Information Week

The wide-scale introduction of artificial intelligence sent shockwaves through every industry, as it disrupted the way we live, work, and even learn. In the education sphere specifically, it’s caused traditional educators to experience a “Gutenberg printing press shock,” as much of their skills have essentially become obsolete overnight. AI’s quick rise has raised fear of risks such as plagiarism and lessened student engagement, causing many learning institutions to restrict or in some cases even ban the technology from classrooms. While we acknowledge and understand the potential risks associated with AI, I believe there is a lot more opportunity for the good of humanity than harm — if harnessed properly and responsibly, this groundbreaking technology has the potential to support and augment students’ learning exponentially — much like the printed book, the calculator, or the computer has done for previous generations. So, the question is not if we should harness AI, but how we should harness AI. It’s clear the technology needs guardrails. In fact, many groups from government officials and business leaders to celebrities like Tom Hanks have joined the debate on AI regulation. Yet, world leaders have been slow to act, and efforts have been restricted to national and regional spheres Related:The Blinking of ChatGPT Why the reluctance and the emphasis on local perspectives? Even during the peak of the Cold War, opposing factions aimed for international consensus, especially on ethical norms or ‘red lines’ related to nuclear weapon usage. Some theorize that this hesitancy toward AI regulation stems largely from their insufficient grasp of the technology and its ramifications. Why wouldn’t we engage the generation that seamlessly integrates AI into their daily routines? Undoubtedly, they not only have viewpoints on the matter but can also provide a more expansive and insightful perspective on the ethics of the technology. A proactive group of international students aged 13 to 18 from Institut auf dem Rosenberg decided to take the initiative and developed a 13-point charter to govern AI, calling for world leaders to promptly regulate AI development and usage through an international treaty and regulatory agency. A selection of the students’ proposed guardrails as a seed for global accord include: Control input and output. All organizations, whether private or state, engaged in designing, engineering, and/or distributing AI products, shall be held unequivocally accountable for the information generated by AI systems. These organizations must establish specialized departments amalgamating human oversight and automated technologies grounded in machine learning to guarantee the responsible utilization of AI. An external, impartial global agency shall meticulously oversee and ensure strict adherence to proper AI usage, conferring AI-Safe-Use approval badges exclusively upon organizations that diligently comply with AI standards. Transparent tracing of sources. Complete transparency in acknowledging the entities responsible for AI processes is imperative. Therefore, all AI-processed information must be transparently traceable to its origins, specifically attributing it to the entities conducting the information processing using AI. Users shall enjoy unrestricted access to all original input data employed by AI systems. Violations of source tracing obligations will be met with resolute legal enforcement. Regulation of deepfakes. Mandatory watermarks or detectable patterns are recommended for all deepfake or artificially created content. We advocate increased investment in deepfake detection technologies. Unethical deepfake actions, including defamation and identity theft, must be unequivocally prosecutable offenses. AI systems shall rigorously maintain accessible interaction histories, with AI software manufacturers being legally accountable for verifying the origin of disseminated information. Prevention of monopolies and duopolies. In the pursuit of equitable AI development and access, signatory parties solemnly pledge to actively champion diversity and counteract monopolies, duopolies, or oligopolies within the AI creative sphere. This commitment aims to foster innovation, fairness, and global collaboration. Support for cultural and academic endeavors. AI programs must be designed exclusively to support cultural and academic creators, refraining from autonomous generation of cultural and academic content. The excerpts provided are just a glimpse into the thorough work of our students. The question about ethics in AI is one that has the potential to bring together a polarized world for the greater good of all mankind — it is an opportunity that we should give the next generation. For a detailed insight into the Rosenberg AI Charter and this significant project, please visit here. source

