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Thomas J. Watson Sr. joins Computing-Tabulating-Recording Company (CTR) in 1914 and over the next two decades transforms it into a growing leader in innovation and technology. He built a worldwide industry; it is called to International Business Machines Corporation (IBM) in 1924. According to Fortune 500, IBM is ranked as one of top 10 firms in 90’s. Let’s take a look at the roadmap of the IBM in the digital transformation, it consists not just software, hardware and services include cognitive solutions and cloud platform. IBM’s Bekas explains that we simply can’t scale enough hardware to solve this. “Ultimately, hardware can’t beat computational complexity. You need to have a combination of algorithmic improvement and hardware development,”

In this article, we will focus on artificial intelligence area and related industry focuses. Artificial intelligence is rapidly coming of age, poised to transform businesses and industries globally. The market for AI is on an exponential growth curve and is expected to reach $16.06 billion by 2023. With over half of all developer teams projected to embed AI services in their apps by 2023, it’s inevitable that consumers will soon be interacting with these new technologies on a regular basis.

IBM’s AI Strategy

Cognitive Solutions

With the highest level of intelligence that exists in technology systems, these solutions tackle challenges ranging from answering client inquiries to helping physicians fight cancer. Watson Health optimizes performance, engage consumers, deliver effective care and manage the health of your population.

What is IBM Watson?

The first cognitive system was Watson, which debuted in a televised Jeopardy! (a quiz competition) challenge where it bested the show’s two greatest champions. Watson answered many questions about synonyms, antonyms or slang and it achieved all of them without the internet connection. To find and understand the clues in the questions use machine learning, statistical analysis, and natural language processing.

New generations of that cognitive systems are trying to use diagnose oncology for healthcare professionals and in the customer services. Watson solutions are being built, used and deployed in more than 45 countries and across 20 different industries. IBM unceasingly pushes the boundaries of Watson increasing its use areas and developing new algorithms.

In earlier 2023 IBM announced the cooperation with Illumina Inc., their new designs’ aim is helping standardize and simplify genomic data interpretation. TruSight Tumor 170 is an assay designed to cover 170 genes associated with common solid tumors by Illumina. In a matter of minutes, Watson for Genomics will read the genetic alteration files produced by TruSight Tumor 170, comb professional guidelines, medical literature, clinical trials compendia, and other sources of knowledge to provide information for each genomic alteration, and produce a report for use by researchers — a process that typically takes scientists more than one week to complete. Watson for Genomics ingests data from approximately 10,000 scientific articles and 100 new clinical trials every month.

IBM’s technology is quite unique thanks to highly adaptable intelligence systems, protect and respect client data, trained in domain depth and transformational services.

What is IBM Watson used for?

IBM’s Watson services based on four main parts as language, speech, vision, and data insights.

In the language part, the conversation is maintained by chatbots that understand natural language and deploy them on messaging platforms and websites, on any device. Document conversation, language translator, tone analyzer, and natural language translator are used and information retrieval is enhanced with machine learning. Also, Natural Language Processing (NLP) has a long and distinguished history at IBM Research and is currently the focus of numerous projects worldwide. IBM interests cover a wide range of topics from Machine Translation, to Information Extraction, to Question Answering. Artificial intelligence tries to understand personality characteristics, needs, and values in written text.

Watson Speech to Text converts audio voice into written text. This system transcribes calls in a contact center to identify what is being discussed, when to escalate calls, and to understand content from multiple speakers. Speech to text creates voice-controlled applications — even customize the model to improve accuracy of the language and content you care about most such as product names, sensitive subjects, or names of individuals. Furthermore, IBM enables computers to speak like humans via converting written text to text into natural sounding audio. The common areas that used are; toys for children, automate call center interactions, and communicate directions hands-free.

Visual Recognition understands the contents of images — visual concepts tag the image, find human faces, approximate age, and gender, and find similar images in a collection. You can also train the service by creating your own custom concepts. It is usually used in the e-commerce sites to detect a dress type. According to February News , a new capability being added to Visual Recognition is color tagging. While Watson has already been able to detect color, it will now return the top colors it sees in each image as response tags, each accompanied by a classification score. The new capability allows users to quickly assess the dominant color schemes within an image and turn these into actionable insights. Not only analyze, fashion designers will predict color trends from ten years of fashion runway images.

