06/202005/06/2021 by Blogs

Successful AI implementations rarely hinge on the unique innovation of a specific algorithm or data science technique. Those are important factors, but even more foundational to successful AI enablement are the core data operations and enabling platforms. These act as the fuel and chassis of the AI machine that a business must build and evolve for continued competitive advantage.

Here are the five foundational elements to be addressed to enable a successful transformation to an AI-empowered business:

1. Define an integration strategy for embedding AI and analytic insights into business operations

Successful digital transformations focus on evolving and optimizing business operations through the better use of data assets combined with modern technologies such as machine learning, AI, and robotics. These paradigm shifts result in the creation of new operating patterns rather than simply more efficient legacy operations. In this way, digital transformation represents the enterprise operations in the way the business wants to be run, rather than the way it has been running due to technical and operational limitations and barriers constraining it.

To go beyond siloed or single-use insights and fully benefit from AI and analytics, it must first be decided how the business desires/needs to function in the future. Determining your business transformation priorities then evaluating the advanced technology and data science options for addressing them is a key step towards maturing and evolving to a data-driven enterprise. This understanding will identify the type of AI and analytics that will be the most beneficial for your business and the technology required to accomplish it. Additional thoughts on overall data strategy can be found in the white paper “Defining a data strategy: An essential component of your digital transformation journey.”

2. Establish a holistic data and analytics platform

Selecting and configuring an integrated set of technologies to support data management and applied analytics is a complex challenge. Fortunately, solutions to such technical integration have matured in recent years into pre-built core platform components and best practices that can be accelerated and augmented further through value-added third party software and partner services.

Cloud-based modular platform environments bring together technical flexibility and financial elasticity with an ever-maturing technical set of capabilities, including interoperability across hybrid environments that include legacy on-premises deployments and geographical federation. In addition to open source components, such platforms include the option to integrate select native modules and commercial technology components for broader flexibility and a customizable architecture that can be deployed as prebuilt services for simpler adoption and integration.

The tools to support and enable AI integration into business operations are beginning to leverage the same capabilities they enable. For example, data pipeline tools are beginning to use machine learning (ML), metadata tools are using AI and ML to identify content and auto-generate the metadata on the fly, and user interfaces are embedding chatbot and digital assistant AI technology to guide end-users through the complexities of data science for accelerated insights. By adopting toolsets and platforms that have embedded AI and analytics in their core, the use and integration of AI into business operations will be more natural and accelerated across the enterprise community.

3. Know your data

Fully understanding the data your enterprise has access to may seem like a fundamental need when supporting operational reporting and analytics within the enterprise. Many organizations, however, stop with simple source systems listings and maybe some high-level business definitions and schemas.

Truly knowing your data includes a lineage-based view of where the data comes from and what business process it represents, what operations are performed on it prior to your access, what transformations are performed thereafter, the associated level of quality and, of course, the core “Vs” of big data; volume, velocity, variety, veracity and value.

Building an easily searchable, enterprise-wide data catalog of information is one of the first steps towards empowering the enterprise with data. Exposing the catalog to a crowdsourced editing model ensures richer content and wider adoption of such information across the enterprise.

4. Control and govern your data

Understanding the types of controls and governance your data needs is a natural extension of knowing your data. By reviewing the types of data and their business content with associated metadata, enterprises can align and define proper governance and compliance policies related to internal policies and to external standards such as HIPAA for healthcare, PCI DSS for secure payments, and PII and GDPR for data privacy.

It is also important that source data retains its original state integrity without over processing or over-filtering it. Aligning to the data pipeline workflow principles of “ingest, refine, consume” allows the same data to be leveraged efficiently for different uses with different policies and operational needs while ensuring security. Such controls can also be extended to support and define quality standards required for using the available data and to trigger any necessary control processes to correct or adjust for deviations in such standards.

You can safeguard proper policy compliance, improve ease of use and increase trust and adoption by the end user community by ensuring that governance controls are built into your data management operations from the start.

5. Simplify access to your data

To further expand the adoption of AI and analytics, it is important to simplify and automate data workflows and the use of analytical tools. Reducing manual process overhead can significantly improve time to market and quality of results. Providing clear and flexible governance allows enterprises to control such access without it becoming a barrier for use.

Self-service leads to rapid user community adoption and better integration of data and insights into business operations. By reducing the dependency on IT resources for complex data integration and preparation tools, average business users can interact with the data through simple common interfaces and receive results in simple and easily consumable formats.

Once these foundational elements are in place, organizations can take full advantage of the unique value proposition offered by advanced analytics and AI. And they can do so with the confidence that the resulting solutions are enterprise-grade in their scalability, security, quality and usability. It is this kind of confidence that leads to business user adoption and, in turn, successful digital transformation.

Why Do SMEs Need to Be Digitally Transformed?

