In this article, we will explain different types of realities such as Augmented Reality, Mixed Reality, and Virtual Reality.

What Is Reality?  

“We have had people literally run out of the VR room, even though they know that what they are witnessing is not real” stated Mel Slater, Distinguished Scientist, and VR pioneer, at the Institute of Neurosciences of the University of Barcelona. With this strong opening statement, we can have a fair idea of how strong this world is.

Have you ever been in an argument over a specific color with anyone? Did it seem black to you and deep blue to the other?  Every human being perceives the same thing with a different aspect. Humans tend to confuse reality with the actual world and fail to get the point of why virtual reality feels real even when they are fully aware of it.

What Is Virtual Reality (VR)?  

VR is an artificial environment created by a computer to give users a feel of a world that does not exist in real life. A person using a VR set will be able to look inside the artificial world, move around in it, and interact with virtual features in that world. It must be sounding like a sci-fi movie but this is what happens in VR. Virtual reality (VR) is a technology that gives a simulated experience that allows the creation of a fully immersive digital environment.

Although VR has been in existence since 1994, it is only now that such technologies are being hyped and have attracted global curiosity. This technology supports the vision of metaverse clusters by aiding in creating the 3D virtual world. The foremost advantage that comes to my mind- it gives people the chance to travel around the world, and even outer space, without having to leave the comfort of their homes since the users are placed in a virtual space with the help of a few gadgets like a headset or eyewear. In a nutshell, we can say VR is an imaginary space that independently exists from the real world.

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What Is Augmented Reality (AR)?  

This world of reality is an extension of the physical world around you, like adding digital content onto a live camera feed, making it look real. It works on a software application, for example, making you look like a giraffe by overlaying digital directions onto the physical streets around you. However, there is no interaction between the digital elements and the physical world elements.

Although AR doesn’t create an artificial environment, it uses real-time information in the form of text, graphics, audio, and other virtual enhancements integrated with real-world objects. It brings together real data and virtual data to create an augmented reality. The first AR technology was developed in 1968 but today we can see its magical wand working in every field such as gaming, education, healthcare, and manufacturing. In a nutshell, AR is an interactive experience that combines the real world and computer-generated content.

What Is Mixed Reality (MR)?  

Unlike AR, a semi-digital experience, a fully digital experience, MR embraces its users with the best of both worlds. MR example includes Instagram filters, virtual makeup applications, and virtual furniture fittings. MR is a technological boon that allows not only the superposition of digital elements into the real environment but the users can visualize and interact with both the digital elements and the physical worlds.

MR is the hybrid reality merging both real and virtual entities to produce new environments. Below is an exhibit reflecting the outlay of a single space of all three worlds of reality.

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VR vs. AR vs. MR: What’s The Difference?

Every virtual environment is not a metaverse, and every pair of smart glasses does not provide an augmented reality experience. There is a thin line of difference between all these which is being shown in the form of an exhibit below. Since all three forms of reality revolve around two world forms- the real world and the artificial world. So let’s remove the confusion by taking its differences.

In AR the users can control their presence in the real world, while in VR the users are controlled by the computer system. VR requires a headset and eyewear equipment, but AR can be accessed with a normal smartphone.

So, only in the case of VR, the user is not aware of the real world, neither he can interact with that world in real-time, nor, both worlds (real and virtual) can interact with each other in real-time. I know it might seem quite complex initially and it must be making your head spin, but the exhibit below shall make the concepts clear.

VR is a fully immersive based digital environment. AR is a snapshot of the real world with an overlay of digital elements. MR is a view of the real world with an overlay of digital elements where physical and digital elements can interact and the users can visualize it.

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Conclusion

Recent advances in AI, especially deep learning, which enable real-time voice and picture recognition have aided in the integration of VR with AI for new applications. All three of these technologies will need to be taken into consideration in product design in the future. Examples of Eolian applications that use AI technology to lower human mistake rates through AR and VR simulations of risky activities are noteworthy.

Through machine learning and AI, Virtualitics offers data visualization in VR and AR settings. These days, we have access to tools that make it easier than ever to exchange information and create stories. They can be employed as highly potent emotional engagement tools that help us connect with audiences and foster empathy beyond what TV, computers, and the internet previously could.

[To share your insights with us, please write to sghosh@martechseries.com]

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Almost 50 years ago, the barcode was scanned for the very first time in 1974 on a pack of Wrigley’s chewing gum, kick-starting the retail industry’s digital transformation. Once viewed as a groundbreaking innovation, the resounding “beep” of a barcode scan is now commonplace at checkout counters and self-serve kiosks around the world – but the industry is transitioning away from this technology, and retailers and industry leaders say it’s for the better.