With AI, convert, normalize and enrich your unstructured data. Discover from already exist pre-enriched datasets by using a simplified query language like Discovery News dataset is a public data set that has been enriched with cognitive insights, and is included within the Watson Discovery Service. It is updated continuously, with over 300,000 new articles and blogs added daily, sourced from more than 100,000 sources.

ABB IBM Partnership

If we consider ABB and IBM collaboration form, organizations using the solutions will benefit from ABB’s deep domain knowledge and extensive portfolio of digital solutions combined with IBM’s expertise in artificial intelligence and machine learning as well as different industry verticals. ABB and IBM will leverage Watson’s artificial intelligence to help find defects via real-time production images that are captured through an ABB system and then analyzed using IBM Watson IoT for Manufacturing. Previously these inspections were done manually, which was often a slow and error-prone process. By bringing the power of Watson’s real-time cognitive insights directly to the shop floor in combination with ABB’s industrial automation technology, companies will be better equipped to increase the volume flowing through their production lines while improving accuracy and consistency. As parts flow through the manufacturing process, the solution will alert the manufacturer to critical faults — not visible to the human eye — in the quality of assembly. This enables fast intervention from quality control experts. Easier identification of defects impacts all goods on the production line and helps improve a company’s competitiveness while helping avoid costly recalls and reputational damage. [1]

All these R&D and acquisitions are claimed to cost $16bn during 2024 but Watson would start bringing in money despite all cost. IBM’s chief financial officer Martin Schroeter said revenue would come through Watson serving IBM’s strategic imperatives and cognitive software. Watson is the “silver thread” running through Watson Health and Financial Services, IBM’s IoT and security, he said. “Watson is firmly established as the silver thread that runs through those cognitive solutions and you can see all of that in the solution software performance.”[2]

Strategic imperatives accounted for 40 percent of IBM’s revenue, $32.8bn for 2024, the firm said. Its stated goal is to make $40bn from them by 2023.

Industry Focus: As IBM brings higher levels of value to its clients, as its offerings are being built for the needs of individual industries. Healthcare and Financial Services are two examples of the company’s initial cognitive focus. In the healthcare industry, IBM Watson achieves remarkable outcomes, accelerate discovery, make essential connections and gain confidence on their path to solving the world’s biggest health challenges.

One year ago from today, IBM announced their plan to acquire Truven Health Analytics, a leading provider of cloud-based healthcare data, analytics and insights for $2.6 billion. Other industries are cyber security and financial guidelines IBM Security — which monitors 35 billion security events a day for 12,000 clients spanning 133 countries — launched the world’s first commercial “cyber range,” where clients can simulate and prepare for real-world attacks and draw on the power of Watson to fight cyber crime. The company told The Telegraph that IBM Watson “can help thwart the major hacks that have become a growing concern”, quoting attacks on Yahoo, Lloyds and TalkTalk. Watson’s security machine can additionally save up to 20,000 hours a year chasing false alarms.

Blockchain will enable financial institutions to settle securities in minutes instead of days; manufacturers to reduce product recalls by sharing production logs along their supply chain; and businesses of all types to more closely manage the flow of goods and payments. Blockchain brings together shared ledgers with smart contracts to allow the secure transfer of any asset — whether a physical asset like a shipping container, a financial asset like a bond or a digital asset like music — across any business network. IBM is working with companies ranging from retailers, banks, and shippers to apply this technology to transform their ecosystems through open standards and open platforms.

In April 2023 National University of Singapore (NUS) School of Computing and the IBM Innovation Center for Blockcha (ICB) are collaborating to develop a module on fintech. The aim is to enhance students’ knowledge and skills. Blockchain is a fast growing area across the globe, with banking, healthcare and the government leading the way in terms of adoption.

“Blockchain is one of the most disruptive technologies in computing today, and it is impacting many industries including financial services, trade, healthcare, and supply chain. This collaboration with the National University of Singapore School of Computing will help prepare a future workforce that is born on blockchain, ready to implement, improve and innovate: core skills required for Singapore to achieve its vision as a Smart Financial Centre and Smart Nation,” said Robert Morris, Vice President Global Labs, IBM Research.