Posted on 15/04/202015/06/2021 by Tushar

Digital transformation is just something for big business, right? Erm, no! In fact, it’s not completely true that bigger organisations led the way: many of the early movers were the disrupter startups that were digital from the offset. Indeed, it’s being digital that enabled them to become disruptors and forced their larger competitors to react. Today, as more businesses and consumers shift towards digital technology, it’s more important than ever for SMEs to consider how digital transformation can help them stay relevant and competitive. Here, we’ll examine why.

Reacting to change

Shifting consumer behaviours, the evolution of new ways of working and the constant roll-out of new technologies means SMEs are consistently having to adapt quickly to change. Not only are more consumers moving online; they are demanding digital services that provide better customer experiences. Employees, meanwhile, are increasingly seeking to work with companies that enable them to work flexibly and away from the office. To acquire and keep new customers and talented employees, and keep up with competitors, SMEs need to be able to adopt new technologies quickly, for example, deploying remote working platforms for staff or personalisation engines for customers.

To do this, they need to be agile, and its digital transformation that enables this. In particular, it’s the adoption of cloud technology, an infrastructure that allows companies to deploy new servers and applications instantly and which provides them with all the scalable resources they need to undertake their workloads. Indeed, by migrating to the cloud, SMBs eradicate the need for expensive, in-house infrastructures, making them even more agile and putting them on a level playing field with their larger competitors.

Taking advantage of AI

Artificial intelligence is starting to permeate all areas of business operations and its easy accessibility means it’s increasingly being utilised in all sectors. Today, SMEs can use AI for a myriad of purposes. It can help find better ways to procure products or materials, make industrial processes faster and more efficient, monitor machine and system health, provide data insights that identify new opportunities in the marketplace, streamline logistics, deliver human-like chat conversations with customers and identify risk, whether that’s financial risk or risk to life.

A key element of digital transformation, AI is something most SMEs can gain enormous benefit from. The good news is that adopting AI is no longer the major technological leap it used to be. Its popularity means there is a growing number of both open-source and proprietary AI applications that are designed to integrate with company systems – the majority of which are cloud-based. For SMEs that have migrated to the cloud, such technology is merely a click away.

Cyber defence

Being an SME doesn’t protect a company from cyberattack, nor does it remove the obligation to comply with regulation and ensure that data is safe. For this reason, SMEs need to develop a robust security strategy and put disaster recovery and business continuity plans into place.

Though ransomware attacks grab the headlines, there are various other ways companies can fall victim, such as through hacking, phishing, malware infection and DDoS attack. Data can be stolen, systems taken offline, websites taken control of and money syphoned from company accounts. It’s a serious issue: 60% of companies that fall victim to a cyberattack fold within 6 months.

The growing sophistication of cyberattacks means criminals can now buy ransomware as a service or brute force software with built-in AI. As a result, SMEs need advanced tools to defend against them. The adoption of these tools is also a key element of digital transformation. Providing this level of security in-house, however, is not only expensive but requires expertise and this puts it beyond the means of many SMEs. In the cloud, however, both the expertise and advanced technologies like next-gen firewalls, intrusion and malware prevention, etc., are part of the service. Companies will also find that vendors provide backup, disaster recovery and business continuity solutions to help them recover swiftly should the worst happen.

Data storage

Data is essential for digital transformation as it provides the information that applications need to process in order to deliver improvements. Today, businesses gather huge quantities of data, not just from customers and their online activity, but from the monitoring of machinery, vehicles, IoT devices, LED lighting systems, energy usage, inventory systems and more. For digital transformation to work, finding the right storage solution for all that data is essential.

SMEs will need to centralise data in order to control access, create customer journeys and ensure employees have access to the latest versions of documents and files. As data grows, businesses need expandable storage rather than having to go through the risk or rigmarole of upgrading to a bigger server whenever capacity is low. That storage also needs to be secure. Cloud storage is ultra-fast, can be expanded easily, has security features like encryption and access control, and is more cost-effective than buying dedicated servers. Crucially, being in the cloud, it enables remote workers to access data wherever they have an internet connection.

Conclusion

More SMEs are undergoing digital transformation than ever before. Indeed, to stay agile and adapt to change, it is becoming a necessity rather than an ambition. It is, however, a necessity that brings with it many rewards. The starting point for digital transformation lies in the adoption of cloud technology, as it is cloud infrastructure with its robust security and cost-effective pricing, on which the applications, tools and platforms of digital transformation are best run.

Most popular questions companies have regarding AI and ML

Posted on 22/02/202015/06/2021 by Tushar

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving to help companies with both digital transformation and innovation. There has been a lot of hype and discussion about these topics, and, in a very short time, we have moved the conversation from “AI is cool” to “AI can drive specific business outcomes.”

