The “hidden figures” of AI: Women shaping a new era of ethical innovation

The end of March marks the conclusion of Women’s History Month. And despite the increased focus on women’s issues and contributions to society throughout the month, the conversation would be incomplete without recognizing how indispensable the success of women—past and present—has been in the tech industry. In particular, women are leading the way every day toward a new era of unprecedented global innovation in the field of generative AI.

However, a New York Times piece that came out a few months ago failed on its list of people with the biggest contribution in the current AI landscape. The piece rightly received criticism for reflecting a broader narrative that has long minimized the contributions of women in technology. That narrative says the contributions of women in AI and technology are peripheral; but we know this isn’t true. In fact, they are central to the innovation and continued development of this field.

Women have been challenging the outdated notion that AI development solely belongs to those who code and construct algorithms—a field that, while shifting, remains significantly male-dominated—for years. Many have been doing this by leading the charge on responsible AI innovation, centered on ethics and transparency, throughout their entire careers.

Women like Kay Firth Butterfield, the world’s first Chief AI Ethics Officer; Elham Tabassi from NIST, spearheading initiatives on ethical AI standards; Miriam Vogel from EqualAI and NAIAC, championing AI equality; Paula Goldman from Salesforce; and Navrina Singh from Credo, advocating for responsible AI use are just a few of the many examples of women leading the way in this space.

Other prominent women figures in tech include Fei-Fei Li from Stanford’s Human-Centered AI Institute, renowned for her contributions to AI image recognition and her advocacy for inclusive and ethical AI development; Joy Buolamwini, the founder of Algorithmic Justice League, highlighting and mitigating biases within AI systems; Lila Ibrahim from DeepMind, responsible for operational strategy behind one of the world’s leading AI research organizations; and Francesca Rossi, leading Global AI Ethics at IBM®, who stands at the forefront of addressing critical AI governance, ethics, responsibility and responsible innovation matters.

These are just a few of the many, many examples of women leading in this field. Leaving women out of the conversation and coverage not only overlooks the diverse perspectives necessary for responsible innovation, but also fails to recognize the vital role of ethics, governance, and consideration of societal implications in the development of AI. It is time for a critical reevaluation, one that acknowledges innovation is as much about its impact as it is about invention.

In a study conducted by the IBM Institute for Business Value, Debra D’Agostino, Managing Director of Thought Leadership at Oxford Economics, reinforces the importance of diverse leadership in AI’s evolution. She highlights how women don’t need to be IT experts to lead AI innovation. The study revealed that women are already more likely than men to have used AI to generate, edit and summarize content; and 40 percent say using generative AI has resulted in a greater than 10 percent increase in productivity. Understanding and anticipating how AI can best augment the unique needs and capabilities of a business or team is as crucial as working with the right people in IT to make it happen, D’ Agostino said.

As Women’s History Month comes to an end, it’s important to acknowledge how the contributions of women in AI are not just paving the way for more equitable technology, but are also crucial in realizing the possibility, and confronting and mitigating the immediate and long-term risks that AI poses to our society. Their work is setting the standards for how we, as a global community, approach the integration of AI into our lives.

The future of AI is being written today and women are not just supporting roles in that narrative—they are leading characters in the story. As we forge ahead, it’s important to remember that the true measure of AI’s advancement goes beyond its technical capabilities. It’s about how we harness this technology to reflect our collective values, address our shared challenges and create a world where innovation benefits all of society, not just the privileged few.

Source: ibm.com

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6 ways the recruitment process is boosted by AI

Nobody likes paperwork. And as important as talent acquisition is for any organization, it involves a lot of it: sifting through resumes, posting job descriptions, onboarding new employees. These tasks aren’t all tedium, and in fact, they often require human-level discernment. However, many components of these tasks can now be automated or augmented by AI, allowing hiring managers to focus on providing smarter, higher-level engagement with candidates. The organization that learns to leverage the latest in AI tools is able to free up employee time, so they can put a little more “humanity” into their human resources operations.

The typical goal of the talent selection process is simple: target the highest qualified candidates and persuade them to apply to vacancies and sign contracts at the most favorable rates to the organization. But there are many ways where this seemingly simple process can break down. A poorly written job description, for example, can result in a deficit of applications—or an abundance of applications from candidates who might not have the right skills, resulting in wasted effort and lost time in either case. Optimizing the process with AI tools can help recruiting teams zero in on right candidates, an essential capability in increasingly competitive employment markets.

Below are some ways that AI is enhancing the recruitment process across its workflow, from discovering hiring needs to attracting, courting, onboarding and retaining top talent.

Predictive analytics

Before a new job listing is even written or an open position has been identified, AI algorithms can help analyze various data sources like historical hiring trends, employee turnover rates, business growth projections and workforce demographics. By processing this data, AI identifies patterns and correlations, providing insights into future hiring needs based on past trends and organizational goals. AI can help predict demand trends for specific competencies, and help hiring teams develop recruitment strategies to plan for skills gaps that might not have even presented themselves as problematic yet. AI can also analyze external data, scraping job postings and public salary information, then model various scenarios and generate reports that might help an employer make hiring decisions about, for example, whether to fill a position with an internal recruitment, fill a gap with a contractor relationship or spring for a new hire. Such tools can also help organizations develop recruitment plans for achieving diversity, equity and inclusion (DEI) goals, identifying areas where hiring policies and trends might be adjusted to align with the organization’s broader DEI strategy.

Job posting

Once a comprehensive hiring strategy is developed, AI can get to work contributing to the creation of job descriptions. Generative AI tools can quickly develop descriptions based on short prompts. Then, once these have been posted on job boards, AI can conduct A/B testing on different versions of job descriptions to evaluate their effectiveness in attracting candidates. By analyzing metrics such as click-through rates, job application conversion rates and time-to-fill, AI helps organizations identify the most successful iterations and refine their approach accordingly. Employment-based social media companies like LinkedIn use AI to help organizations A/B test ads on their platform.