The UPC Barcode’s Legacy

The UPC barcode, found on the back of every consumer product today, will always be known for its role in streamlining retailers’ front-of-store and back-end process. Its ability to store a product’s identification in a linear format and digitally scan at point-of-sale eliminated the need for cashiers to manually enter prices at the register, expediting the checkout process while simultaneously minimizing human error and reducing costs. Additionally, prior to the barcode, retailers had to manually count every item on their store shelves, in their back rooms, and in their warehouses to assess inventory levels – a daunting process that was often conducted infrequently and inaccurately, allowing for product shrinkage to go unnoticed and making inventory management a challenge.

The UPC barcode enables retailers to more accurately and efficiently track, plan, and adjust their product stock levels.

But despite its immense contributions, the UPC barcode wasn’t built for today’s tech-forward industry, hindering retailers’ digital transformation progress. While useful for price lookup at checkout and basic inventory management, the UPC can only hold a limited amount of information, the product’s identification number – this is no longer enough.

The Industry’s 2D Transition

Unlike 1974, retailers have significantly more to contend with than long queue lines and irregular inventory counts. To compete, they need a standardized way to address both supply chain needs and consumer demand. Thanks to explosive supply chain issues and changing priorities since 2020, it’s easy to picture the evolving market – empty store shelves and overflowing warehouses from inaccurate demand forecasting during the pandemic, the nation’s on-going battles with product or food recalls (which hit a 10 year high in 2022), and the evolving ESG regulations aimed at mitigating green-washing in consumer product marketing and supply chain processes. Tackling each of these challenges requires a level of product information and traceability largely unseen in our global supply chains today.

The need to address these gaps in supply chain visibility and data quality is driving retail’s transition to two-dimensional or 2D barcodes. Unlike the legacy UPC technology, 2D barcodes, commonly seen as QR codes, can encode more granular levels of product identification including a batch/lot or even a serial number. Additionally, using GS1 Digital Link it can link to highly detailed product information – including a product’s ingredients or components, where its sourced, how it’s made, and where it stops along the supply chain – to enhance retailers’ initiatives for consumer safety, trust, and engagement.

The Beginning of a New Retail Era

Through an initiative known as Sunrise 2027, guided by GS1 US and industry, retailers have committed to being capable of reading and processing the 2D barcode at point of sale, as brands transition from UPC to 2D barcodes. But many innovative brands are already making the switch today.

PepsiCo is delivering better experiences and up-leveling its digital operations with 2D barcodes on their products. These 2D barcodes take traditional QR code capabilities a step further – rather than pointing to a static URL, they leverage data standards, like GS1 Digital Link, to web-enable their barcodes and provide connections to all types of business-to-business and business-to-consumer information.

In the event of a product recall, PepsiCo’s manufacturers can use the 2D barcodes to quickly notify consumers, convey necessary safety information, and help retailers pull contaminated products from store shelves.

If a consumer picks up a bottle of PepsiCo’s Starry soda and scans the on-package digital QR code with their smartphone, they will immediately find key details on the drink’s ingredients, allergens, and nutritional information. They can also access insights on the product’s sustainability and recycling processes, as well as loyalty rewards or curated brand content (i.e. PepsiCo’s TikTok page) for further engagement opportunities.

Meanwhile, if a consumer scans the QR code on Puma’s sneakers, they can see proof of the product’s sustainable development and identify the materials that were used to create it. This information on when, where, and how the shoes are produced instills consumer trust and loyalty with Puma by allowing shoppers to validate the company’s ESG claims in an age where retail greenwashing runs rampant.

But the implementation of 2D in retail goes beyond consumer information sharing, helping retailers improve back-end processes, like inventory management. 2D gives retailers like Woolworths (an Australian Supermarket) a more granular view of their inventory, so they can identify which products are nearing expiration, reposition them towards the front of store shelves, and mark them down to encourage sales, thereby reducing food waste by over 40%.

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The retail industry is just scratching the surface on what can be done with 2D barcodes today, but one theme is clear – the technology will drive an unprecedented level of transparency and information sharing between brands and consumers moving forward. By 2027, 2D barcodes will become more commonplace on product packaging, allowing brands to adapt to the latest consumer preferences, curate new customer experiences and promotions, distribute essential product information, authenticate purchases, and more – all while also scanning at point-of-sale, eliminating the need for multiple data carriers taking up package real estate.

[To share your insights with us, please write to sghosh@martechseries.com]

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Customer service stands as a crucial pillar today, directly shaping customer satisfaction and loyalty. However, the conventional methods of customer service often fall short, consuming time and leaving room for improvement. This is where the power of AI steps in – artificial intelligence introduces a way to streamline customer service, expedite responses, and create more individualized connections.

Let’s check some of the advantages of integrating AI into customer service and how it contributes to the success of businesses.

A significant challenge faced by customer service teams is meeting customer expectations within tight time-frames. AI provides a solution by automating routine tasks and offering instant replies to customer queries, thus reducing average response times and enhancing customer satisfaction. For instance, a study by Forrester revealed that a chatbot’s average response time was just about 6 seconds, in contrast to the 8 minutes taken by a human agent. This level of rapidity is particularly beneficial during peak hours or periods of high demand when human agents might struggle to keep up.