Experience has allowed companies to clarify the economics of AI and reduce time to value. In addition, the technology has continually improved, with advancements including:

extracting unstructured data for improved insights and processes
moving from simple chatbots to more sophisticated conversational assistants that are smart, use more natural language interaction (text and/or voice), and enable the initiation of transactions
integrating operational and knowledge management systems

As our clients start to understand more about AI/ML, there are 2 key questions typically asked:

Question 1: “How do AI and Machine Learning differ from traditional programmed systems?”

There are three different capabilities that AI/ML typically extends and enhances into existing applications. One aspect is understanding – enabling systems to understand language, other unstructured data, including pictures, just like humans do. A second aspect is reasoning – enabling systems to grasp underlying concepts, form hypotheses, and infer and extract ideas, similar to humans. The third aspect is focused on learning – improving over time and avoiding repeated mistakes. These capabilities, often referred to as U-R-L (understand, reason, learn), are easily integrated into existing applications as consumable APIs, can reduce time to value.

Question 2: “Where is the value from Artificial Intelligence/Machine Learning?”

As we look across client environments, most clients are very comfortable with structured data that is within their firewall – things like transaction systems, customer records, and even predictive models. Many analysts estimate that 80% of data created today is unstructured, which requires clients to expand their current perspectives in 2 dimensions:

Structured to unstructured, such as documents, transcripts, social media, weather, images, IoT and sensor data, and news
Within the firewall to outside the firewall, including public data, licensed private data, and new types/sources being created every day

From a value perspective, the enticing aspect of AI and machine learning is connecting these structured and unstructured data types within and outside these walls. This enables richer, more, and unexpected insights, new business processes, and improved workflows at dramatically reduced cost levels.

As AI and machine learning mature, three use cases patterns have emerged around customer care, human capital management and the rethinking/reimagining of processes thanks to insights gained from unstructured data and natural language capabilities.

We’ll expand on these use cases and how we are applying AI/ML for our clients in our next blog post.

AI and ML Advancement leading to new Application Emergement

Posted on 20/02/202017/06/2021 by Tushar

With most innovative technologies, at some point in the solution evolution there’s a tipping point from “this is cool” to “there are real business outcomes to gain.” We are now seeing that shift with artificial intelligence (AI) and machine learning (ML), especially in three major use case patterns:

1. Customer care

When it comes to customer care, many companies face a similar set of challenges: Customer service representatives (CSRs) typically need to view multiple windows on multiple screens to find answers to a caller’s questions. This results in calls taking a long time to handle, frustrated customers, lower CSR productivity and overall poor customer satisfaction.

AI and ML can help change these outcomes. One insurance customer implemented AI and ML to transform its contact center, which responds to approximately 5 million voice calls per year. It also introduced an intelligent voice agent to respond directly to 50-70% of all customer inquiries. The power of AI and ML extended and enhanced the customer experience and the productivity of CSRs, who are now able to deliver more accurate and consistent answers, at lower cost and greater speed. As a result, we have also seen reduced attrition, as the CSRs are happier in their roles and require less training time to become self-sufficient. Overall, by using AI and ML, the contact center is able to provide a high level of customer service, enhanced customer satisfaction and more efficient engagement.

In another example, a customer service center handling 60M service requests per year is implementing AI “smart chat” to transform customer service, drive better quality, improve consistency and lower costs.

The transformation of an operation as complex as a customer call center is a multi-step journey:

Organizing and visualizing the data required to support typical workflows is a critical early step and can include authenticating/validating the caller, providing easy access to the data from different back end systems, initiating transactions and closing out the call.
A second step adds natural language processing (NLP) to allow the CSR to ask more complex questions. NLP uses a trained ML model to determine the most likely answers from a collection of large, unstructured documents.
A third step includes self-service for external callers, using voice and natural language to respond directly to client inquiries without requiring interaction with a CSR.

While the steps themselves remain the same, the sequencing may vary to align to the specific outcomes the organization desires.

2. Human capital management

By applying cognitive capabilities to human resources, companies are able to transform their human capital management (HCM) processes.

One valuable use case improves the employee experience by leveraging intelligent natural language conversation services to integrate data in operational HR data systems, such as Workday, and in knowledge management systems that explain policies, procedures and guidelines. Benefits include improved employee engagement, higher productivity and lower costs.

Other potential HCM use cases that are ripe for AI and ML include recruiting, selection and on-boarding of candidates. AI and ML models are already being used to help analyze and match job descriptions and candidate resumes, and to recommend potential jobs for existing employees.

3. Process rethinking/reimagination

There are also multiple examples of leveraging AI and ML for rethinking and reimagining processes.

In one example, an insurance company reduced claim settlement time and costs by incorporating AI and ML to improve First Notice of Loss (FNOL) initiation and data collection, as well as claim classification/assignment processes.