While hype over artificial intelligence may be spurring organizations to hire professionals with matching skills to maintain a competitive edge, many businesses have more fundamental IT talent gaps.    An April survey of 1,400 executives and IT professionals found skills gaps throughout cybersecurity, cloud, and software development — along with interest in skill development for these areas for 2025.   In fact, understanding tech skills gaps is something many organizations struggle with — just a third of executives surveyed said they completely understand their organization’s skills gaps, and 68% of technologists say that business leaders aren’t aware of their IT skills gaps.   Chris Herbert, chief content officer at Pluralsight, says to combat this lack of knowledge, business leaders need a data-driven approach to uncovering skills gaps.  “This can be in the form of tech skills assessments, which can benchmark where technologists fall on a sliding scale of expertise in a given tech skill,” he says.  He adds that it can be useful to survey tech teams internally in areas where they feel they need to deepen their skills. Creating a culture of learning always starts at the executive level, he says.  “Business leaders need to be vigilant about the areas where their tech teams are falling behind and set up systems and initiatives that will help enable direct managers to assess their team’s skills on a consistent basis,” Herbert says.   Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI Anant Adya, executive vice president and head of Americas delivery at Infosys, says that businesses are moving away from hiring or training workers based on expertise in a single technology and towards cultivating talent proficiency across many disciplines.  “Building diverse talent pipelines and offering opportunities to build both hard technical skills and soft communication skills are effective strategies,” he says.  Adya adds there is great value in investing in “data readiness” and fostering a culture of responsible experimentation as part of upskilling.  Continued Demand for IT Pros   According to CompTIA’s State of the Tech Workforce 2024 report, tech occupation employment over the next decade is expected to grow at about twice the rate of overall employment across the economy.  Projected growth rates for several tech occupations are well above the national rate, most notably for data scientists and data analysts and cybersecurity analysts and engineers.   Tim Herbert, chief research officer at CompTIA, explains AI is “undoubtedly” the wildcard factor on the minds of many employers and workers.  Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness “While some of the AI hype has moderated, there continues to be plenty of experimentation and anticipation for what comes next,” he says via email.   According to CompTIA analysis of Lightcast data, AI job postings account for 10-12% of all tech job postings in recent months.   “Every industry is hiring technology professionals,” he says. “There really aren’t high-tech or low-tech industries anymore”  He lists infrastructure, software, cybersecurity, and data as the four big buckets, with help desk and support in another category.  “Increasingly, the problems become less technology problems and more related to the industry — compliance and privacy, for example,” he says. “You have to know the drivers and priorities within the industry.”  He notes that there are also more and more jobs that require technology professionals to interact with other teams and to understand more about the business and the problems they are trying to solve.   Power of Programmatic Upskilling  The Pluralsight survey indicated upskilling employees is proving to be more cost-effective and timelier than hiring new talent.  While hiring can cost over $23,000 and take up to 10 weeks, upskilling costs around $5,000 per employee and can be implemented faster.  Related:The Impact of AI Skills on Hiring and Career Advancement Despite these advantages, time constraints remain a major barrier to successful upskilling programs, as reported by organizations over the past three years.  Pluralsight’s Herbert says it is crucial to make upskilling within the organization “programmatic,” which involves mapping your technologists’ skill-building journey to business needs.  “This is where developing a culture of learning comes in,” he says. “If upskilling is integrated as a core business competency, tech teams will have the nimbleness needed to switch focus from one skill area to the next as business needs ebb and flow.”  Steve Watt, CIO at Hyland, says that CIOs should also provide robust upskilling opportunities to show IT professionals they’re dedicated to their growth and career interests.  “The job market for IT professionals is being swayed by the demands of AI but security and cloud professionals have invaluable skills that are still very much in need,” he explains.  By offering opportunities for IT professionals to sharpen their core skills in their roles, companies are helping strengthen their own business while showing talent they’re committed to their long-term success and interests.  