IBM’s PowerAI system use combination of deep learning, machine learning, and AI and deploys a fully optimized and supported platform for your business.

What happened to IBM Watson?

At launch, IBM’s Watson was suggested to have boundless applications, from spotting new market opportunities to tackling cancer and climate change, however, these great expectations collapsed under the complications of building real world medical applications. 

Oncologists at University of North Carolina abandoned Watson after using it for a year at the institute on cancer genetic data to spot mutations. The decision to let go of Watson was due to its lack of flexibility in diagnosis and because, as physicians claimed, Watson did not produce better outcomes than traditional diagnosis methods. [4]

MD Anderson Cancer Center terminated the “the Oncology Expert Advisor” project which relied on Watson to analyze patients’ EHRs and suggest treatment recommendations. After adopting Watson, MD Anderson switched to a new EHR system and Watson wasn’t able to decipher unstructured physician’s notes or patients’ historical data, for instance Watson couldn’t reliably distinguish the acronym for Acute Lymphoblastic Leukemia “ALL” from physician’s shorthand for allergy “ALL” introducing very confusing and risky treatments. [5]

Strategic Mergers and Acquisitions

A strategy based on hiring experts of a relative area and gain power from cooperates.

‘Build a network of like-minded people, whether it is a digital community or an in-person one. Establishing your network and growing your connections is vital to becoming a new collar worker.’

– Randy Tolentino, Software Developer, Austin, TX.

April 2023: Early 2023 IBM announced that they will be acquiring myInvenio, an Italian startup that builds and operates process mining and digital twin of an organization software. IBM and myInvenio had worked together since November 2023 to integrate myInvenio technologies on IBM’s Cloud Pak for Business Automation run on OpenShift. The acquisition will provide myInvenio’s capabilities to IBM’s business partners to enable customers to generate data-driven insights about their business processes and optimize their digital transformation roadmap.

June 2023: IBM has acquired Turbonomic, an AI-powered cloud Application Resource Management (ARM) and Network Performance Management (NPM) software provider, to launch Watson AIOps which uses AI to automate IT operations. This acquisition complements IBM’s strategy to become a hybrid cloud and AI company as Turbonomic’s tools rely on AI to automate management, analyze performance, and suggest changes to meet network usage requirements.

July 2023: IBM has acquired Red Hat, a global open source enterprise software provider, for $34B, which is claimed to be IBM’s largest acquisition ever. Red Hat’s open hybrid cloud technologies would enable IBM to progress in the cloud infrastructure market where it used to lag behind tech giants such as Amazon and Microsoft. In August 2023, IBM announced the launch of their software portfolio to Red Hat OpenShift, Red Hat’s Kubernetes-based container platform which runs on Linux and integrated automation solutions such as robotic process automation (RPA), document processing, workflows and decisions. This step allows IBM users to run OpenShift on AWS, Azure, Google Cloud Platform or IBM’s own cloud, among others such as DB2, WebSphere, API Connect, Watson Studio and Cognos Analytics.

April 2023: The combination of digital solutions-artificial intelligence-machine learning. New solutions aim to bring real-time cognitive insights to the industry. AI does not just simply gather data, will help eliminate inefficient processes and redundant tasks to understand the actions. Using data will be more sense and reasoning for the cognitive computing of IBM.

The era of cognitive systems

The sectors have already used or planned on using of cognitive systems are:

Though IBM is one of the major providers of AI solutions to enterprise they are not the only one and they are not active in all areas of AI. You can check out AI applications in marketing, sales, customer service, IT, data or analytics. And If you have a business problem that you want to solve where AI can be helpful:

IBM’s history of AI research

IBM has been a leader in AI research since the field’s early days in the 1950s, when Arthur Samuel developed a checker player that learned from experience. In 1961 he put his program up against the Connecticut state checker champion, the number four ranked player in the nation. His checkers program won. This work was one of the earliest and most influential examples of machine learning. Forty years later, IBM Research’s chess-playing program Deep Blue made history when it beat Gary Kasparov, becoming the first chess-playing program to defeat a reigning world champion. We continue to take on new challenges, including Jeopardy! and Go. Summarily here the list of IBM’s contributions to AI:

Deep Blue — Computer Chess (1997):

IBM chess machine Deep Blue defeated World Chess Champion Garry Kasparov in a six-game match. Thanks to Its successful algorithms, Deep Blue’s victory has a fundamental part of the AI history and development.