Companies can improve the speed and accuracy of FNOL by enabling claimant self-service using natural language voice/chat to initiate a claim. They can integrate external data sources such as weather and employ a fraud scoring model to facilitate claim classification and corresponding assignment to the correct processing alternative. Processing alternatives can include:

Straight-through processing without human involvement
Sending appropriate claims to the legal department for potential early intervention to reduce lawsuits
Rejecting claims because the deductible is higher than the claim
Assigning claims to a human analyst/adjustor

Predictive modeling to triage injured worker claims has been developed and applied to help determine “return to work” outcomes, along with typical treatment plans for various types of injuries (NOT replacing doctors) and estimated treatment costs. This technique optimizes case manager resource assignment and helps injured workers get back to work, improving service quality and lowering costs.

Applying an ML model to extract information from unstructured documents and forms helps to provide insights from previously unanalyzed sources. This capability works by accelerating processing of information buried in those large unstructured documents and drives new insights, lowers costs and improves processing efficiency.

In a fourth example, Anteelo is automating the underwriting process for home inspection to lower costs and improve scalability and overall quality. This use case applies visual recognition to highlight details in home inspection photos and use those details to consult the underwriting guidelines and then augment the underwriting decision.

As you can see from these use cases, the ability to improve the customer experience and gain operational efficiencies through AI and ML are real and very achievable. What other use cases can you think of for your industry? I’d love to hear your thoughts!

AI Applications in Documents

Posted on 13/02/202015/06/2021 by Tushar

“We are drowning in information, but starved for knowledge”

This is a famous quote by John Naisbitt which shows the key difference between information and knowledge. Advancement in data engineering techniques and cloud computing have made it easy to generate data from multiple sources but making sense of this data and getting insights is still a huge challenge. The data volumes have now increased exponentially and along with the traditional structured data, data can now reside in different formats like unstructured social media text, log files, audio/video files, streaming sensor data etc.

Applying manual methods to process this diverse data is not only time consuming and expensive but is also prone to errors. Hence the need of the hour is to use Artificial Intelligence (AI) based automated solutions that can deliver reliable insights and also give a competitive advantage to customers. Here are few examples of how customers across industries can benefit from AI driven solutions.

Microsoft Azure based AI solution

In 2017, more than 34,000 documents related to John F Kennedy’s assassination were released. The data volume was huge, and data existed in different formats like reference documents, scanned PDF files, hand written notes and images. It would take researchers months to read through this information and hence manually reviewing this data was not the most optimal solution. Microsoft Azure team applied AI based Cognitive Search solution to extract data from these diverse sources and gained insights. Technical architecture for this use case was built using Azure Cognitive Services components like Computer Vision, Face Detection, OCR, Handwriting Recognition, Search and core Azure components like Blob Storage, Azure ML, Azure Functions and Cosmos Database. This solution also annotated text using custom CIA Cryptonyms.

Hospitals usually deal with a lot of patient data which could reside in electronic medical records (EMR), handwritten prescriptions, diagnostic reports and scanned images. AI based Azure Cognitive Search could be an ideal solution to efficiently manage patient’s medical records and create personalized treatment plan. Many downstream use cases like Digital Consultations, Virtual Nurses and Precision Medication can be built once the patient data is optimally stored.

Google Cloud Platform (GCP) based AI solution

GCP introduced Document Understanding AI (beta) in Cloud Next 19. This is a serverless platform that can automate document processing workflows by processing data stored in different formats and building relationships between them. This solution uses GCP’s vision API, AutoML, machine learning based classification, OCR to process image data and custom knowledge graph to store and visualize the results. Customers can easily integrate this solution with downstream applications like chatbot, voice assistants and traditional BI to better understand their data.

Customers who deal with Contract Management data like Mortgages are usually faced with a lot of manual tasks to ensure that the contracts are complete and accurate. This could mean processing contracts in different formats/languages, reviewing the supporting documents, ensuring that the details are accurate and complies with regulatory standards across documents. By using Document Understanding AI and integrating it with a well-designed RPA framework, customers will be able to efficiently process Mortgage applications, Contracts, Invoices/Receipts, Claims, Underwriting and Credit Reports.

Use cases from other industries

Document Management AI solution can also be applied to diverse use cases from other industries like processing claims related to damages to shipped products by e-commerce companies, handling know your customer (KYC) process in the banking industry, invoice data processing by Finance teams, fraud detection during document processing etc.

As more and more companies embrace the digitization wave, they will be faced with different variations of data/document management challenges. Based on the current trend, number of use cases are only going to increase and an AI driven solution is probably the most efficient way to solve this problem as it can reduce manual work, save cost and deliver reliable insights. This will ensure that companies can spend more time on building their business and less time on manually processing documents and data preparation.

What Does Product Management Look Like in Data Science?