Conventional customer service often involves generic templates and scripts that overlook unique customer needs and preferences. AI takes a different route by examining customer data and tailoring its responses accordingly, leading to more personalized experiences.

According to a survey by Salesforce, 75% of customers expect personalized experiences when interacting with businesses. Here’s an example, a chatbot could pose follow-up questions based on a customer’s past interactions or suggest products based on their previous purchases. This personalized touch fosters trust and connection between customers and businesses, resulting in heightened loyalty and retention.

AI’s role in customer service can also translate into cost savings for businesses.

According to a McKinsey report, companies that embrace AI-powered customer service witness operating expenses decrease by up to 20%. This stems from AI’s ability to automate repetitive tasks like data entry and email communication, allowing human agents to dedicate their efforts to intricate, high-value AI tasks efficiency could potentially reduce the need for many customer service agents, as AI can manage multiple interactions concurrently.

AI opens doors to valuable insights regarding customer behaviors and preferences. These insights, in turn, prove instrumental in refining product development, sharpening marketing strategies, and optimizing overall business operations. By scrutinizing customer data, businesses can pinpoint trends and patterns that inform decision-making and help them maintain a competitive edge. For example, AI can assist in identifying fraudulent activity, highlighting unusual spending patterns, and unveiling fresh revenue streams.

While AI offers a plethora of benefits to customer service, there are accompanying challenges and limitations to be mindful of. A significant drawback lies in the potential bias in AI systems, which could result in unfair or discriminatory treatment of specific groups. To mitigate this risk, businesses must adopt rigorous testing and evaluation protocols, ensuring fairness and transparency in their AI systems.

Another hurdle revolves around obtaining high-quality training data, which can be particularly demanding for niche industries or markets. Also, AI systems necessitate ongoing maintenance and updates to sustain effectiveness, entailing substantial resources and expenses.

Effective Integration of AI in Customer Service

To surmount these challenges and harness the complete advantages of AI in customer service, businesses should consider the following best practices:

1. Begin incrementally: Initiate the integration of AI into specific segments of your customer service, such as chatbots or email automation, instead of attempting an all-encompassing deployment.
2. Leverage cloud-based platforms: Cloud-based solutions offer flexibility and scalability, enabling seamless integration of AI without extensive infrastructure investments.
3. Prioritize user experience: Guarantee that your AI system offers a seamless, user-friendly experience that aligns with your brand identity and customer expectations.
4. Monitor performance diligently: Regularly assess the efficacy of your AI system and implement adjustments as necessary to enhance performance and customer satisfaction.
5. Strike a balance between AI and human touch: While AI can handle routine tasks, maintaining a human element in customer service is essential for empathy and understanding.

AI holds the potential to reshape customer service operations by expediting responses, personalizing interactions, and trimming costs. By adapting the best practices and tackling challenges head-on, businesses can effectively integrate AI into their customer service strategies, fostering greater success. As the utilization of AI in customer service continues to evolve, companies that uphold innovation and prioritize customer experiences will be well-equipped to thrive in the competitive landscapes of today.

[To share your insights with us, please write to sghosh@martechseries.com]

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Please tell us about your current role at PwC. How did you arrive at this company? 

I currently serve as a Cloud and Digital Partner focused on AI and Data.

I have been at PwC for two years now. I came to PwC after following a career inspired by how AI, Data, and technology impact business. I led multiple new technologies as they came to market over the years including early cloud computing, the Internet of Things, and Watson. And, I later worked in services roles to help apply these technologies to transform business.

AI trends have completely changed in the last 2 years. Could you tell us about the key challenges for the AI development and research teams looking to build a more human-led technology infrastructure?

Building a more human-led technology infrastructure in AI, while beneficial, also presents several key challenges and considerations for researchers and IT teams. One of the biggest challenges until recently, was that AI was considered a deep technology limited to data scientists. This made it harder to engage people who could think about the human side of AI. At its core, AI is a tool that helps people work in new ways, so understanding the intersection of the human experience with technology is essential to ensure it delivers value and has a positive impact on people, businesses, and society.

Given the power and scale of AI, ethical considerations are paramount. Before even harnessing the power of AI, leaders, and workforces need to understand where it should be applied and how to establish guardrails that consider the potential impacts of bias, privacy, and transparency in AI systems. Additionally, data quality is critical to delivering quality AI solutions.

So, it must be carefully addressed to ensure diverse and unbiased datasets are used in training.

Effective collaboration between people and AI is also crucial. AI is a tool designed to be used to augment and amplify the work of people. So, designing solutions with AI requires deep involvement from the people who will benefit from it to help shape requirements and to provide feedback on its effectiveness, usefulness, and impact on people. AI systems change over time based on changing data and training, so this is a constant process of human engagement and feedback.