Adya says that training must balance skills required for basic infrastructure with those needed for swiftly emerging technologies.   “For cloud in particular, programs should be self-paced, in collaboration with academic institutions, specialized for local hiring, and grounded in digital reskilling,” he says.  The program should additionally incorporate hands-on experiences and input from cross-functional teams.  “Companies should additionally create incentives, necessary infrastructure and support to properly add employees to the process,” he says.   Top IT Talent Desire Flexibility   Watt says remote work flexibility continues to be a key differentiator as well.  “Because IT and security are ubiquitous across every industry and skills transfer almost regardless of vertical markets, these workers have a lot of options when picking a company or industry to work in,” he says.  Being inflexible — especially with IT and security staff when it comes to remote work — can significantly hinder the ability to attract and retain top talent.  “I recently spoke with another CIO who had commented that after a rollout of a mandatory three-day-in-office policy they lost 20% of their IT staff in about 4 months,” he says. “They rolled that policy back specifically for IT very quickly.”  source

“Digital Resilience: Merging IT Growth with Environmental Responsibility“ Building Resilient IT Infrastructures for Sustainable Digital Growth The white paper titled “Digital Resilience and IT Growth” by Chatsworth Products explores the critical aspects of building resilient IT infrastructures in the face of rapid digital transformation. It delves into strategies for enhancing data center reliability, scalability, and efficiency to support growing digital demands. The paper emphasizes the importance of integrating advanced technologies and best practices to mitigate risks, improve operational continuity, and ensure robust performance. By adopting these strategies, organizations can effectively manage IT growth, protect vital data, and maintain a competitive edge in an increasingly digital landscape. Download this whitepaper to learn more.  Offered Free by: Chatsworth Products, Inc. See All Resources from: Chatsworth Products, Inc. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. source

Over the last 15 years, the tech professional community has identified a small number of core cybersecurity capabilities that offer a high payoff: preventing known cyberattacks while supporting compliance requirements. Are these 8 core capabilities in your cybersecurity toolkit? source

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ORLANDO, Fla. — During their opening keynote Monday at the Gartner IT Symposium/Xpo 2024, analysts Mary Mesaglio and Hung LeHong described the key ingredients to building a successful AI stack and how businesses and organizations should pace themselves. “Because of the relentless innovation happening in the tech vendor race, CIOs feel like they are always living the hype, which the reality of their AI outcomes race — how tough it is to get value — makes it feel like they are also in the trough,” Mesaglio said. The conference was expected to attract more than 8,000 CIOs and senior IT leaders. With an arms race to adopt AI and GenAI strategies, the analysts tried to add some clarity for business leaders who may have varying degrees of need. They also talked about the different races going on to adopt GenAI. It’s important to understand, they said, that the vendor race happening should be separated from their own race to implement AI technologies. CIOs are bearing most of the burden of rapid AI innovation expectations: A Gartner survey showed 57% of CIOs were tasked with creating an AI strategy. “And even with all this GenAI fatigue of the last year, you’re still under pressure from the CEOs to execute,” Mesaglio said. That pressure can cause leaders to lose sight of the AI needs for their specific business and outcome needs. Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI AI Steady vs. AI Accelerated LeHong added, “However, CIOs can set the pace in their outcomes race. If you have modest AI ambitions, in an industry that isn’t being remastered by AI yet, you can afford to go at a more measured pace. This is an AI-steady pace. For those organizations with bigger AI ambitions, or in an industry that’s being reinvented by AI, the pace will be faster. This is an AI-accelerated pace.” No matter which paths a business chooses, the goal should be the same: delivering value and outcomes, LeHong said. But generating business value has been difficult for many businesses. A 2024 Gartner survey of over 5,000 digital workers in the US, UK, India, Australia and China found employees said they saved an average of 3.6 hours per week by using GenAI. While those savings can help cut costs, the gains vary from business to business. “Here’s the real challenge with AI productivity,” said LeHong. “Productivity gains from GenAI are not equally distributed. Gains vary by employee, not just because of their personal interest and levels of adoptions, but according to complexity of job and level of experience.” Hung LeHong (photo by Shane Snider) Building an AI ‘Sandwich’ The analysts shared a visualization of a successful AI strategy or “stack” that looked like a sandwich, with structured and unstructured data and all the types of AI used making up the top and bottom slices of bread. The middle of the sandwich consists of an organization’s trust, risk, and security management (TRiSM) technologies that create security. Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness “As CIO, your job is to design a tech sandwich that can handle the messiness of AI, but still keeps you open to new opportunities,” said Mesaglio. “AI-steady organizations (10 AI initiatives or fewer) will govern their tech sandwiches using human teams and committees. AI-accelerated organizations will add TRiSM technologies — a set of technologies designed to create trust, monitor risk, and manage security for safe AI at scale.” Being Mindful of AI’s Human Impact The analysts noted that employees’ feelings about AI can range from positive to negative — with some employees feeling threatened or resentful. Those negative feelings can impact work performance. In a Gartner survey, only 20% of CIOs said they are being proactive about protecting the employee’s well-being when it comes to the potential negative impacts of GenAI. “Most enterprises aren’t curious enough about how AI makes their employees feel. This matters because AI can lead to all sorts of unintended behavioral outcomes,” said Mesaglio. “The critical point is that if you use change management to manage this, be intentional about who owns which behavioral outcomes. Organizations must manage behavioral outcomes with the same rigor as technology and business outcomes.” Related:The Impact of AI Skills on Hiring and Career Advancement source

Orlando, Fla. — Wearing his trademark black leather jacket, Nvidia CEO Jensen Huang on Tuesday delivered a highly anticipated keynote at Gartner’s IT Symposium/Xpo — where he talked about a range of leadership topics. Nvidia has experienced meteoric success with its graphics processing units (GPUs). Once thought of mainly as a processor to handle graphics intense workloads, like video games, it turned out that the high-performance units were also efficient tools for large language models (LLMs). The near overnight success of Open AI’s ChatGPT after launching two years ago has created an arms race for companies to build GenAI platforms. Nvidia has profited well from that race, launching it to the top of the world’s most valuable companies. So CIOs were eager to hear from Huang about finding similar success. Hundreds of attendees lined up more than an hour before the doors to Huang’s keynote started opening. Huang sat for an interview with Daryl Plummer, a Gartner analyst and vice president. “Nvidia showed us a different path, from graphics chips to data centers to large scale generative AI, they released computing power that hits AI, the game, then world changing phenomenon that it is today,” Plummer said before Huang came onto the stage. Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI Fielding a question from Plummer about his personal style — which consists of the same publicly worn all-black attire — and if that simplicity leaves room for his leadership vision, Huang said his leadership has more to do with leaning into the future than focusing on style. “When you see something impactful, something surprising and unexpected, you’ve got to ask yourself, ‘What does this mean and what’s the impact long term?’ … Now the next part is that if you deeply believe something, are going to do something about it. The best technique is to get started.” Living in the Future and ‘Tokenization’ Huang said CIOs should embrace a future-forward mentality that allows them to embrace a quickly changing technology landscape. “It’s easier to live in the future than it is to live in the past,” he said to applause. “Living in the past is more painful.” Future thinking is “hopes and it’s dreams, it’s belief… the question is, once you manifest that future in your mind, are you going to go do something about it?” Nvidia certainly did something about it. The company’s quick transformation into a critical supplier of AI-enabling processing units has paid off. In its most recent financial report, the company reported $30 billion in revenue in the second quarter of 2024, marking a 15% increase from the previous quarter and a 122% increase year-over-year. Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness He talked about how the industry has changed very rapidly, from one focused on hardware and software, to one focused on invisible ‘tokens’ that could translate visual and linguistic data into usable commodities. “This industry never existed before, and this industry is going to have factories — these buildings with computers inside — and these computers are incredibly good at transforming the raw material, which is data, into this new invisible thing that is monetized by millions of tokens per hour… floating point numbers that could be reconstituted into language, reconstituted into images and videos.” Eventually, Huang said, “we’ll tokenize robotic articulation, we’ll be able to tokenize proteins and chemicals… What we are witnessing … this is a beginning of a new industrial revolution.” Digital Workers vs. Human Workers While many have cited concerns about the rapid development of artificial intelligence possibly replacing a large amount of the workforce, Huang offers a more optimistic vision. Digital workers, working alongside human workers will increase productivity and create more opportunities for everyone. He said agentic AI will have human employees interacting with a digital workforce that’s not necessarily there to replace them, but to enhance productivity and growth for the whole company. Related:The Impact of AI Skills on Hiring and Career Advancement “And all of these digital employees … I’m prompting them in the same way I’m prompting biological employees. They’re going to find each other, they’re going to work together as teams, and we’re going to give them issues they can accomplish together.” He added, “We need to create more AI jobs first so we can create more human jobs… If you created more AI jobs right now, you will be a more productive company. You would generate more earnings, which will allow you to hire more people. source

Few technologies have captured the public imagination quite like generative AI. It seems that with every passing day, there are new AI-based chatbots, extensions, and apps being released to eager users around the world.  According to a recent Gartner survey of IT leaders, 55% of organizations are either piloting or in production mode with generative AI. That’s an impressive metric by any degree, least of all considering that the phrase ‘generative AI’ was barely part of our collective lexicon just 12 months ago.   However, despite this technology’s promise to accelerate the productivity and efficiency of its workforce, it’s also left a minefield of potential risks and liabilities in its wake. An August survey by Blackberry found that 75% of organizations worldwide were considering or implementing bans on ChatGPT and other generative AI applications in the workplace, with the vast majority of those (67%) citing the risk to data security and privacy.  Such data security issues arise because user input and interactions are the fuel that public AI platforms rely on for continuous learning and improvement. Consequently, if a user shares confidential company data with a chatbot (think: product roadmaps or customer information), that information then becomes integrated into its training model, which the chatbot might then reveal to subsequent users. Of course, this challenge isn’t limited to public AI platforms, as even a company’s internal LLM trained on its own proprietary datasets might inadvertently make sensitive information accessible to employees who are not authorized to view it.  Related:Bridge the Gap Between Business Leaders and Tech Teams To better evaluate and mitigate these risks, most enterprises who have begun to test the generative AI waters have primarily leaned on two senior roles for implementation: the CISO, who is ultimately responsible for securing the company’s sensitive data; and the general counsel, who oversees an organization’s governance, risk, and compliance function. However, as organizations begin to train AI models on their own data, they’d be remiss to not include another essential role in their strategic deliberations: the CTO.  Data Security and the CTO   While the role of the CTO will vary widely depending on the organization they serve, almost every CTO is responsible for building the technology stack and defining the policies that dictate how that technology infrastructure is best utilized. Given this, the CTO has a unique vantage point from which to assess how such AI initiatives might best align with their strategic objectives.  Related:How to Submit a Column to InformationWeek Their strategic insights become all the more important as more organizations, who might be hesitant to go all-in on public AI projects, instead opt to invest in developing their own AI models trained on their own data. Indeed, one of the major announcements at OpenAI’s recent DevDay conference focused on the release of Custom Models, a tailored version of its flagship ChatGPT service that can be trained specifically on a company’s proprietary data sets. Naturally, other LLMs are likely to follow suit given the pervasive uncertainty around data security.    However, just because you choose to develop internally does not mean you’ve thwarted all AI risks. For example, consider one of the most valuable crown jewels of today’s digital enterprise: source code. As organizations increasingly integrate generative AI into their operations, they face new and complex risks related to source code management. In the process of training these AI models, organizations are often using customer data as a part of the training sets and storing it in source code repositories.   