Posted on 11/02/202015/06/2021 by Tushar

The topic related to ‘Product Management’ has received several laurels in recent years. Several rounds of discussions have happened to create an analogy out of client’s stand point. As I heard more of these conversations, there was an uncomfortable ambiguity stemming from disbelief – is this another fad or is there meaning to it? Well, the initial rumblings were from the cool kids in the bay. But, why did grounded Midwest and shoot-from-the-hip south latch on? Must be something deeper, right?!

Product management has been around forever in the software, e-commerce world. But, today, mainstream IT and AI teams in fortune 500 companies are thinking of a product paradigm shift. Leading consulting firms are also developing products or beefing up their technology as an eventuality.

But, the question that begs attention here is – why products? What happened to software as a service, platform as a service, ML as a service? Do we need another paradigm shift? Or as the saying goes – Old wine in a new bottle?

IT teams are today being led by progressive Chief Digital Officers, Chief Data officers. Conventionally, CIOs have been leveraging their value by app dev teams, BI teams, infrastructure teams et al. While this may have become a table stake, it has been around for a while already. The question is – ‘How to deliver incremental value to business?’

So, what has changed?

Demand:

IT is today called upon to be a true business partner. And, given the rate at which business is facing change, the time to deliver value is compressed.

Glocal innovation:

For a fortune 500 firm operating globally, innovation is striking at its core from multiple directions. While the USA is still the biggest revenue (EBITDA generation engine), problem and solution innovation is happening in other markets faster than the USA. For starters, they have less legacy to deal with. The local markets are facing competition from nimbler players. VC money is flowing into firms in China, Israel, Korea, India which are encountering newer problems in e-commerce, voice commerce sectors. Other traditional revenue generating markets, individually facing slower growth, find it difficult to make a business case to invest in solutions led by such innovations.

Problem repeatability:

This is going to sound rhetorical. But, I must state it because it is relevant. Business problems in today’s enterprise are constantly changing. Few of them get recreated, and hence are not available in large volumes. Few others are becoming common across markets and thus moving into a constant state of being a tightly defined problem that can be applied globally. Repeatable.

A good indicator to this is AWS recent product launches – out of the box image, text, voice, reinforcement learning, forecasting. Common problems which are finding repeatable solutions.

The AI candy shop:

Today, nobody wants to use process automation tools that are not embedded in intelligence. Passé, inefficient. Wallstreet, investors and boards are lapping up the buzzwords – cognitive, AI, embedded ML.

Cloud enabling global scalability:

Cloud platforms such as Azure, AWS have ensured that once you have these AI capabilities developed, they can be deployed globally. The global-local adaptation is a key design criterion in this context.

Glocal solution adaptation…er,… maybe Glocal problem adaptation:

Each market has its secret sauce in terms of the market structure, drivers and customer nuances. Thus, before adapting a solution from one market to the other, it is essential to adapt the problem as well. For example, it is an interesting pursuit to adapt the problem structure from the modern trade Australia market to half way across the world in Brazil.

And, then adapt the solution.

So, who’s game is it anyway?

Given the above guard rails, it is quite evident that the business case should be developed by a country specific P&L or ROI measure. It must be a global mandate. IT is one of the few functions which is ideally poised to ride this wave. That, they own the data systems is coincidental. Or, well.. was that the whole plan! Go, Trojan..

Finally, after rambling about half the things in the world – we come to the initial topic of this article. Products. Why?

A product has a life – it evolves constantly. The focus is on continually making the best product for its end user, ever possible. It has a roadmap. In a world of multiple users, it needs a strong owner who plans and decides well. It has a clear value proposition in each update/release. It can be developed in a sprint like manner. It can be defined with a bounded scope and sold internally in enterprises, with greater ease. And, be defined, abstracted, customized for a global roll out.

Looks like a duck, walks like a duck, sounds like a… must be a duck. Yes, I guess it does look like a product.

But, how do we help organize people and teams to get the products rolled out?

While the below roles are common to a product-oriented firm, the thought process is different from conventional IT projects. Sharing of resources across projects being the biggest drawback. The smartest of each of the below folks will perhaps still fail, without an organizing framework. The roles to work in a closely integrated manner, dedicated to making a single product management successful.

Product Designer:

The role of a product designer is someone who can completely immerse himself in the shoes of the end user, without worrying about the AI or Tech related issues that may occur sometimes. Just solve the end user’s real problem and keep tracking the end user’s behaviour as the product usage evolves. In product management, there is a contradictory school of thought which mandates that the designer must appreciate “how” a product works. This, however, might dilute the designer’s objective of empathizing with the end user.

Product owner:

A functional expert of impact analysis who can connect the dots and identify the nuances of each problem. A great problem solver, with functional expertise, has the knack to see through the commonalities, and the uncommon aspects too. Prioritization between the must-haves, nice-to-haves and must-not-haves is a key skill required in the role.

Product BAs

Products are quite massive in terms of their scope today. Primarily, each product usually is broken down into sub products which are owned by individual product Bas.