AI contributes to the creation of more inclusive and broadly enticing job descriptions. Language biases and unintentional exclusions can deter diverse candidates from applying. AI algorithms, armed with insights derived from a vast array of data, can craft job descriptions that are not only gender-neutral and culturally sensitive but also optimized to attract a wider pool of candidates. By fostering inclusivity, organizations can tap into a more diverse talent pool, bringing in fresh perspectives and skills that contribute to a vibrant and innovative company culture.

Resume screening

Reviewing resumes is probably the first thing that many HR professionals imagine when they think of the rote work they wish they could automate. And fortunately, AI-based screening technologies are getting smarter all the time, so there’s less chance of accidentally screening out a great potential hire.

With traditional methods, recruiters grappled with a deluge of resumes and cover letters, sometimes thousands for a single role. How could HR professionals expect to pick the needle out of the haystack in a timely fashion? AI, on the other hand, can swiftly analyze vast volumes of resumes, extracting relevant information and highlighting the best candidates whose qualifications most align with the job specifications. This ensures a more objective and consistent screening process, reducing the risk of overlooking qualified candidates. AI tools can deliver a shortlist to hiring managers, enabling them to spend less time sifting through huge piles of resumes, and more time both enhancing the candidate experience and delivering value to their organization.

Initial interviews

AI recruitment software can also come in handy during this phase to schedule interviews by coordinating available time slots between the candidate and the recruiter. This reduces the administrative burden on recruiters and streamlines the interview process.

Some job openings require many rounds of interviews. Conducting interviews, especially when high-level managers are involved, can become quite expensive. The point of initial interview questions is to give the candidate and the organization basic information about one another. This “first impression” does not necessarily need to involve a human agent on the organization’s side. Chatbots can engage candidates in a conversation to gather basic information about their preferences, availability and eligibility for a role. This can serve as an additional filter on top of the resume screening phase. Meanwhile, chatbots can answer frequently asked questions (FAQs) and distribute documentation about the organization to potential candidates.

This exchange of information can make subsequent interviews more useful to both parties, and help save both parties time if the candidate lacks necessary skills that the resume screening, for whatever reason, didn’t catch. On the flip side, a chatbot-led interview might also indicate to the interviewee that the position is not what they thought, obviating the need for subsequent interviews.

Chatbots can also administer quizzes or skills assessments to evaluate a candidate’s knowledge, skills or problem-solving capabilities. Virtual assistants can use the latest Natural language processing (NLP) capabilities to field open-ended answers in plain language, and help determine if those answers predict whether or not an employee is likely to be a good “culture fit.” If a candidate fails to meet certain performance criteria during this phase, the organization can move on with more suitable candidates without engaging HR staff. AI can also help job seekers more seamlessly provide information for background checks.

Contract negotiation

After selecting candidates and building a job offer, the organization can rely on AI for the negotiation process. AI is increasingly good at parsing information in offer letters and contracts to ensure compliance with relevant laws, regulations and industry standards. By flagging potential legal issues or discrepancies, AI helps ensure that contracts adhere to legal requirements, reducing the risk of disputes or litigation. By evaluating factors such as termination clauses, non-compete agreements and intellectual property rights, AI helps negotiators assess the potential impact of contract terms and negotiate accordingly.

AI can analyze clauses within employment contracts and compare them to industry benchmarks or standard templates. By identifying deviations or unusual provisions, AI helps negotiators understand the implications of each clause and negotiate more effectively.

AI can provide recommendations to the organization for negotiation strategies based on historical data, industry norms and the specific context of the negotiation. By analyzing past negotiation outcomes and success factors, AI helps negotiators develop informed strategies to achieve their objectives.

AI can automate the redlining and drafting of contract amendments or revisions based on negotiators’ input. New job title? No problem. NLP technology can make quick updates that don’t need to involve manual edits. By generating proposed changes and alternatives, AI streamlines the negotiation process and accelerates the exchange of contract drafts between parties.

Onboarding and retention

The onboarding process is a fantastic arena for AI to prove itself useful, from providing new hires with relevant information, answering their queries to guiding them through the initial steps, ensuring a smoother transition for new employees. AI-powered chatbots or virtual assistants can provide immediate support to new hires by answering frequently asked questions about company policies, benefits, IT setup and other onboarding-related queries. This reduces the burden on HR staff and empowers new employees to find information quickly and independently.

AI systems can automate the creation and processing of onboarding documentation. By streamlining administrative tasks, AI frees up HR personnel to focus on high-touch aspects of the onboarding process, while ensuring compliance with regulatory requirements.

As an extension of the overall employee experience, AI can also help to ensure that employees stay satisfied throughout their tenure with the organization. AI can recommend relevant training and development opportunities for employees based on their performance, skills and career goals, contributing to ongoing professional development. By offering tailored training programs and career paths aligned with individual goals, AI helps employees feel valued and invested in their professional growth, increasing their likelihood of staying with the company.

Algorithms can analyze employee workloads, productivity levels and stress indicators to identify individuals at risk of burnout. By recommending workload adjustments, time management strategies or wellness initiatives, AI helps prevent burnout and promote work-life balance, leading to higher retention rates. AI algorithms can analyze employee profiles, skills and interests to match new team members with peers and mentors. By connecting new employees with experienced colleagues who can provide guidance and support, AI accelerates the integration process and promotes knowledge sharing within the organization.

Bringing automation to your recruitment process

Looking for ways to develop a more effective recruitment process? Your search would be missing something if it didn’t include AI. IBM watsonx Orchestrate automates repetitive HR tasks with a conversational interface to manage and simplify multiple application workflows in HR. It includes robust recruiting automation capabilities. Built to automate repetitive tasks in your recruitment process, watsonx Orchestrate integrates with the top tools you already use every day to save you time and effort across your recruitment workflow.

Source: ibm.com

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IBM Databand: Self-learning for anomaly detection

Almost a year ago, IBM encountered a data validation issue during one of our time-sensitive mergers and acquisitions data flows. We faced several challenges as we worked to resolve the issue, including troubleshooting, identifying the problem, fixing the data flow, making changes to downstream data pipelines and performing an ad hoc run of an automated workflow.