Backgammon:

‘In the early 1990’s, IBM Researcher Gerry Tesauro demonstrated that reinforcement learning (RL), hitherto regarded as a mere theoretical curiosity, could achieve spectacular success in complex real-world problems. The ensuing intense interest led to RL becoming one of the most important areas of machine learning research, particularly for tasks requiring automated decision-making. Using “temporal difference” RL combined with a neural network, TD-Gammon played millions of games against itself, in the process developing a level of play on par with world champion human backgammon players. Considering that it started from a completely random initial strategy, used only the raw board state (with no hand-crafted features), and used only the binary win/loss signal at the end of the game to guide its learning, this result shocked the machine learning world.’[3]

RL in real-world domains including elevator control, production scheduling, network routing, financial trading, spoken dialog systems, power plant control, and video game AI.

Infomax Principle for Neural Network Learning

Ralph Linker’s discovery that a standard (Hebbian) learning rule, combined with locally correlated random activity, causes a model visual system network to automatically form “neurons” that respond selectively to light-dark edges having a preferred orientation, and to organize a layer of these neurons

The infomax principle addresses a general feature of biological information processing — the brain’s ability to learn automatically to recognize visual, auditory, and other features present in the environment.

References:

[*] Summarized from the IBM Annual Report 2024

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Ibm Watson At The Us Open: Showcasing Ai

This week IBM held an analyst session on their Watson AI platform, and it was being used to provide security and other features for the US Open.  It is uniquely capable of identifying and mitigating unique threats. Besides, it was used to assist fans debating the game and create other features that made this year’s US Open without fans present (due to the Pandemic) more interesting. 

But John McEnroe was also on the call, and he answered some fascinating questions regarding AI and Djokovic, the number one contender, being kicked out of the event. He argued that Watson might have come to a very different and fairer conclusion. 

One of the enormous problems with sports judging is that it can seem to be very arbitrary and often capricious because people are doing the judging, and even the same people aren’t consistent. And you get different judges so players, and fans, often think they were mistreated. 

What AI can do is bring a level of consistency to judging where emotions take a back seat, and judgments can at least appear to be fair more unbiased. 

Let’s chat about that this week as we explore how AI could make sports seem more fair.

How AI Could Fix Sports

What happened at the US Open was that Novak Djokovic, upset with how the game was going, hit a ball out of anger, and it bounced off a line judges neck sending her rapidly to the floor. He didn’t intend to hit the line judge, but the Judge was injured enough. She had to leave the game, and this caused the umpire to remove Novak from the tournament.

Many of the fans and Novak seemed to think this was unfair because the act was an accident. And, had the ball not hit the line Judge, it is believed there would have been no severe penalty. This decision had come after Novak had contracted COVID-19 after failing to follow social distancing and mask rules, and what appeared to be a failed attempt to start a rival players union. So he was likely on thin ice. Still, the appearance of unfair treatment took the focus off the game and put it on the judges and umpires. 

What IBM Watson is very good at doing is rapidly aggregating relevant information and then making recommendations based on historical facts for what should be done in a particular instance. It will also provide the details on how it reached the decision showcasing it wasn’t capricious or vindictive.  So both the player and the fans would have received not only the decision, but the validating facts that led up to that decision would show that it was consistent with prior decisions and based on facts, not emotions. In short, it would have appeared fairer. 

Watson, tied to line cameras, likely could also perform consistently concerning whether tennis balls were in or out of the court, allowing line judges to be remote from the field and less likely to be hit by tennis balls or the occasional flying racket. This practice would not only be safer, but it would allow the judges to more rapidly confirm the call that the ball was in or out of the court. 