By actively tackling these challenges, AI development teams can strive to create AI systems that empower humans, operate ethically, and contribute positively to society.

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What is a good way to harness AI for businesses? What are the inherent challenges for businesses looking to start from scratch with AI and machine learning?

Utilizing AI can bring significant advantages to businesses, accelerating and automating work, improving the quality and consistency of content and communications, enhancing customer experiences, and helping unlock insights from the vast amount of unstructured data in the enterprise. AI empowers businesses to improve operations and streamline processes, resulting in cost reduction and improved speed and scale.

However, there are challenges for businesses starting from scratch with AI. Skill gaps in data science and machine learning may require investment in upskilling or hiring experts. Data quality is crucial, as AI relies on accurate and unbiased data. Ethical considerations around privacy, fairness, and transparency must also be addressed. Adding to this, integration with existing systems can be complex, requiring seamless incorporation of AI solutions into established processes. If any of these factors are overlooked, businesses can expect these issues to manifest themselves into much bigger challenges.

However, when organizations proactively recognize and address these challenges, businesses can use AI to open doors to enhanced decision-making capabilities, improved productivity, and a competitive edge in today’s data-driven business landscape.

AI practices are now influenced by new techniques to generate new AI models and orchestrate new workflows. Could you tell us how PwC sees generative AI as a key enabler in AI development and orchestration?

Generative AI has greatly expanded the ways that AI can be applied to a wide range of use cases.  Using Foundation models and extending them with your own proprietary data, generative AI can transform business processes. As a result, it is often combined with applications, analytics, and traditional AI/ML to enable new workflows.  Those workflows can transform almost all knowledge work.

At PwC, we see generative AI as a catalyst for innovation, enabling AI systems to generate new content, make intelligent decisions, and enhance the overall capabilities of AI development and orchestration. Its potential to expand the boundaries of AI applications and drive advancements in various fields makes it a key enabler in the ongoing development and advancement of AI technologies.

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You recently announced $1b funding into AI. Please tell us more about this initiative and how it would accelerate AI innovations in your industry.

Our $1B investment will expand and scale our AI offerings and help our clients and partners reimagine their businesses through the power of generative AI. This investment features an industry-leading relationship with Microsoft, creating scalable offerings using OpenAI’s GPT-4/ChatGPT and Microsoft’s Azure OpenAI Service. 

While we recently announced this investment, PwC’s dedication to AI innovation is not a recent development.

PwC has consistently prioritized and invested in AI, reinforcing our capacity to provide solutions that combine human expertise with technological advancements. Our focus on AI empowers us to deliver innovative, human-led, and technology-powered solutions, fostering confidence among our clients and driving sustainable outcomes. Through ongoing investment and strategic efforts, PwC remains committed to staying at the forefront of AI advancements and leveraging its transformative potential to meet the evolving needs of our clients.

Furthermore, PwC will invest in upskilling opportunities for its workforce of 65,000 individuals, equipping them with the necessary skills and expertise in AI tools and capabilities. This initiative aims to enhance their productivity, enable smarter work practices, foster career growth, and empower them to provide valuable guidance to clients regarding the advantages of AI and other transformative technologies.

We are expecting to see this investment have a significant industry-wide impact on accelerating AI innovations, driving transformation, and unlocking new opportunities for businesses and society as a whole.

Your message to CEOs and business owners who want to accelerate sales revenue with AI products and services:

CEOs and business owners must first start by understanding what Generative AI can do.  Becoming “AI aware” is a critical skill for business leaders now as they prioritize their investments and shape their business strategy.

Generative AI has the potential to create new business models and enable disruptive new players in most industries.

Once the CEOs see what it can do, they should focus on identifying where AI can have the most impact. To identify these areas, leaders can start by examining processes that are currently time-consuming, repetitive, error-prone, or involve complex data. By undertaking this critical step, leaders can lay the foundation for successful AI implementation and ensure that AI technologies are deployed where they can make the most meaningful difference.

Leaders should begin now, even with small-scale implementations, to build organizational learning and skills as they test AI solutions to optimize their effectiveness and avoid any large-scale mistakes. Additionally, leaders should seriously consider training and upskilling their sales team to effectively utilize AI.

Leaders should also seriously consider what their governance strategy will entail. According to PwC’s recent Trust Survey, nearly all business leaders say they are prioritizing AI-related initiatives, but only 35% of executives say their company will focus on improving the governance of these AI systems. Without strong and ethical guidelines, organizations risk unintended consequences, ethical dilemmas, and potential harm.

It is important to note that AI is not a one-size-fits-all solution. CEOs and business owners must tailor their AI initiatives to their unique business needs and industry dynamics. Leaders should embrace the transformative potential of AI, all while having a responsible-centric focus, and they’ll be well-positioned to accelerate their sales revenue in the evolving business landscape.