Enhancing data resolution and monitoring efficiency with Databand

After the immediate issue was resolved, a retrospective analysis revealed that proper data validation and intelligent monitoring might have alleviated the pain and accelerated the time to resolution. Instead of developing a custom solution solely for the immediate concern, IBM sought a widely applicable data validation solution capable of handling not only this scenario but also potential overlooked issues.  

That is when I discovered one of our recently acquired products, IBM® Databand® for data observability. Unlike traditional monitoring tools with rule-based monitoring or hundreds of custom-developed monitoring scripts, Databand offers self-learning monitoring. It observes past data behavior and identifies deviations that exceed certain thresholds. This machine learning capability enables users to monitor data with minimal rule configuration and anomaly detection, even if they have limited knowledge about the data or its behavioral patterns.

Optimizing data flow observability with Databand’s self-learning monitoring

Databand considers the data flow’s historical behavior and flags suspicious activities while alerting the user. IBM integrated Databand into our data flow, which comprised over 100 pipelines. It provided easily observable status updates for all runs and pipelines and, more importantly, highlighted failures. This allowed us to concentrate on and accelerate the remediation of data flow incidents.

Databand for data observability uses self-learning to monitor the following:  

• Schema changes: When a schema change is detected, Databand flags it on a dashboard and sends an alert. Anyone working with data has likely encountered scenarios where a data source undergoes schema changes, such as adding or removing columns. These changes impact workflows, which in turn affect downstream data pipeline processing, leading to a ripple effect. Databand can analyze schema history and promptly alert us to any anomalies, preventing potential disruptions.
• Service level agreement (SLA) impact: Databand shows data lineage and identifies downstream data pipelines affected by a data pipeline failure. If there is an SLA defined for data delivery, alerts help recognize and maintain SLA compliance.
• Performance and runtime anomalies: Databand monitors the duration of data pipeline runs and learns to detect anomalies, flagging them when necessary. Users do not need to be aware of the pipeline’s duration; Databand learns from its historical data.
• Status: Databand monitors the status of runs, including whether they are failed, canceled or successful.
• Data validation: Databand observes data value ranges over time and sends an alert upon detecting anomalies. This includes typical statistics such as mean, standard deviation, minimum, maximum and quartiles.

Transformative Databand alerts for enhanced data pipelines

Users can set alerts by using the Databand user interface, which is uncomplicated and features an intuitive dashboard that monitors and supports workflows. It provides in-depth visibility through directed acyclic graphs, which is useful when dealing with many data pipelines. This all-in-one system empowers support teams to focus on areas that require attention, enabling them to accelerate deliverables.

IBM Enterprise Data’s mergers and acquisitions have enabled us to enhance our data pipelines with Databand, and we haven’t looked back. We are excited to offer you this transformative software that helps identify data incidents earlier, resolve them faster and deliver more reliable data to businesses.

Source: ibm.com

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IBM’s new Watson Large Speech Model brings generative AI to the phone

Most everyone has heard of large language models, or LLMs, since generative AI has entered our daily lexicon through its amazing text and image generating capabilities, and its promise as a revolution in how enterprises handle core business functions. Now, more than ever, the thought of talking to AI through a chat interface or have it perform specific tasks for you, is a tangible reality. Enormous strides are taking place to adopt this technology to positively impact daily experiences as individuals and consumers.

But what about in the world of voice? So much attention has been given to LLMs as a catalyst for enhanced generative AI chat capabilities that not many are talking about how it can be applied to voice-based conversational experiences. The modern contact center is currently dominated by rigid conversational experiences (yes, Interactive Voice Response or IVR is still the norm). Enter the world of Large Speech Models, or LSMs. Yes, LLMs have a more vocal cousin with benefits and possibilities you can expect from generative AI, but this time customers can interact with the assistant over the phone. 

Over the past few months, IBM watsonx development teams and IBM Research have been hard at work developing a new, state-of-the-art Large Speech Model (LSM). Based on transformer technology, LSMs take vast amounts of training data and model parameters to deliver accuracy in speech recognition. Purpose-built for customer care use cases like self-service phone assistants and real-time call transcription, our LSM delivers highly advanced transcriptions out-of-the-box to create a seamless customer experience.

We are very excited to announce the deployment of new LSMs in English and Japanese, now available exclusively in closed beta to Watson Speech to Text and watsonx Assistant phone customers.

We can go on and on about how great these models are, but what it really comes down to is performance. Based on internal benchmarking, the new LSM is our most accurate speech model yet, outperforming OpenAI’s Whisper model on short-form English use cases. We compared the out-of-the-box performance of our English LSM with OpenAI’s Whisper model across five real customer use cases on the phone, and found the Word Error Rate (WER) of the IBM LSM to be 42% lower than that of the Whisper model (see footnote (1) for evaluation methodology).

IBM’s LSM is also 5x smaller than the Whisper model (5x fewer parameters), meaning it processes audio 10x faster when run on the same hardware. With streaming, the LSM will finish processing when the audio finishes; Whisper, on the other hand, processes audio in block mode (for example, 30-second intervals). Let’s look at an example — when processing an audio file that is shorter than 30 seconds, say 12 seconds, Whisper pads with silence but still takes the full 30 seconds to process; the IBM LSM will process after the 12 seconds of audio is complete.

These tests indicate that our LSM is highly accurate in the short-form. But there’s more. The LSM also showed comparable performance to Whisper´s accuracy on long-form use cases (like call analytics and call summarization) as shown in the chart below.

How can you get started with these models?

Apply for our closed beta user program and our Product Management team will reach out to you to schedule a call.As the IBM LSM is in closed beta, some features and functionalities are still in development.

Source: ibm.com

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Trends driving managed file transfer and B2B data exchange modernization

The most important business conversations your organization has are the daily digital exchanges of data with your customers and suppliers. Significant negative trends have emerged in the past few years, raising pressure on the critical software that most companies use to exchange business data within and outside their company, especially with their customers and suppliers. Your organization is likely feeling some challenges created by these trends.