The AI solution developer(s)

Usually, it is very difficult to get a product owner who really gets AI solution development. By and large, individual intelligence is anyways overrated. It is important to have a dedicated AI solutioning team which can translate the problem into a modular AI solution.

The AI deployment team

It is not enough to develop a modular AI solution. To be able to deploy it in globally scalable platforms requires seasoned IT big data engineering & testing capabilities. The plumbing and wiring required to take the AI concept to enterprise last mile reality is no mean task. It is a specialized function. Truly speaking, they give the product its real-life form.

Scrum & Program Managers

Last but not the least, you need the scrum team and program managers. Everyone benefits from their discipline and order amidst the chaos.

Things to consider while defining Machine Learning and Artificial Intelligence use case

Posted on 10/02/202014/06/2021 by Tushar

While many companies are interested in applying artificial intelligence (AI) and machine learning (ML) to help their businesses transform and innovate, few have an appetite for a lengthy project or a large initial investment. But when companies can see meaningful value in six to eight weeks, they are more likely to expand their usage of Artificial Intelligence(AI) and machine learning (ML) into new initiatives.

To demonstrate the value proposition for Artificial Intelligence(AI) and Machine Learning (ML), we recommend concentrating on unlocking insights from vast amounts of unstructured data to help augment human intelligence, create process efficiencies and lower operational costs. We have also found that the best results tend to come from a business-led/IT supported model.

Four key questions are central to scoping out an initial minimal viable product (MVP) for a valid use case.

Which persona are you trying to assist – external or internal? The answer is not a value judgement, but rather a focus that impacts some of the design considerations and decisions. As an example, in a contact center, the focus for the customer service rep is productivity – quickly giving the rep as much information as possible. This usually means fewer prompts, fewer stops, one- or two-word questions, and fewer and more dense answers. Contrast that with the external customer, where we tend to focus on a warm and engaging experience that incrementally guides the customer to the answers they need. This typically means more prompts, more questions, a wider variety of natural language to ask the questions, and more and shorter (more digestible) answers.
How are you are trying to help that persona? This aspect typically helps us hone in on part of an initial business process to understand how we are going transform — whether by adding more data for consideration, augmenting analysis, and/or automating the execution of steps.
Where does the data come from to support this initiative? This is often the most challenging aspect for several reasons: There may be more questions than current answers, the current answers may be inconsistent or conflicting, or the current answers exist, but may not easily understood or comprehensible. All of these situations tend to require focus on curating, modifying, and/or creating content in order to get the right answers.
What is the value? Value can be determined most successfully when we can be as specific as possible. Very often, benefits can be described in terms of metrics. From a best practice perspective, having specific metrics-based objectives makes it clearer for business and IT to determine the value. Examples include: a) higher customer satisfaction, measured by Net Promotor Scores or revenue increases, b) lower costs, shown by reduced handling times or increased processing volumes with the same staff and c) better employee engagement, indicated by reduced attrition and training time.

To further the dialogue as you consider progressing towards transforming your business by leveraging artificial intelligence, here are some additional questions to guide your thinking:

Are you focused on transformation and innovation?
For what initiatives are you considering AI/ML support?
What business outcomes are you driving?
What is your current level of experience with AI and ML?
Have you identified any use cases or do you need some assistance in defining them?

The key to evolving your approach from MVPs to multiple initiatives developed and deployed in production at scale, is to effectively balance faster time-to-value and major transformation outcomes. This balance demonstrates business results in smaller time increments and sets the foundation for flexible and continuous transformation and innovation.

How AI is likely to transform the Consumer Packaged Goods (CPG) Industry

Posted on 16/06/201917/06/2021 by Tushar

The future is Artificial Intelligence (AI), and various industries are investing heavily in creating self-evolving AI applications. The CPG industry (consumer packaged goods) has till now been somewhat dormant and is looking at opportunities to improve efficiency and reduce expenses. Amazon, Microsoft and Facebook have been among the front runners in this domain. Amazon, for instance, spends more than 10% of its yearly revenues on Tech research, whereas top CPG companies are still at 1-2%. This is changing and changing fast, more and more CPG players recognize the hidden power and are putting in robust efforts in the area.

Expansion of AI Application in the CPG Industry

Overall, the possibilities of AI application in the CPG industry is infinite. However, presently the state of AI application is still at a stage of infancy, lagging far behind other sectors such as retail and technology. Even though investment in AI from CPG companies has considerably increased, most companies are still working on identifying the critical applications with high business impact. In 2015, CPG firms, on average, spent 0.66% of revenue on AI application. This percentage is expected to keep increasing until a precise evaluation of their AI maturity is established. This article will discuss the areas in which CPG companies can expect to find successful applications of AI.