With any sport, there is always a concern that betting will corrupt the judging, and Watson could not only provide a level of prevention but could also look at trends and determine if a Judge or Umpire was compromised. This capability would potentially reduce the likelihood of scandals, which can have a severe impact on fan loyalty and attendance. 

Finally, Watson could help players when something like this does happen.  For instance, after the ruling, Novak just left for the airport and didn’t talk to reporters or apologize in person who reflected poorly on him.  Watson could have provided options based on past events, like those McEnroe was involved in, to get valid options on what he should do to mitigate the damage to his brand. 

Wrapping Up: Turning The World Into A Fairer Place

IBM’s goal for its AI efforts is to enhance and not replace humans. Sports provide an exciting showcase for how AI across a broad spectrum of activities and in full view of fans who, themselves, might be interested in this kind of enhancement for their responsive executives and firms.  And similar capabilities could be used to enhance and supplement our already overworked and underfunded Judicial system.

In the end, finding a way not only to provide unbiased judgments but proof they were unbiased may be a critical part of finding a way to get through today’s divisive times.  The world has never been a fair place, but maybe, an AI like Watson could make it fairer than it has ever been before.

Ibm Watson Wins Jeopardy, Humans Rally Back

IBM super computer Watson came away victorious during Jeopardy Wednesday, but not before the game show’s former champions Ken Jennings and Brad Rutter rallied a formidable defense. In the end, however, the humans were no match for Watson, which won with a commanding lead of $77,147 after three days of Jeopardy play. Jennings took second place at $24,000 and Rutter was third with $21,600. “I for one welcome our new computer overlords,” Jennings jokingly wrote in his answer during Final Jeopardy on Wednesday’s broadcast. The three-night Jeopardy challenge was taped in January at IBM’s T. J. Watson Research Laboratory in Yorktown Heights, New York.

Watson Commands Then Stumbles

At the outset of Wednesday’s broadcast it appeared as though Watson was going to obliterate its human rivals once again. But the tide turned thanks to a category that asked you to match the name of an Actor/Director to the person’s movie titles. Rutter and Jennings appeared to be jumping on the buzzer before knowing the answer; confident they would be able to answer these questions on the spot. The strategy worked, and after the first 15 questions, the score (in terms of clues answered) was Watson 7, Jennings 4 and Rutter 3. Watson and Rutter also each answered one clue incorrectly.

Watson Gets PWNED

Watson stumbled on a variety of subjects from politics to vague knowledge about newspaper sections to USA Today’s price hike in 2008. Watson failed to answer, for example, that Slovenia is the only former Yugoslav republic in the European Union. The computer did have Slovenia as one of its three probable answers, but its certainty about the correct answer wasn’t high enough. Watson answered, “What is Serbia?” instead–a country that is not in the EU. Rutter also answered this clue incorrectly and Jennings didn’t hazard a guess.

Humans In Jeopardy

Despite Watson’s shortcomings in the first regular Jeopardy round, the super computer bounced back during Wednesday’s Double Jeopardy. Watson took a commanding lead with 18 correct answers to Jennings’ 7 and Rutter’s 4. Watson also incorrectly answered a Daily Double question during the round.

After that it was on to Final Jeopardy where all three contestants correctly answered that Bram Stoker’s Dracula was inspired by William Wilkinson’s ‘An account of the principalities of Wallachia and Moldavia.’

The Takeaway

Watson is a significant leap a machine’s ability to understand context in human language. As IBM has said on several occasions, the goal was not to create a self-aware super computer that can run amok such as HAL 9000 from 2001: A Space Odyssey or Skynet from The Terminator. But a question and answer machine like the ship computer in Star Trek: The Next Generation.

But we’re not quite there yet. To construct Watson, IBM used 200 million pages of content stored on 4 terabytes of disk space, as much as 16 terabytes of memory (reports have varied), about 2,800 processor cores and approximately 6 million logic rules to determine the best answers. Watson took up 10 server racks, each with 10 IBM Power 750 servers and two large refrigeration units all of which was housed in its own room on IBM’s Yorktown Heights campus. All that for a computer that can parse language via text files, and not through voice-based input as the Star Trek computer does.