Among the current key trends impacting file transfer and B2B systems, undoubtedly the main disruption comes from the overall increase in transaction volume. With the accelerated digitization of many business processes, organizations have seen unprecedented growth in their file transfer and B2B transactions in recent years.

Other trends include:

◉ A growing number of customers and suppliers. As more organizations look to increase the agility of their supply chain, they are adding suppliers.

◉ Expectations for faster response times. Many organizations are receiving response time objectives from their customers. Not replying to an inventory request or shipping date query fast enough means the customer may move on to the next supplier.

◉ IT and cloud efficiencies. By moving to the cloud, IT efficiencies are being explored in every IT department. Your company’s journey to the cloud can impact which solution best suits the digital conversation you have with your organization’s business partners.

◉ Increasing transaction complexity. As some customers and suppliers request semi-custom APIs and Hybrid EDI-API options, complexity soars.

◉ Increasing costs of data breaches. As found in the IBM Security report Cost of a data breach 2022, the cost of a breach has risen 12.5% as ransomware and destructive attacks surge.

Trends, challenges, disruptions

Ignoring these trends can be extremely costly. If not appropriately analyzed, trends can become challenges. And unaddressed IT challenges can become business disruptions. Businesses that ignore this landscape are more likely to experience order delays and interferences resulting in revenue loss, unexpected costs or customer dissatisfaction.

As these challenges remain unaddressed, IT teams are beset by tasks of increasing complexity driven by weak or inconsistent security and lack of visibility.

Siloed solutions and IT skills shortages are also common pain points. These challenges cause companies to struggle with their journey to the cloud and modernization efforts, leading to difficulty in scaling and slow onboarding of new customers or suppliers.

Is your business experiencing any of these IT trends in your file transfer or B2B transactions? Then you need modern solutions to meet and overcome IT challenges before they impact your business. And if your organization is not yet seeing these trends surface, then it’s important to start building the foundation for growth your business needs before the storm hits.

Businesses need a modern and reliable B2B data exchange solution built for demanding workloads. An effective solution must provide:

◉ Connectivity across a wide range of standards and protocols, data translation and validation

◉ End-to-end insight at the document, transaction and business level

◉ Rapid onboarding and efficient management of customers and partners

◉ Hybrid cloud, on-premises, and SaaS capabilities to support self-managed, vendor-managed or custom deployment

◉ Secure, scalable and reliable solutions for a diverse range of demanding workloads

IBM Sterling® Data Exchange is one such effective solution: a group of offerings built to improve the exchange of data with customers and suppliers, revolving around managed file transfer (MFT) and B2B integration. Take advantage of IBM’s long-standing experience in EDI, APIs and MFT business data exchange.

Source: ibm.com

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Experiential Shopping: Why retailers need to double down on hybrid retail

Shopping can no longer be divided into online or offline experiences. Most consumers now engage in a hybrid approach, where a single shopping experience involves both in-store and digital touchpoints. In fact, this hybrid retail journey is the primary buying method for 27% of all consumers, and the specific retail category and shopper age can significantly increase this number. According to Consumers want it all, a 2022 study from the IBM Institute for Business Value (IBV), “today’s consumers no longer see online and offline shopping as distinct experiences. They expect everything to be connected all the time.”

Experiential hybrid retail is a robust omnichannel approach to strategically blending physical, digital, and virtual channels. It empowers customers with the freedom to engage with brands on whichever shopping channel is most convenient, valued or preferred at any given time. For example, consumers may engage in product discovery on social platforms, purchase online and pick up items at a store. They may also be in a store using digital tools to locate or research products. The possibilities are endless.

While hybrid retail is now an imperative for brands, it has created new complexities for retailers. “Channel explosion is a reality and retailers are challenged to scale their operations across what is essentially a moving target,” says Richard Berkman, VP & Sr. Partner, Digital Commerce, IBM iX. The result is often disconnected shopping journeys that fail consumers. Imagine selecting the “in-store pickup” option for an online purchase, only to discover that fulfillment of the order was impossible since the store was out of stock.

According to Shantha Farris, Global Digital Commerce Strategy and Offerings Leader at IBM iX, the real cost of an unsuccessful approach to hybrid retail is losing customers—potentially forever. There are still a lot of pandemic-weary consumers for whom patience and tolerance for shopping-related friction is at an all-time low. Additionally, people remain desperate to feel connected. Retailers must be totally on point, pleasing customers with friction-free and highly experiential omnichannel commerce journeys. When this doesn’t happen, customers can react harshly. Farris refers to this phenomenon as “rage shopping” and observes that consumers will choose to shop elsewhere based on one disappointing experience. “End customers demand frictionless experiences,” she says. “They’re empowered. They have choices. They want to trust that their brand experience will be trusted, relevant, and convenient—and that this will remain true every time they shop with you.” Retailers must modernize their technology ecosystem for omnichannel and cross-channel consistency.

“Whether the transaction itself occurs digitally or physically is beside the point. It’s got to be experiential.”

Websites. Mobile apps. Social, live streaming and metaverse platforms. Determining which channels to strategically activate is tricky, but it’s not impossible. Commerce begins with brand engagement and product discovery, so it is critical to leverage data-driven insights to understand customers: everything from who they are to how they prefer to progress through the end-to-end shopping journey and how compelling they rate the experience. Then, Berkman says, “retailers need an experience-led vision of the future of their commerce initiatives across channels, with an ability to activate data and dynamically manage those channels.”

Which channels offer the best chance for positive consumer engagement? It depends on the brand. Additionally, measuring the success of each individual channel cannot be assessed using only conversion metrics. Farris comments, “You might discover a product on TikTok, but conversion will probably take place elsewhere.”

A primary benefit of augmented reality is increased consumer engagement and confidence at the earliest stage of a purchase.

The reality of rage shopping is a useful premise for retailers re-examining the current efficacy of every interaction along the purchase journey. Each step, from product discovery to last-mile fulfillment and delivery, needs to “meet customers where they are and evolve into one connected experience,” Berkman says.