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Thank you, Bret! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]

• About Bret
• About PwC

About Bret

Bret Greenstein is PwC’s US Data, Analytics and AI Leader. He has worked for over 25 years helping clients to transform through the adoption of new technologies, including AI, Data, Internet of Things, and Cloud, to deliver new business models and new ways of working.

About PwC

At PwC, our purpose is to build trust in society and solve important problems. We’re a network of firms in 152 countries with over 327,000 people who are committed to delivering quality in assurance, advisory and tax services.

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The results are in, and the verdict is emphatic: Personalization works. And yet, though proven to drive revenue and customer loyalty to the point that one 2022 study found 86% of consumers appreciate personalized offers, many smaller retail businesses aren’t doing personalization as effectively as their industry-dominating counterparts.

It’s an anomaly that makes little sense. Even single-store, independent retailers possess large quantities of data, from customer transactions, browsing history, channel preferences, and mobile app usage to customer demographics. With such an abundance of data, any retail business should be able to deliver personalized messages to the right shoppers at the right time over the right channel.

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That’s the theory, anyway. Reality often has different ideas. In the case of retailers, the reality facing many is customer data that’s fragmented, inconsistent, and scattered across silos. The result is a lack of a unified, 360-degree view of data across all channels that’s so crucial for effective personalization.

Why Personalization Delivers Mixed Results

In the ongoing pursuit of personalization, retailers have tried various approaches.

One is for the IT team to stitch customer data together into a cohesive foundation. A complex endeavor, it is one that’s also labor-intensive, costly, and routinely drags on for many months where IT teams must hand-code integrations to connect applications and data.

Another tedious and resource-draining approach is for business-side staff to round up data into spreadsheets or databases. Yes, data can be fed into any number of personalization solutions, but if that data isn’t sound to start with, retailers invite the risk of off-target messaging that can damage brand equity.

To complicate matters even further, personalization projects are often isolated to a specific business unit, such as loyalty or marketing. Unless data is connected across all business units and to the full omni-channel environment customers use, from search to purchase to support, personalization results are going to be mixed at best.

Then there’s the fact that business applications change frequently. Indeed, it’s not uncommon for sizable retailers to have dozens of systems in place for e-commerce, point of sale (POS), loyalty, customer service, and merchandising, to name but a few. Whenever a retailer subsequently adopts a newer, improved system, whatever brittle integrations had been cobbled together previously must be rebuilt.

In fact, integration is the #1 obstacle that retailers face when implementing a new system, according to TotalRetail’s annual retail technology report for 2022.

The Ideal of Omni-channel Personalization

The increasingly sophisticated Integration Platform as a Service (iPaaS) market is emerging as a formidable panacea for these personalization challenges.

Leading iPaaS solutions are equipping retailers with intelligent connectivity and automation to break down stubborn data silos, accelerate business processes, and unlock the power of data — all critical elements for the most compelling personalization success stories.

For example, modern, rapidly deployed turnkey solutions designed to accelerate personalization allow retailers to personalize their customer journeys with a framework for data that is comprehensive, consistent, and timely.

Comprehensive. Retailers can achieve the ideal of omni-channel personalization by capturing customer interaction data from all touchpoints — not just a handful that may be necessary or specific to a function like eCommerce or physical stores.

Consistent. Data stored in disparate applications will always be inconsistent, with customer addresses and even names changing over time. High-order iPaaS data capabilities allow retailers to aggregate, cleanse, and enrich siloed data into uniform records that provide a single source of truth.

Timely. Personalization’s payback skyrockets when a retailer can do “marketing in the moment,” tempting customers with offers as they evaluate their options and close in on purchase decisions. Leading iPaaS solutions can feed near real-time activity data into personalization tools, triggering immediate outreach through email, digital ads, or text. The result is improved engagement, revenue, and customer loyalty.

The importance of a data-centric culture

As effective as iPaaS solutions are for driving personalization, their potential is hobbled where the retailer in question has not steeped the culture of their business in all things data.

With competition intensifying across the market, retail companies require deep insights into their data, which cannot be achieved by relying on either intuition or off-the-shelf software packages.

However, the adoption of a data-centric culture is not without challenges. Aside from legacy systems that are not compatible with advanced data analytics tools, there is typically some resistance from employees to change.

Here, plans for providing relevant workforce training becomes imperative. If expertise is lacking, companies can look to external partners who specialize in data analytics and the implementation of data-centric cultures.

Alternatively, retailers can look at their recruitment. Hiring data specialists can help improve the understanding of customer behavior and preferences, as well as identify the trends and patterns that lead to more informed decision-making. With data specialists on board, product offerings and marketing strategies can be more effectively tailored to meet the demands of customers.

Ultimately, it is this which leads to increased profitability and the prospect of growth.