Voice-activated or not, IBM believes the technology behind Watson can be applied to a variety of fields, most notably medicine. The company plans to announce on Thursday a joint project with Columbia University and the University of Maryland to create a cybernetic physician’s assistant using Watson’s technology, according to The New York Times

It’s a world of exciting possibilities for Watson’s technology, especially if within our lifetime we will one day be able to walk up to a computer and say, “Tea. Earl Grey, hot.”

For a look at Watson and the philosophical questions the existence of this super computer poses, check out IBM’s A Smarter Planet blog post about Jeopardy Day 3.

Connect with Ian Paul (

@ianpaul

) and

Today@PCWorld

on Twitter for the latest tech news and analysis.

Ibm Artificial Intelligence (Ai) Portfolio Review

IBM’s artificial intelligence (AI) portfolio carries a few of the market’s most widely used AI solutions.

With just over 340,000 employees, Armonk, New York-based IBM is able to provide technical services such as AI to companies in over 171 countries.

IBM reported $303.8 million in AI revenue for 2023, according to a 2023 report by IDC. 

See below to learn about IBM’s artificial intelligence offerings, which are a key part the company’s global portfolio:

Watson Explorer is a cognitive exploration solution. Its combination of search and content analytics aims to help users find and understand the results of their inquiries.

Understands the structures of documents

Mines texts for insights

Delivers smart passages for answers

Chatbot integration

Watson Machine Learning is a full-service cloud offering. It allows data scientists and developers to create applications with predictive capabilities integrated. 

Machine learning (ML) model training and development

model training and development

Advanced data

refinery

Visual tooling integration

Decision optimization capabilities

OpenPages is IBM’s governance, risk, and compliance (GRC) platform. It empowers companies to manage risks and regulatory compliance across their organization and spanning multiple regulatory requirements.

Single data repository

Dynamic and user-friendly dashboard

Embedded flexible workflow models

A task-focused user interface

Watson Knowledge Catalog is a centralized cloud-based data cataloging tool. It’s an intelligent self-serving repository that provides data for AI, machine learning, and deep learning applications.

End-to-end data catalog

Automated governance

Flexible deployment in cloud

and

as a managed service

Ability to share self-service insights with peers and analytics tools

Watson Natural Language Understanding is a natural language processing (NLP) service and text analysis tool. Deep learning algorithms are used to extract meaning and metadata from unstructured blocks of text to provide insights.

Customizable to understand the precise language of a company 

Real-time text analysis and actionable insights

Flexibility to deploy in cloud or behind a local firewall

Creates employee-centered strategies

Mitigates risk of harm exposure

Creates a flexible workplace that prioritizes employee safety

Monitors and enhances customer service

Conducts regular social listening on online conversations

Built-in chatbot integration

Watson Captioning Live uses a combination of ML, AI, and cognitive speech-to-text applications to deliver automated real-time captions for audio files.

Saves time compared to manual captioning

Trained to understand various accents and local idioms

Creates more accessible media

Watson Assistant is a white-label cloud service that enables software developers to embed AI and virtual assistant (VA) functionality into their company’s software and offer it to clients.

Easy to deploy with a drag-and-drop interface

Integrates with multiple channels and existing content

Enterprise-ready and scalable

Watson Studio is IBM’s software platform for developers, data scientists, and analysts to develop, run, and manage AI models.

AutoAI for building model pipelines

Ability to manipulate, cleanse, and structure data on-site

Model risk evaluation and management

Built-in metrics monitoring and insights

Integrated data visualization tools

See more: Artificial Intelligence: Current and Future Trends

IBM offers the companies the chance to become a Watson AI partner to get hands-on support from their AI experts.

Partners are also able to join IBM’s live AI workshops and webinars at select locations.

IBM’s AI solutions have been used by thousands of clients in a variety of industries from all around the globe.

GM Financial, the financial arm of General Motors, serves millions of customers in the automotive industry. 

One issue they faced was the lack of engagement with their previous customer support chatbot.

“So, our first chatbot was just an FAQ bot. It didn’t leverage artificial intelligence by any means. And it was very unsatisfactory,” says Bob Beatty, EVP of customer experience at GM Financial.

They utilized Watson Assistant to answer customer questions and improve employee workday.