Here are three ways to approach hybrid retail using technology along the customer journey. “Whether the transaction itself occurs digitally or physically is beside the point. It’s got to be experiential,” Farris says. “And to provide that experience, you need technology.”

Enhance product discovery with AR

A primary benefit of augmented reality (AR) is increased consumer engagement and confidence at the earliest stage of a purchase. Farris points to work done for a paint company in which IBM designed and deployed a color selection tool, which allows consumers to virtually test different paints on their walls. “There’s a huge fear factor in committing to a paint color for a room,” she says, but with virtual testing, “all of a sudden, your confidence in this purchase goes through the roof.”

AR has a measurable impact on reducing returns, which can cost retailers up to 66% of a product’s sale price.

Similar AR tools have been a hit for retailers like Ikea and Wayfair, allowing consumers to see how furniture will look in their actual homes. Smart mirrors provide another example: This interactive AR tool enables a quicker try-it-on experience, creating an expanded range of omnichannel buying opportunities for in-store shoppers. Effective AR use is also shown to have a measurable impact on reducing returns, which can cost retailers up to 66% of a product’s sale price, according to 2021 data from Optoro, a reverse logistics company. And a 2022 report from IDC noted: “AR/VR—over 70% view this technology as important, but less than 30% are using it.” That said, a study shared by Snap Inc found that by 2025, nearly 75% of the global population—and almost all smartphone users—will be frequent AR users.

Empower decision-making with 3D modeling

“Digital asset management is a fundamental part of commerce; 3D assets are just the next generation of it,” Farris says. 3D coupled with AR allows consumers to manipulate products in space. “It’s about making product information really convenient and relevant for consumers,” she says. In 2021, Shopify reported on average a 94% increase in the conversion rate for merchants who featured 3D content. One example is being able to virtually “try on” a shoe and see every angle of it by rotating your foot. The technology is useful for B2B too. “Instead of reading 50 pages of requirements and specs for some widget, buyers can actually turn the part in space and see if it’ll fit on an assembly line,” Farris says.

3D assets coupled with AR go beyond providing retailers with today’s tools. It’s a measure of futureproofing. “Some of these technologies will give you immediate returns today,” Farris says, “but they will also help retailers build capabilities that will be applicable to deploying a full metaverse commerce experience.”

Digitize how consumers interact with physical stores

In-store customer experiences can be significantly enriched with the use of digital tools and seamless omnichannel integration. Farris points to a major home improvement retailer that does this well. “If you go into one of these stores and can’t find an associate to help you, you can whip out your phone, go to the store’s website, and it’ll tell you what bin a product is in, down to the height of the shelf it’s on. Your phone becomes your in-store guide.”

The employee experience is also dramatically improved with the right digital technologies and omnichannel access. “Store associates need to have real-time data and insights relative to anyone who might walk in the door,” Berkman says, noting that associates, service agents and salespeople should act more like “a community of hosts.” Armed with the right information and access to technology like predictive analytics and automation, Berkman says, “those employees would have the insights to effectively engage customers and create more immersive and personalized experiences to drive brand growth.”

Source: ibm.com

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Drama, disruption and daring to look ahead

The recent potential for a rail strike in the US threatened a system integral to our supply chain. A disruption would have catastrophic impacts on industries, suppliers, manufacturers and retailers across the country. It would be yet another stressor on an already strained supply chain that, over the last 3 years, has faced disruptions from the global pandemic, rising inflation, climate change, historic geopolitical events, unstable financial markets and sustainability challenges.

As chief supply chain officers (CSCOs) walk the tightrope over potential disaster, their steps are far from tentative. A new report by the IBM Institute for Business Value (IBV) collects insights from 1500 surveyed CSCOs. According to the report, CSCOs are helping their organizations differentiate and create competitive advantage by using data and AI to improve their supply chains. And that competitive advantage is driving profitability and an increase in revenue over their peers.

Smart investments give clear advantages

Corporations that align their digital and sustainability transformation agendas achieve 20% higher revenue growth when compared to their peers, the report reveals. CSCOs are seeing a clear competitive advantage because of these investments, from a top-line and bottom-line perspective. They’re also getting a differentiated proposition to attract top tenured talent and early professional talent.

Over half of surveyed CSCOs are accelerating investments in digital technologies, including increasing automation and digitization of physical and asset-driven processes. 48% are applying open innovation with business partners, 46% are exploring new risk models and 54% are taking a cutting-edge approach to data-driven innovation, including employing predictive approaches and implementing tech-infused workflows.

Take for example the Hong Kong Applied Science and Technology Research Institute Company Limited (ASTRI). This R&D center for information and communications technologies is tasked with promoting Hong Kong’s competitiveness in technology-based industries. As part of its mission, ASTRI is helping manufacturers shorten their time to market, reduce development costs and enhance the quality of their products.

ASTRI has developed a digital-twin approach to equipment development. By implementing a science-based, agile approach to designing smarter manufacturing equipment, leveraging intelligent workflows across assets throughout the extended production process and using requirements-driven analysis and a model-based design, the organization creates a digital twin of an equipment piece. This allows engineers to perform a wide range of simulations and tests at nominal incremental cost to identify potential design defects sooner in the cycle. This model-based method also enables earlier validation of customer requirements.

ASTRI estimates that the twin approach has reduced integration time by 40% and cut the total cost of development by 30%. In addition, the use of robotic automation, IoT sensor integration and digital twin modeling for predictive maintenance supports 24/7 factory uptime.

Innovators edge out the competition

Using such transformational technology investments, CSCOs manage their supply chains to drive powerful results. They ratchet up tech strategies and adopt a data-driven innovation approach that emphasizes the scaling of a hybrid cloud infrastructure, AI-enabled workflows, customer-focused sustainability and a deeper focus on cybersecurity. These CSCOs make up the top 20% of those surveyed, also known as “the innovators.”