This intermingling of sensitive customer data with source code presents a number of challenges. Whereas customer data is typically managed within secured databases, with generative AI models, this sensitive information can become embedded into the model’s algorithms and outputs. This creates a scenario where the AI model itself becomes a repository of sensitive data, blurring the traditional boundaries between data storage and application logic. With less-defined boundaries, sensitive data can quickly sprawl across multiple devices and platforms within the organization, significantly increasing the risk of being either inadvertently compromised by external parties, or in some cases, by malicious insiders.   Related:How Many C-Levels Does It Take to Securely Manage Regulated Data? So, how do you take something that is as technical and as abstract as an AI model and tame it into something suitable for users — all without putting your most sensitive data at risk?   3 Ways the CTO Can Help Strike the Balance  Every enterprise CTO understands the principle of trade-offs. If a business unit owner demands faster performance for a particular application, then resources or budget might need to be diverted from other initiatives. Given their top-down view of the IT environment and how it interacts with third-party cloud services, the CTO is in a unique position to define an AI strategy that keeps data security top of mind. Consider the following three ways the CTO can collaborate with other key stakeholders and strike the right balance:  1. Educate before you eradicate: Given the many security and regulatory risks of exposing data via generative AI, it’s only natural that so many organizations might reflexively ban their usage in the short term. However, such a myopic mindset can hinder innovation in the long run. The CTO can help ensure that the organization’s acceptable use policy clearly outlines the appropriate and inappropriate uses of generative AI technologies, detailing the specific scenarios in which generative AI can be utilized while emphasizing data security and compliance standards.  2. Isolate and secure source code repositories: The moment intellectual property is introduced to an AI model, the task of filtering it out becomes exponentially more difficult. It’s the CTO’s responsibility to ensure that access to source code repositories is tightly controlled and monitored. This includes establishing roles and permissions to limit who can access, modify, or distribute the code. By enforcing strict access controls, the CTO can minimize the risk of unauthorized access or leaks of sensitive data as well as establish processes that require code to be reviewed and approved before being merged

“State of ITSM in Financial Services“ An InformationWeek Report | Sponsored by TeamDynamix Data from InformationWeek’s State of ITSM in Financial Services Report shows that there’s a wide range of maturity in how ITSM teams are dealing with the unique challenges of supporting technology stacks in today’s financial vertical. While application portfolios grow and tickets mount, ITSM teams remain fairly lean. But they’re not necessarily running efficiently, as they’re forced to cope with legacy ITSM platforms, a low level of automation, and inefficient project management capabilities. Key Findings: 40% of FS ITSM teams support 100 or more applications13% of these ITSM teams service 400 or more applications58% of FS firms manage more than 500 tickets per month40% of FS IT teams struggle with low ITSM maturity43% of FS IT Service Desks identify manual processing as top issue Download this report to see how you compare. Offered Free by: TeamDynamix See All Resources from: TeamDynamix Recommended for Professionals Like You: source

Retail is a highly competitive business that increasingly uses technology to improve customer satisfaction and efficiency of internal processes. Robots, AI, data analytics, and more are enabling retailers to work smarter and faster, but their efforts aren’t perfect yet as evidenced by the amount of clearance and destroyed inventory that previous solutions were supposed to help avoid.   “Emerging technologies like AI-powered customer support, automation, and real-time data analytics have become game changers for retailers,” says Tim Peters, CMO at omnichannel customer experience solutions provider Enghouse Systems.   There’s also a positive impact on customer experience and loyalty when tech makes things run smoother and faster.  “As customers increasingly expect personalized experiences, emerging technologies like AI and machine learning allow retailers to predict customer preferences and deliver personalized recommendations,” Peters says. “A study by Salesforce found that 60% of consumers expect personalized experiences based on their past behavior. These technologies help retailers anticipate customer needs and improve loyalty by making the shopping experience more engaging and intuitive.”  