“We’re looking at Watson as another agent,” Beatty says. “And we’re telling our team members you have a new team member, and it’s IBM Watson Assistant.”

Overall, IBM AI Watson solutions have positive reviews regarding efficiency and quality.

A few downsides that some corporate users describe are the lack of compatibility with some systems and apps, slow loading time, and a learning curve.

TrustRadius: 8.8 out of 10

Gartner Peer Insights: 4.4 out of 5

Capterra: 4.3 out of 5

IBM has a track record of releasing acclaimed AI products.

Watson won the prestigious Gottlieb Duttweiler prize in 2023 for its outstanding contribution to the well-being of the economic environment.

Additionally, IBM Watson OpenPages recently won the Red Dot design award for the best in design.

IBM holds the largest share of the AI software market (8.8% in 2023), according to a 2023 report by IDC.

Microsoft holds the second largest share of the AI software market in the report (5.6%), and SAS ranks third (4.4%).

The AI software market was worth an estimated $3.5 billion in 2023, IDC says.

It is the fifth consecutive year IBM ranks as the market share leader in the report.

See more: Top Performing Artificial Intelligence Companies

Transforming Business Models With Integrated Customer 360 Views

In an operating environment marked by transparent markets and rising global competition, the customer has undoubtedly become king. A 2023 Salesforce survey revealed that the two biggest challenges faced by organizations today are meeting customer expectations and reacting appropriately to shifts in market demand. For the most part, businesses have responded to these issues by implementing varying levels of digital transformation across their front and backend processes. These strategies encompass everything from setting up ERP and WMS systems within supply chain functions to leveraging social media platforms and mobile applications to their make marketing and sales more accessible for users.  

Technology Alone Isn’t a Competitive Differentiator

While technological investments may help establish your brand amongst a growing stable of forward-thinking organizations, they can only produce lasting ROI when accompanied by a comprehensive reporting and analytics framework. Take a look at industry leaders like Netflix, Apple, Amazon, and Tesla. You’ll see that they all share an obsessive dedication to understanding their audience and creating personalized offerings that seamlessly meet the demands of each individual. This outward-in strategy is built around a robust reporting and analytics framework that draws data from varied sources both within and outside the organization. From real-time user behaviors tracked through website analytics to lead lists and sales histories gathered from CRM systems to sentiment analyses drawn from customer service channels and review aggregators, each touchpoint creates a wealth of data that can be used to create end-to-end customer experiences. Unfortunately, insights are all-too-often hidden behind siloed systems that are only accessible to specific functions. If they want to create cohesive business strategies that translate across multiple channels, businesses need to consolidate their disparate datasets and move them into a centralized repository that can provide a complete view of prospective and current customers across all targeted market segments.  

Creating a 360 Degree Customer View

Traditionally, these objectives have been served through a system of records (SOR).  This architecture served as a hub for all data relating to a specific business function. Once consolidated, inputs were combined to provide a more insightful look into the underlying process. Unfortunately, the SOR of old was often limited to a single platform, i.e., Salesforce for sales data, HubSpot for marketing information, 3PL for logistics, and supply chain. While accurate, the picture provided by these systems often lacked a greater business context. As a result, a far more holistic approach was required to achieve the ever-elusive 360 view. That’s where the concept of a golden record becomes so essential. A golden record consists of cleansed, validated, and merged data collected from disparate platforms both within and outside the enterprise. Data extraction from each of these sources is automated to ensure minimum lag time between the receipt of information into each operational store and its delivery into the golden record.

Matching data from different sources removes duplicated and contradictory data so that a definitive customer profile is available for decision-makers across the enterprise.

A 360-degree architecture is time-sensitive and touchpoint-sensitive; it provides an account of when and how customers interact with your brand, which channels they prefer to use, and where their last-known engagement occurred. With this information at hand, organizations can build a complete map of their buyer’s journey.

As new data filters in from each source platform, it is updated dynamically in the golden record, so the customer 360 is always as current as possible from every possible perspective.

The 360-view encompasses external data such as market trends, 3rd party reports, and public statistics to supplement internal sources. These inputs provide context for customer decisions and help organizations make macro-level decisions with far greater certainty.