Innovators are edging out other CSCOs by embracing digital transformation. They use hybrid cloud platforms 60% of the time, versus 49% among their competitors. Innovators also lead the field with digital infrastructure that enables new technology investments to scale efficiently and deliver value. They use digitalization and AI automation 81% more than their peers. 90% of Innovators use AI and advanced analytics in demand management and predictive forecasting, which is 18% more than their peers.

The results highlight the importance of innovative CSCOs who prioritize digitalization for efficient end-to-end visibility and dynamism. In short, if you want to increase performance and value, data-led innovation is your journey.

Source: ibm.com

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How IBM Consulting and the US Open evolve the fan experience and accelerate innovation

IBM® has been the official technology partner of the US Open Tennis Championships for more than three decades, and the relationship goes much deeper than courtside logo placement. It’s an ongoing partnership delivering world-class digital experiences to fans, built on IBM’s open, flexible technology platform. “We need to constantly innovate to meet the modern demands of tennis fans, anticipating their needs, but also surprising them with new and unexpected experiences,” says Kirsten Corio, Chief Commercial Officer at the United States Tennis Association (USTA).

Read More: C9560-507: IBM Tivoli Monitoring V6.3 Implementation

Year after year, IBM iX, the experience design arm of IBM Consulting™, works with the USTA to integrate technology from dozens of partners, automate key business processes and use the power of artificial intelligence (AI) to transform vast quantities of tennis data to deliver key insights.

Bringing fans closer to the game they love

This year’s tournament features colorful personalities and compelling stories. But as host of a leading spectator event viewed by nearly 10 million people every year, the USTA is charged with delivering ever more engaging experiences. IBM Consulting asked: How can we use the digital experience of the US Open to serve the USTA’s mission and grow the game of tennis? How can we better serve fans with live scores, stats and player information while they watch a live match? How can we deliver the answers they need? How can we provide relevant and timely insights they can’t find anywhere else?

These questions led to several innovations: The IBM Power Index with Watson ranks player momentum and combines performance and punditry, queried through IBM Watson® Discovery, to create a “Likelihood to Win” prediction and highlight compelling matchups. Match Insights with Watson delivers head-to-head pregame analysis of every match, using natural language generation to translate historical statistics into easily read sentences. And US Open Fantasy Tennis, enriched with Match Insights, lets fans create and follow their own fantasy team.

A collaboration that drives innovation

Creating digital experiences that drive enthusiasm requires a human lens. To achieve that, the US Open digital strategy team partners closely with IBM iX, one of the largest business design consultancies in the world. IBM iX uses collaborative design thinking brought to life by the IBM Garage methodology — an end-to-end model for accelerating digital transformation — to address challenges within a variety of management frameworks including lean startups, human-centered design, agile and DevOps.

Stage 1: Co-create

At the co-create stage, squads agree on the nature of the challenges, prioritize them and conceptualize solutions. For example, for the 2022 US Open, a top priority was providing more explainability to the “Likelihood to Win” prediction.

Stage 2: Co-execute

At the co-execute stage, development teams build minimum viable products or solutions, and test them. Using this process, IBM and USTA developed the “Win Factors” feature, which shows the top three variables affecting the prediction such as head-to-head record, winning record on this surface or Power Index rating.

Stage 3: Cooperate

The cooperate stage is not simply about operational maintenance. It’s about ongoing performance management, improvement and product development. The USTA and IBM Consulting cooperate virtually year-round to develop and refine the digital experience, starting with a debrief after the tournament asking questions such as Where did we succeed? What could be improved? How can we be more efficient and effective?

Beyond solving the problems at hand, co-creating with IBM Garage can be a transformative experience for organizations, helping them prioritize their development queue, iterate solutions and evaluate them in a cycle of ongoing improvement. Using this method, IBM and the GRAMMYs delivered artist insights for live coverage based on IBM Watson analysis of millions of articles. IBM and the Masters® built a digital platform to scale the capabilities of the Masters Digital team.

Over 30 years in, IBM and the US Open continue to overcome new challenges and engage fans with new experiences. For a tournament, fan expectations and technology that are always evolving, this partnership keeps the USTA ahead of the ball.

Source: ibm.com

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Technology to support the journey to net zero

As companies around the word focus their attention on reducing greenhouse gas (GHG) emissions to deliver on their net zero commitments, the requirement for robust data and analytics to support this journey is intensifying. Technology vendors who service this market with carbon management software are the focus of a recent Verdantix Green Quadrant study.

We are delighted that IBM scored so highly in the 2022 edition of the Verdantix Green Quadrant: Enterprise Carbon Management Software study. The report is welcome validation of our leading position in the market and encourages us to continue on the path to expand key functionality and capabilities in this space. It also highlights the synergies we have made between Envizi, the IBM Environmental Intelligence Suite and other mission-critical software.

The Verdantix report focuses on key functionality required to deliver carbon management outcomes, including data quality control, renewable energy sourcing and the ability to measure and track physical climate risk. IBM achieved leading scores in all three of these criteria in the Verdantix Green Quadrant, achieving the highest cumulative score for enterprise carbon management. The value of this was reinforced by the report’s authors, who noted, “IBM offers customers a 360-degree view of GHG emissions across their operations alongside integrated tools for climate risk assessment.”

The report provides a detailed fact-based comparison of the 15 most prominent carbon management software vendors in the market. It includes a set of capability criteria that will drive value for any organization looking to assess different products in this sector, including:

◉ Data acquisition

◉ Data management

◉ Data modeling (scope 1, 2, and 3)

◉ Data quality control

◉ Carbon accounting methodologies

◉ Carbon emissions calculation engine

◉ Renewable energy sourcing and contracts

◉ Net zero strategy development and implementation

◉ Carbon disclosure management

◉ Physical climate risk

◉ Organizational data management

Data and AI are core to accelerating your sustainability journey

Once your sustainability strategy and goals are set, the next step is to establish a data and systems infrastructure to track ongoing performance and inform the operationalization stage, in which you embed sustainability decision making into daily business operations.