Rethinking Retail’s Innovation Priorities

The retail industry has always maintained a strong focus on innovation. However, priorities have all too often centered around the latest and greatest front-end customer experience applications. Regrettably, this has come at the expense of back-end systems like integration platforms when the time comes for the C-suite to approve technology investments.

It’s a curious trend because the effectiveness of front-end applications depends heavily on their integration with other systems across the entire retail environment. Where these other systems are neglected, even the most impressive front-end applications have their potential muzzled.

Moreover, retail culture has often strayed from a focus on data with many viewing it as a less important than established cultures built around customer expectations of their product offerings. But, as we have come to learn, customer expectations are constantly evolving, and it is increasingly falling to departmental leads to make a business case for the tools that unlock the value of data for deeper personalization and for the people skilled in their usage.

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Hospitality Ventures Management Group (HVMG), an Atlanta-based, private hotel investment, ownership and management company, announced the appointment of Bryan Gatzemeyer as vice president of business intelligence. He will report to Cory Chambers, chief commercial officer. Chambers, along with a team of hotel industry and data science experts, has led HVMG’s efforts building the necessary infrastructure and data warehouse to activate a prescriptive business intelligence platform. Gatzemeyer will lead HVMG’s data and analytics strategy, and his appointment punctuates the company’s commitment to data-centric decision making using artificial intelligence and machine learning to drive performance.

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“I look forward to bringing my expertise in data science to HVMG in a way that will deliver value to our owners like no other organization in the industry.”

“Our business intelligence strategy is all about developing insights that will help our owners, corporate team leaders and general managers more effectively predict what is going to happen in their business, but also prescribe strategies that deliver the most profitable mix of business and drive bottom line performance,” said Robert Cole, president and CEO, HVMG. “Bryan’s experience developing and deploying prescriptive analytics makes him an excellent leader to spearhead this vital component of our business.”

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With a diverse background in hotel general management, revenue management and most recently data science, Gatzemeyer brings a unique mix of industry experience and analytics to HVMG. In prior leadership roles, he successfully developed comprehensive analytics platforms by utilizing forecasting, machine learning and data mining techniques to enhance owner profitability. His expertise centers on prescriptive analytics and the creation of holistic revenue optimization systems. Gatzemeyer has several certifications in revenue management specific to the hospitality industry, including CHRM (AHLEI), CHIA (AHLEI) and CRME (HSMAI). He graduated from the University of South Dakota with a bachelor’s degree in business administration.

“HVMG’s business intelligence strategic initiative and the culture of excellence they’ve fostered, are a perfect fit for me,” Gatzemeyer said. “I look forward to bringing my expertise in data science to HVMG in a way that will deliver value to our owners like no other organization in the industry.”

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 [To share your insights with us, please write to sghosh@martechseries.com] 

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Hi, Gregor. Welcome to our Interview Series. Please tell us a little bit about your journey and what inspired you to create Scoutbee.

I was a project engineer for a multinational medical device company when the Tōhoku earthquake and tsunami hit Japan in March 2011. We were working on developing a new MRI device design that would improve kids’ patient and clinician experience during pediatric MRI procedures. These scans can be very challenging for patients who are anxious or claustrophobic. Our new design would have made the scan very fast so the image would be more accurate, and we could alleviate children’s anxiety during the process. The tsunami caused catastrophic supply chain disruption that wiped out my team’s work on the project. Like many teams, we were unprepared for events like this. Frustrated, I knew something needed to change.

So, I went back to school to earn my MBA. After graduation, I founded Scoutbee in 2015 with a mission to help organisations build true supply chain resilience.

My team and I are dedicated to developing solutions that enable procurement teams to be proactive, see where there are gaps and exposures in their supply base, and find the best suppliers to fulfill their needs before disruption hits. Our technology helps organisations to architect a supply base that is truly resilient and pivots fast in the face of risk events, and also addresses other strategic priorities such as sustainability, diversity, innovation, and more.

The need for Scoutbee’s solutions was clear 12 years ago when the tsunami hit and has continued to grow ever since.

Tell us about the enterprise-level supply chain ecosystem that you are currently focusing on and how it changed in the last 3 years? How did the pandemic change the landscape?

The last three years have underscored how complex, interdependent, and fragile our global supply chains are and how susceptible they are to disruptive events. Procurement teams have long relied on unactionable and fragmented data, and that fundamental issue was brought to light and exacerbated by COVID’s supply chain impacts and the series of disruptions that followed. Companies that had unreliable data were forced to conduct ‘post mortems’ after each event happened, instead of solving the issue in the moment. Most procurement teams were stuck in a reactive cycle. Today, there’s a much greater understanding of the value of actionable supplier intelligence and ability to make proactive, fast, and confident decisions that drive resilience.

We support global procurement and supply chain teams across many industries, including automotive, CPG, and industrial manufacturing. We’re seeing companies increasingly reshore, near-shore, and “friend-shore” manufacturing operations to mitigate the product, material, and component shortages that crippled global commerce throughout the pandemic. Companies are also placing greater value on diversifying their supply base and focusing on sourcing from suppliers that carry low financial and operational risk. And they’re prioritizing suppliers that are reliable and can demonstrate commitment to environmental sustainability, human rights, social justice, and more.