This system is designed for enterprise-wide accessibility. The goal is to allow workforces must to derive insights from data based on their functional concerns.

   

Automating Data Integration in Your Customer 360

Ultimately, the biggest obstacle to creating the 360 view isn’t data extraction from disparate sources, the subsequent validation and cleansing of data, or even consolidation in an accessible repository – it’s the time, resources, and expertise required to design and deploy these processes from start to finish. Here are just some of the challenges that can arise during a manual implementation:

Each siloed system will house data in a unique format and layout. Connecting to these sources and linking extracted datasets with those from other functions requires a considerable amount of technical skill.

A data steward will need to be appointed to oversee the transfer of data in line with applicable governance policies.

Processes will need to be put into place to ensure updates and changes to source systems are transferred to the 360 view as quickly as possible. Otherwise, records will become outdated. Again this will require significant intervention from your IT team.

Data may be duplicated in different organizational silos, and there could be inconsistencies between these records, or they may be formatted differently. In these cases, the master record will need to be identified and missing or erroneous entries removed.

The timeliness of data may differ from system to system, which will hamper the accuracy of the 360 view. So, the duration of updates for each silo will need to be verified, and these details will need to be accounted for when designing manual integration flows.

How Ai And Robots Are Transforming Solar Energy

Without getting excessively technical, basically, the entire reason of AI is a machine emulating the human brain. The machine can learn and adjust to various situations, and as time passes, the machine gets smarter and responds diversely to accomplish better outcomes. A one of a kind opportunity exists to apply AI to a particular part of the clean energy value chain: materials. Materials fill in as the structure blocks of clean energy, for example, the solar cells that make up the photovoltaic panels found on rooftops. Enhancing the materials used to manufacture parts of clean energy is significant on the grounds that current materials are frequently lethal, non-earth rich, and require carbon-concentrated processing. Utilizing AI along these lines can give producers an edge. Manufacturers will in general put resources into upgrading downstream production capacities, which has prompted a few AI applications in sensor innovations and process optimisation. Utilizing AI for upstream design purposes, nonetheless, is an undiscovered business opportunity that could decrease the time it takes to find new materials, opening up capital for deployment and commercialisation strategies. In July 2023, Curtis Berlinguette, a materials scientist at the University of British Columbia in Vancouver, Canada, acknowledged he was burning through his graduate student’s time and ability. He had asked her to refine a key material in solar cells to boost its electrical conductivity. In any case, the number of potential changes was overpowering, from spiking the formula with hints of metals and different added substances to shifting the heating and drying times. According to Berlinguette, there are such a significant number of things you can go transform, you can rapidly experience 10 million [designs] you can test. So, he and associates re-appropriated the effort to a single-armed robot overseen by an artificial intelligence (AI) algorithm. Named Ada, the robot blended various solutions, cast them in films, performed heat medicines and other processing steps, tried the film’s conductivity, assessed their microstructure, and logged the outcomes. The AI deciphered each examination and figured out what to blend next. At a meeting of the Materials Research Society (MRS) a week ago, Berlinguette revealed that the system immediately homed in on a formula and heating conditions that made defect-free films perfect for solar cells. What used to take them 9 months presently takes 5 days. Ada could change how clean energy is made at a small amount of time and cost. Via autonomously testing materials at high computing forces, Ada plans to make solar panels stronger and to transform carbon dioxide into valuable fuels. Robots have already made a difference. They are currently generally used to blend many somewhat various recipes for a material, store them on single wafers or different platforms, and afterward process and test them all the while. In any case, basically trudging through recipe after the recipe is a moderate course to a breakthrough. High throughput is an approach to do heaps of experiments, however, not a great deal of development. To speed the procedure, numerous teams have included computer modeling to foresee the equation of likely pearls. “We’re seeing a torrential slide of exciting materials originating from the forecast,” says Kristin Persson of Lawrence Berkeley National Laboratory (LBNL) in California, who runs a large-scale prediction enterprise known as the Materials Project. However, those frameworks still commonly depend on graduate students or experienced researchers to assess the consequences of trials and decide how to continue. However, Individuals still need to do things like rest and eat.

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