As the core data layer in your sustainability software stack, Envizi is designed to collect, manage and derive insights from sustainability data. Providing a comprehensive single system of record, it supports the integration technology required to send and receive data from data lakes or any other sources of data, such as metering systems, IoT platforms, utility providers, ERP systems and other third parties whose data is required to calculate a comprehensive GHG emissions footprint. This functionality is complemented by our Environmental Intelligence Suite, which incorporates weather, climate and environmental data to assess physical climate risk.

Envizi connects with a growing number of IBM operational performance improvement systems including TRIRIGA and Maximo. Further connectivity solutions are currently in development, with Turbonomic and the Supply Chain Intelligence Suite planned for delivery by the end of 2022.

Our practical approach aligns sustainability goals to business objectives. With open technology and consulting services, we work with companies to operationalize sustainability end-to-end by integrating and automating quality environmental, social and governance (ESG) data into daily workflows in a robust and auditable way. IBM’s breadth and depth of capabilities plus industry-leading research helps customers set, operationalize and achieve their ESG goals. IBM can help you accelerate your enterprise carbon management and sustainability journey.

Source: ibm.com

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Customer-driven digital marketing: Marketing has become one of the key drivers for enterprise growth

According to a 2019 McKinsey report, 83% of CEOs say marketing has become one of the key drivers for enterprise growth. But how does it work in reality? On an operational level, people still believe marketing is the first budget to be sacrificed when profitability slumps. The marketing budget is often reduced to improve the firm’s overall financial results in the short term.

50% of marketers say they struggle to demonstrate their quantitative impact according to a 2020 Gartner Marketing Data and Analytics Report. Having held multiple senior marketing leadership positions myself, I have firsthand experience with this struggle.

Read More: C1000-058: IBM MQ V9.1 System Administration

I am thrilled that nowadays marketers have strong, reliable data to prove their value and impact. They can specify how actual revenues are driven through marketing campaigns and calculate the respective ROI of specific channels. By taking advantage of real-time data, augmented through artificial intelligence, marketers can adjust campaign, advertisement and agency spending in real time.

Investments in innovative technology drive enormous efficiencies. But how many marketing departments are following the call to digitize their processes?

To be a true partner for the CEO, marketers need to dive deeper into numbers. To achieve this, here are three areas that provide CMOs unparalleled opportunities to make their case, crystallizing the value that marketing contributes to manifest significant revenue growth for B2B and B2C:

◉ Harvest significant cost savings through operational efficiency in marketing

◉ Focus on measurable dimensions of customer centricity in a cookie-free world

◉ Generate incremental revenues through real-time AI-driven analytics and campaign steering

Harvest significant cost savings through operational efficiency in marketing

While the merits of creativity in marketing remain undoubted, efficiency plays a vital role in the complex orchestration of marketing tools and assets. Marketing organizations can gain efficiency by questioning processes to find more ways to leverage technology.

Having an online collaboration tool embedded within your digital asset management software provides the backbone of the workflow, providing the ability to track each change request, each piece of feedback, and each change executed along the complete workflow. This transparency leads to significant cost savings in agency fees.

CMOs should also consider evaluating the opportunities to industrialize the creation of standard marketing assets such as landing pages and banners. Pre-configured templates enable marketers to launch a campaign or a promotion in a matter of hours versus days or weeks.

Focus on measurable dimensions of customer centricity in a cookie-free world

As the focus shifts from third-party cookies towards primary data, marketing requires the collection of data and preferences from all channels in one single system. Turning to a first-party, data-led multichannel marketing strategy requires marketers to customize content with the help of marketing technology and innovation. Marketers need to create a continuum of feedback and analysis to improve and maximize use of valuable data from multiple sources, including sales, actual experiences, real-time analytics and customer service data.

To leverage this set of diverse information for marketing campaigns, you must enrich it with attributes related to algorithms and powered by artificial intelligence. Intelligent marketing campaign design and marketing platforms allow truly customized content to be automatically created and shared with the customer via the preferred channel of communication.

Generate incremental revenues through real-time AI-driven analytics and campaign steering

Only 54% of marketing decisions are influenced by analytics, and CMOs are often slow to adapt to data-driven decisions and recommendations, according to a 2021 Forrester Global Marketing Study. Many marketing departments still need days or weeks to compile reliable data. The shift to relying on a combination of data from multiple sources, along with the improvement of cross-channel attribution, is paramount to fully understanding customers and the market.

A good system constantly monitors the results based on classic marketing KPIs, ROI and revenues. Underlying negative trends can be identified before they have an impact on marketing campaigns, revenues or business in general. This leads to a more objective, predictive and proactive approach to data discovery, automatically identifying patterns and trends that humans may never uncover. It can unveil bias within data sets stemming from unconscious human preconceptions or flawed data collection techniques, helping to avoid a negative performance impact.

By mirroring the customer journey, attribution modeling reveals which parts of the experience customers prefer and which parts need to be enhanced.

Conclusion

Though CMOs face several challenges, they are now in a unique position to manage marketing like a business operation. Digitalization allows them to harvest significant cost savings by implementing efficient and seamless processes within the marketing team. Additionally, partnering with creative and media agencies frees up resources and allows for significant cost savings. Addressing the skill gap unlocks the full potential of data and technology.

Aligning all campaign data and introducing real time analytics unleashes incremental revenues. If the data and customization strategy is in place and aligned with the business objectives, CMOs can set their companies apart from competitors, increase NPSs significantly, and create stable brand loyalty. Now is the time that CMOs, along with their teams and partners, can respond to the CEO’s call to identify new areas of enterprise growth.

Source: ibm.com

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Get more value from your data with a data transformation roadmap

Data Economy - The Transformation Roadmap

So, how can you get the most value from your data? A knowledgeable and phased approach facilitates a smooth transition from legacy practices and products to processes that tap into the advantages of Canada’s data economy. Defining policies and roles, developing data-sharing control mechanisms, understanding existing and potential data across the company and beyond, and planning best use cases will lead to increased profitability, reduced operating costs, expanded products and services, and valuable insights to benefit you and your customers. And efficiently shared data promotes constructive collaboration with partners and stakeholders, both internal and external.