Consumer Feedback

Receiving feedback from customers at a massive scale usually involves leveraging natural language processing (NLP) programs for sentiment evaluation. Fundamentally, NLP focuses on teaching a machine to infer the gist of raw text. It is tremendously valuable but more complicated and resource-demanding than processing structured data. Structured data is greatly systematized and easily cognized by machine language. For instance, an AI program will be easily able to compute names, credit card numbers, geo-locations, stock data, etc. Analyzing customer sentiment, on the other hand, requires much more resources. The analysis is only the first step. A comprehensive AI-powered system must also be able to integrate ways to convey this analysis to the company’s customer feedback manager in plain and simple terms so that essential modifications can be made.

For instance, Hitachi devised a way to analyze customer feedback in a bid to reduce food wastage. They conducted a test in a hospital where trolleys mounted with cameras were used to collect trays from patients. The camera clicked images of the leftovers, and machine learning was used to detect the patterns of leftovers. In future servings, these wasted food items were not included in the patients’ meals.

Supply Chain

Another heavily researched field of AI application in the CPG industry is forecasting consumer demands. The data of wasted and sold food items in the past can help businesses efficiently forecast market demands. At the retail level, supermarkets will be able to stock precise amounts of food, considerably reducing wastage and avoiding stock shortages. CPG companies can easily supervise product locations and stock availability using AI tools that will eliminate the need for manual labor and boost effectiveness in logistics.

For example, when a major apparel company was faced with exceeding supply chain expenses, their products were not being able to reach potential customers, making lost sales a critical problem. Even a 1% recovery could provide a substantial increase in its yearly revenue. The company implemented AI to examine its products and discover how in-demand they were in the eyes of the customer. This application of AI was able to forecast precise classifications by store and by item. The AI program was able to predict which store would sell which item, ranking each item in terms of expected demand. Based on this analysis, the company was able to cut down on excess inventory and provide its customers with improved product-availability.

Marketing

A recent Nielsen survey revealed that over seventy per cent of CPG investment in marketing fail to breakeven. The major problem that CPG companies have when it comes to marketing is that they fail to integrate forecasting and planning in order to find the best promotional solutions. AI has the capability to introduce a data-driven method for CPG industry marketers. Powered by historical data, they can easily detect which marketing avenues are expected to generate maximum returns. An efficient program will be able to forecast and make recommendations on whether an in-store marketing tactic like – ‘buy two, get one free offer’ is the most effective for a specific product or brand, or if television advertisements will give the desired results.

AI programs can evaluate thousands of scenarios, incorporating even the littlest amounts of data before providing the ideal suggestion on which promotional channel will deliver the best results. Providing marketers with vital data like this is the only way for CPG companies to implement operationalized marketing projects on a large-scale yet cost-effective manner.

Pitfalls to avoid

CPG companies find themselves in a perfect place to make the most out of the AI boom. The technology is widely accepted as useful, and there are several verified methodologies shaped by other industries. By analyzing companies that are already reaping the benefits of AI application, the key takeaways include –

AI as a means, not an end – CPGs can apply AI to various aspects of their business operations and get augmented results. However, this is only possible when AI application is treated as a means to help workers, not eliminate them. For instance, when applying AI in marketing, any substantial discoveries or predictions should be provided to the experts in the marketing team so that they can then make even more informed decisions.
Streamlined Approach – CPG companies should avoid incorporating AI into every aspect of the business. Launching ten initiatives at once will more than likely result in those projects being stuck in the development phase for the next ten years. Companies must narrow down on one or two aspects of their business in order to have a better chance of delivering mass-scale results.

CPG company heads must stop viewing AI investment as “research projects” and welcome it as a way of carrying out day to day business tasks. Accepting the use of data-driven models in departments where employee intuition has always led operations can be a challenging and combative change. There is lot that CPG companies can achieve by using AI applications to support business but choosing the right initiative may be the key to its successful implementation.

The Rise of AI in Art: Ushering in a New Era of Creative Machines

Posted on 06/02/201919/06/2021 by Tushar

The Rise of AI in Art: Ushering a New Era of Machines with Creative Behaviors Just a few years ago, AI seemed like some futuristic tech straight out of a Sci-Fi movie. But the tables have turned now. We probably experience AI-based tech more often than we think.We come across at least one instance of AI in our daily life – be it a product recommendation algorithm on an online shopping platform or text auto-correction in our smart phones.Recent developments in AI, have begun to question the very characteristic of human nature which makes us unique, i.e., creativity. AI is already creating a myriad of visual art, poetry, music, and likewise, which bear an eerie resemblance to real human art.The Creative Leap of AI

Can we really imagine intelligent machines rivaling human creativity? Somehow, it’s still difficult to imagine a mathematical algorithm to be creative, isn’t it? But, not anymore.What we’re witnessing is that AI can be creative, even artistic. Recognizing and sorting images is one thing, but how about creating those from scratch?