ESG was a ‘nice-to-have’ a few years ago, but stakeholders are increasingly putting pressure on organisations to use their purchasing power to make a difference on the planet and society. The latest UN Intergovernmental Panel on Climate Change report indicates the global community will likely fail to limit global warming to 1.5 degrees Celsius, which will likely cause catastrophic and irreversible climate change. And an International Labour Organisation report states there are ten million more victims of modern slavery than there were pre-pandemic. Organisations are increasingly making ESG factors a core part of their daily procurement decisions to make a positive impact. There’s still a lot of opportunities to drive progress around ESG, and Scoutbee is committed to helping procurement teams find suppliers that will drive forward their businesses’ ESG priorities and profitability simultaneously.

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What are your core offerings? How do you incorporate various advanced technologies to create your products for large and mid-sized companies?

We strive to provide our customers with as much actionable information – intelligence – as possible so they can perfect their supply base and advance strategic initiatives, whether that’s ESG, risk management, innovation profitability, or something else. To that end, we’ve developed a SaaS suite – the Scoutbee Intelligence Platform (SIP) – that includes two solutions – Scoutbee Clarity and Scoutbee Discovery.

Procurement teams can use Scoutbee’s intelligence platform to get a holistic overview of their existing supply base, based on accurate, dynamic, and enriched supplier data. The solution sits on top of organisations’ procurement solutions and integrates, aggregates, enriches, normalizes, and visualizes the multiple data streams into one dashboard. Procurement teams get radical transparency into what their supplier base looks like today and where there’s room for improvement in terms of risk reduction, diversity, sustainability, supplier financial health, and more.

Once they see which steps, they need to take to strategically redistribute suppliers, they can use the platform’s advanced supplier discovery features to identify and onboard the best new and alternative suppliers for their needs in weeks instead of the typical months-long processes using traditional methods.

One of the biggest problems in procurement today is that the data teams rely on is fragmented. Procurement functions have invested in many solutions – ERP, ESG, procure-to-pay, risk monitoring and other tools – and there’s no harmonization of the exploding data from the systems.

Our data foundation – which is based on advanced knowledge graph technology – lies beneath both products, which enables teams to connect to and bring in any data point – internal, supplier, customer, third-party, Scoutbee data, and more – they need to make strategic and proactive sourcing decisions. Scoutbee connects the data to the knowledge graphs and then runs an algorithm on top to analyze the relationships between data points and ultimately help procurement draw the right conclusions so they can take the right actions and drive the best outcomes for the business.

What is the opportunity for organisations when it comes to utilizing AI and Machine learning in their operations?

AI is fairly pervasive in downstream supply chain operations in terms of inventory optimization and other use cases, but the main benefit is that it makes operational costs cheaper. That cost reduction is important, but there’s more strategic value and differentiation awaiting companies that leverage AI in upstream supply chain operations. This is where the opportunity is.

Artificial intelligence gives procurement teams the efficiencies and visibility they need to find the best suppliers, evaluate them, and make data-driven sourcing and procurement decisions at scale. AI can be a force multiplier for understaffed procurement teams that have to perform at the same level (or higher) with the same (or fewer) resources. Without AI-integrated solutions, it’s incredibly difficult for procurement teams to fulfill their mandates.

Procurement defines the competitiveness of the organisation – the profit it makes, the innovations it develops, and the resilience of the company. The more stable the supply chain is, the more competitive the company is. There’s tremendous opportunity in equipping your procurement team with AI and better data capabilities to help them find, evaluate and bring in the best suppliers faster. These supply partners drive efficiency, cost savings and strategic value for the organisation and play a direct role in the success of the broader enterprise.

AI is an invaluable tool, but we’re always going to need humans in the loop to validate AI and its outputs, and to take the insights that AI provides and use it for strategic planning, relationship building, and more. AI is an excellent sidekick on procurement’s journey.

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ChatGPT and Google’s Bard have taken the tech industry by storm. Could you let us know how generative AIs could transform intelligent automation?

The hype around large language models, such as ChatGPT, is very real and warranted. What makes these models so powerful is that they are proven to not just predict the next word or the completion of the sentence, but they can also grasp context quite well.

Generative AI has shown its full potential in general use cases. It answers general questions through the system very well. I expect that over the next few months, domain-specific expertise will be used to train algorithms and other large language models to get specific results for sub-domains. Generative AI models, for example, have already aced the Harvard medical exam with 99.99% accuracy. We’ll start to see very specific use cases for generative AI arise based on the success we’ve seen so far.