I recommend a three-step tactic of migration, modernization and monetization. Migration moves the data to the most appropriate cloud environment, or target state architecture, where core data models can be rebuilt and modernized, and then monetized through effective data and digital agile ecosystems that are ready for growth.

Here are two examples of the positive impact of data monetization in two very different industries.

Internal data monetization: an international airline launches a transformative journey

A large international airline needed to transition to a more streamlined technology landscape with an optimized operating model. Analysis of their current data landscape revealed multiple legacy applications that could not yield the insights required for their growth.

With guidance from IBM, the airline launched a Data Platform Stability and Modernization journey, migrating select on-premise data platforms and workloads to the cloud. Within the modernized data landscape, they could create models for customer and passenger data, then expand the insights to different lines of business, such as cargo, loyalty and commercial applications.

Their data modernization journey has realized three significant benefits. Revenue has been increased through their transformed data sources, channels and products. They have developed a data-driven decision-making culture through the resilient, cloud-based data environment. And their data security and governance has been improved, setting a foundation for realizing a good return on investment through further data monetization and data-sharing initiatives in the future.

External data monetization: Yara goes from bushels to bytes

“Agriculture is one of the last industries that has focused on systematic process optimization.” — Pål Øystein Stormorken, Yara

Norway-based Yara is the world’s largest fertilizer producer. It has established a solid reputation as a reliable source of information and a distributor of agricultural products, with an ethical and balanced approach to best practices for food production. Yara is dedicated to the exploration of new technologies that promote sustainable intensification to protect the environment, growing more food on existing farmland and avoiding deforestation. With the United Nations estimate that the population will reach 9.7 billion by 2050, along with alarming statistics on climate change and soil loss, Yara wanted to find solutions to the challenges to the food supply.

Yara partnered with IBM to build a digital farming platform with two new products: weather forecasting and crop-yield forecasting, following a pay-as-you-go commercial model. The cloud-agnostic strategy enables consistent data governance and data security, using DataOps to automate data functions so that its scientists could focus on data models and innovation.

The platform provides holistic digital services and instant agronomic advice around the globe, with the ability to reach 620 million farmers and serve up to 7% of the world’s arable land. These accelerators are just the first of many: an open innovation layer will allow Yara to create new revolutionary algorithms and a cognitive roadmap for farmers through constructive decision-making insights.

This is an example of the power of data monetization, generating not only business value, but also societal value in sustainable practices.

Your Opportunity

Canada will generate value for all of its citizens, industries, businesses and researchers by developing a flourishing data economy. IBM can help you understand and monetize your data, guiding you through your journey as you assess and prioritize your needs, select the right governance and operations models, and design a plan that propels you into the exciting future of data-driven innovation.

Source: ibm.com

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How Canada is growing its data economy

The data economy is booming. In 2021, IDC estimated the value of the data economy in the U.S. at USD 255 billion, and that of the European Union at USD 110 billion. In these and many other regions, growth in the data economy outpaces GDP. IBM has examined Canada’s particular potential for data leadership, with lessons for any other country hoping to compete in the data economy.

Will we get to CAD 1 trillion value of data in Canada before 2030? In mid-2019, Statistics Canada estimated that Canadian investment in “data, databases and data science” has grown over 400% since 2005. At an upper limit, the value of the stock of data, databases and data science in Canada was $217B in 2018, roughly equivalent to the stock of all other intellectual property products (software, research and development, mineral exploration) and equivalent to more than two-thirds the value of the country’s crude oil reserves.

As the world continues to rapidly change around us, ground-breaking opportunities are presenting themselves that will shift the fundamentals of how businesses, governments and citizens function. This shift will be supported by enormous amounts of data, regardless of the part of society in which these transformations take place.

What is the data economy?

The amount of data throughout the world has almost doubled in just two years, with growth expected to triple by the year 2025. With data’s unprecedented growth, important decisions will have to be made about how to use it; and these decisions will determine the commercial success or failure of the digital revolution.

The data economy is the social and economic value attained from data sharing. While data has no inherent value, its use does. When it is organized, categorized and transformed into information that can drive innovation, solve complex problems, create new products, or provide better services its value becomes apparent.

While data can solve critical challenges in our society, most of its value is inaccessible due to the siloed and fragmented nature of most data ecosystems. Governments cannot develop effective policies; business leaders are unable to fully tap their resources; and citizens are prevented from making informed decisions. Leveraging data to benefit society depends upon the amount of connections that we can form between contributors and consumers, among enterprises and governments. A prosperous data economy must be linked to intelligent governance, administered for the good of everyone.

Why does it matter?

1. Citizens can assume more control of their data, ensuring its appropriate use and security while benefiting from new products and services.

2. Businesses can customize their products to align with their clients and better manage regulations.

3. Governments can collaborate on national and international strategies to achieve optimum effectiveness on a global scale.

And what can it do for you?

The profound implications of well-managed global data exchanges illuminate the vision of a better world, opening the window to myriad possibilities:

◉ Fighting disease through shared research on diagnostics and therapeutics

◉ Identifying global threats and reacting to them quickly

◉ Deploying advanced applications to solve organizational issues, unlocking innovation

◉ Harnessing data to promote environmental health, prevent environmental degradation and protect at-risk ecosystems

◉ Coordinating data to benefit industrial sectors such as tourism or agriculture

Canada has the potential to create a world-leading data economy, positioning us to develop innovations that will allow us to compete globally. We have many advantages in our favour: a highly trained workforce strengthened by our skills-based immigration system; our government’s commitment to accountability, security and innovation; and our unique history, geography and public policies.

Our success will depend upon a collective effort to promote engagement and facilitate the transition to a data-driven economy. Together with its financial investment, Canada must focus on cultivating data literacy among its citizens, as businesses increasingly embrace digitized platforms.

Fast-tracked by COVID-19, investment in data science has accelerated, alongside the proliferation of emerging technologies. By leveraging the opportunities in the rising data economy, Canada can unlock a trillion-dollar benefit within the next decade.

Source: ibm.com

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