Intelligent AI systems are now capable of creating artwork using certain algorithms. Much like in the creative world, where there is no set of rules to be followed, similarly to create AI art there are no particular rules. Thousands of images are analyzed and then the algorithm generates a new image. In the same way as we accessorize our paintings and those get better, AI too includes stylistic processes to generate images. More importantly, AI art does not replicate what humans do; rather it replicates the actual human thought process and enhances human creativity – a process called as “co-creativity”.

Interestingly, Creative adversarial networks (CANs) – a set of machine learning frameworks – maximize deviation from established styles. Human artists are directing the code with a desired visual outcome in mind and some really interesting artwork is being made. Quite artsy, isn’t it?

Collaboration is the key

Since AI has officially entered the world of art, it seems we’re getting overwhelmed and been thinking AI to be a threat to the art world. Here’s the catch – AI works best with human collaboration. Basically, AI uses algorithms that are fed to it. The more you feed, the better and easier it gets. Seemingly, AI works best when there’s an amalgamation between human creativity and modern machines. It’s not the technology alone that makes the difference but rather the knowledge and creativity of humans. It means AI is never going to replace humans in art as there is a requirement for real creativity, the real human emotions.

Impact of AI algorithms in Art: Overcoming the Limitations of Human Creativity

Creativity is attainable. But, human creativity has its limitations. This is where AI comes to your rescue and solves your problems. AI emulates and enhances our creative thought process in art and business as well. AI art or art created with neural networks has recently surged up with being hotshot “AI artists” on the rise. With an algorithm named AICAN, a solo exhibition was held in New York having each of its portraits sold for $6000 to $18,000.

Another example is Unsecured Futures, a solo exhibition to showcase artwork – drawing, painting, sculpture and video art – by Ai-Da, the first ultra-realistic humanoid robot artist. The brainchild of Gallery Director Aidan Meller, Ai-Da is capable of using her eyes and pencil for drawing people from life using AI-based algorithms. This exhibition actually questions the human relationship with technology but interestingly, it was a grand success that earned Ai-Da around 1 million pounds worth of artwork.

Applications of AI have found their way into the music industry too. AI-based algorithms are being used by musicians in their live performances, studio production in the form of various plug-ins and software. Moreover, some of the current AI technologies have successfully composed entire songs.

The best example would be Bach’s music, which integrates math into its music following a structured pattern, and can be easily replicated by AI. Facebook AI Research (FAIR), whose research team has created high-fidelity music with neural network is another such example.

The song “Drowned in the sun”, written by AI Magenta and launched by Google is also a work of art. Google’s latest Poem Portraits is another such example of how far the field of AI has come in the past few years. New AI tool – Deep Nostalgia – animates the faces in old photos to make them look alive.

Recently, GPT3 – the third generation of the language predicting deep learning algorithm took the world by storm by generating some of the most human-like conversations such as poems, stories, articles, etc.

Could AI be the Future of Art?

Humans have been raising the bar from drawing machines to generating arts using AI. Needless to say, AI has transformed our society and has changed the way we interact with technology. Though its impact upon us is greater, still there will always be negative consequences associated with it. But it would be hasty on our part to predict that AI will take over our life.

When GPT3 was announced to the world, it received a mixed response; one of utter amazement as well as deep concern. The age of the Industrial revolution witnessed machines replacing humans as a better alternative.

Now, this raises the question – will we be replaced by AI algorithms in the same manner? Are the algorithms the better alternatives? And if so, will it be for the greater good of mankind? These are some genuine reasons for concern.

Algorithms are a product of the human thought process. AI is not as artificial as we might deem it to be. AI algorithms merely implement our thought processes on a computer. Hence, all that an AI can create is a product of collaboration among humans. We feed in the data that other humans have generated.

Art by AI algorithms is a reflection of the global creativity of mankind. They are an ideal representation of what we, as individuals, have put into the world.

On this World Art Day, let’s rejoice in the artistic results of a synergistic collaboration between humans and intelligent machine systems.

1. Define an integration strategy for embedding AI and analytic insights into business operations

2. Establish a holistic data and analytics platform

3. Know your data

4. Control and govern your data

5. Simplify access to your data

Reacting to change

Taking advantage of AI

Cyber defence

Data storage

Conclusion

Question 1: “How do AI and Machine Learning differ from traditional programmed systems?”

Question 2: “Where is the value from Artificial Intelligence/Machine Learning?”

1. Customer care

2. Human capital management

3. Process rethinking/reimagination

Microsoft Azure based AI solution

Google Cloud Platform (GCP) based AI solution

Use cases from other industries

Demand:

Glocal innovation:

Problem repeatability:

Cloud enabling global scalability:

Product Designer:

Product owner:

Product BAs

The AI solution developer(s)

The AI deployment team

Scrum & Program Managers

Expansion of AI Application in the CPG Industry

Consumer Feedback

Supply Chain

Marketing

Pitfalls to avoid

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