The reason the release of such a technology gains so much interest is the fact that organisations of various sizes can now use the large language models created by these organisations and fine-tune them for their specific domain at a fraction of the cost as opposed to retraining the large language model from scratch. This opens up possibilities for innovation and industry transformation.

Scoutbee has been working on large language models for the past two years now. With the advancements in large language models for generative AI, Scoutbee is researching how to use these models along with knowledge graphs to build the future of user experience for data products. Given the nature of the data and generative AI, we are running our version of the open-source models in our infrastructure to keep customer data safe and maintain our commitment to preserving data privacy and security.

What are your views on the future of supply chain optimization with ChatGPT and other AI techniques?

Typical supply chain optimization tools will remain. Optimization challenges are more statistical challenges, not language challenges. ChatGPT and generative AI will have the biggest impact in terms of helping teams connect the dots between certain market movements or market forecasts to supply chain operations. From that standpoint, generative AI and ChatGPT offer a lot of value. Large language models can help teams grasp the bigger picture.

Large language models, including Chat GPT and other generative AIs, can also add value to procurement teams by connecting the dots among extremely diverse and disconnected data across systems, thereby making it accessible in a meaningful way.

There are a ton of exciting use cases for large language models outside of procurement and supply chain that will get prioritized by other tech companies. It will be the responsibility of specialized companies, like Scoutbee, to bring this technology to our customers in the easiest way possible.

Do you foresee any challenges brands will deal with as a result of AI-based optimization and automation?

Data is the lifeblood of procurement: it must be centralized, refined and distilled into actionable intelligence in order to be valuable. Doing this in-house and without technology is incredibly difficult. And these kinds of data transformation projects can take years. Are business leaders willing to risk their companies’ futures on bad data or long-lead transformation projects?

Companies need AI-driven solutions to automatically and intelligently aggregate data from disparate sources – internal and external – and then categorize, cleanse, enrich, and convert it into intelligence. The technology then needs to connect the dots among the data points to draw conclusions and avoid potential risk scenarios. Only then can companies drive AI-based optimization that has a tangible and positive impact on the enterprise.

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Thank you, Gregor ! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]

• About Gregor
• About Scoutbee

About Gregor

Gregor Stühler is Co-founder and CEO of Scoutbee where he helps procurement teams make strategic and proactive decisions that strengthen supply chain resilience, improve sustainability, drive innovation, and reduce time to market.

About Scoutbee

Scoutbee drives better business outcomes by giving companies the actionable insights they need to perfect the supply base and advance strategic initiatives, such as risk management, ESG and innovation. The Scoutbee Intelligence Platform (SIP) uses graph technology and predictive and prescriptive analytics to deliver holistic supplier visibility that helps procurement make confident supplier decisions, drive cross-functional efficiency, and optimize their existing technology investments. Scoutbee’s AI-powered data foundation connects teams to any data point – internal, external, third-party, and more – and any data combination necessary to orchestrate a resilient, competitive, and sustainable supply base.

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Entrinsik, a leading global provider of data analytics and business intelligence solutions, announced the promotion of Madhavi W. Chandra to the position of Chief Product Officer (CPO). This strategic move demonstrates Entrinsik’s commitment to accelerating innovation and driving growth through cutting-edge product development. Chandra will work closely with CEO Brad Leupen, collaborating on company strategy and initiatives, and charting the course for its suite of products.

Chandra, a seasoned veteran in product management and technology leadership, has been with Entrinsik for almost ten years, most recently serving as Director of Product Management. As CPO, she will be responsible for overseeing the entire product portfolio and ensuring that Entrinsik continues to deliver innovative and market-leading solutions.

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“Madhavi’s deep understanding of our products, customers, and the market has been a driving force behind the success of Entrinsik’s product offerings,” said Brad Leupen, CEO of Entrinsik. “Her exceptional leadership and vision make her the ideal candidate to take on this critical role in shaping the future of our company, and I am looking forward to shaping the future of Entrinsik with her.”

During her tenure at Entrinsik, Chandra has been instrumental in the development and launch of key products, including the award-winning Informer 5, a self-service data discovery and analytics platform. She also started and led the highly successful Informer Advisory Council, which accelerates Informer innovation and development. She has consistently demonstrated her ability to identify customer needs and spearhead innovative solutions to address them.

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“I am truly honored and excited to be taking on this new role as Chief Product Officer,” said Chandra. “I am committed to driving innovation, strengthening our product portfolio, and delivering value to our customers. I look forward to working with the talented team at Entrinsik to continue to build upon our reputation as a market leader.”

Prior to joining Entrinsik, Chandra held leadership positions at several high-profile technology companies where she gained valuable experience in product management, software development, and business strategy. She holds a Bachelor of Engineering degree in Computer Engineering from The University of Miami, a Master of Computer Engineering, and a PhD in Computer Engineering from The Johns Hopkins University. She has also been an Adjunct Professor at North Carolina State University since 2013.

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[To share your insights with us, please write to sghosh@martechseries.com]

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