But, of course, there are challenges. One of the main ones is integrating new technologies with existing systems.   Related:How AI is Reshaping Retail “While emerging tech offers significant advantages, the costs of integration and ensuring compatibility with legacy systems can be complex,” Peters says. “Additionally, scalability remains a challenge as the needs of retailers evolve with customer expectations.”  Another challenge arises when tech adds friction to customer experiences.  “While technology can streamline operations, an overreliance on automation without human touch can sometimes backfire,” Peters says. “Consumers still value human interaction, especially in complex support scenarios. It’s crucial for retailers to balance automation with human agents, particularly in areas that require empathy and nuanced decision-making.”  Competitive Advantages for All  Companies of all sizes benefit from greater organizational efficiency, and tech has been the fuel powering digital transformation. For example, Lowes uses AR for home improvement shopping while Sephora uses it for virtual make up try-ons. Walmart is stepping up automation in its battle against Amazon.  But smaller retailers are benefiting, too. For example, western footwear, apparel, and accessories brand Tecovas uses AI to improve their SMS targeting strategy and drive more revenue. Using AI tools from AI-powered SMS and email marketing platform Attentive, the company has been able to convert more site visitors, improving incremental revenue and realizing a 14X ROI improvement for the brand.  Related:5 Key Ways AI and ML Can Transform Retail Business Operations “I am a one-person CRM team, so anytime I can shift some of my resources to AI and ML, it is a win for me. For instance, building the correct segments is time-consuming and [prone] to error. Audience AI, a feature of AI Pro, takes the guesswork out of identifying and communicating with our customers how they want to receive messages and delivers the results we are looking for,” says Megan Edwards, senior manager, CRM at Tecovas, in an email interview. “We expect this to be a key success driver with the upcoming holiday season to cut through the noise by creating messages based on [the] individual rather than batch and blast tactics. We will be weaving AI into our holiday marketing strategy heavily this year, and we are looking forward to the results on the SMS channel.”  As with many small retail businesses, marketing tends to involve a lot of trial and error which translates to time and costs.   “We’ve been extremely pleased with the uptick in our site visits and incremental revenue since activating AI, and we didn’t have to spend a lengthy amount of time determining what raised our numbers,” Edwards says.  Related:’Tis the Season for Autonomous Retail KPMG considers GenAI a “game changer” in the consumer and retail sector. According to the company’s recent report, GenAI can drive commercial effectiveness, operational efficiency and cost optimization. It can also be used for price optimization, better ad targeting, more engaging product descriptions and a more personalized customer experience.  Accelerating Fulfillment and Delivery With AI  When Matt Naslund, vice president and head of solutions at Mytra worked for personal styling service provider Stitch Fix, one of the things they were able to deploy fastest were autonomous mobile robots (AMRs), though design time to deployment took two years and the robots could only operate on the ground floor. At large-scale automation solution provider Mytra, he and others at the company are accelerating deployment in distribution centers and warehouses with modular “cells” the size of pallets that can be automatically configured and moved. This accelerates fulfillment and delivery and addresses the problem of demand peaks and valleys.  “One of our customer’s last large-scale automation took them five years from the time they started the concept to deployment,” Naslund says. “For context, the pandemic, was four and a half years, and the amount of volatility that the supply chain saw over the four years was insane. We saw inventory gluts, inventory shortages, and panic buying. Then you saw a warehouse shortage capacity, everybody’s panicking to get warehouses. Then, they suddenly have too much space.”  The pandemic years were arguably the worst time for a long-term rollout, given the constant and dramatic changes happening at the time, from lockdowns to supply chain chaos.   Stitch Fix’s use of AMRs allowed the company to improve operational efficiency and expand into new markets with clothes for men and children, in addition to its traditional audience — women. Stitch Fix also launched in the UK.  “All of a sudden, our footprint, our landscape, our ordering, our inventory, everything looked completely different,” Naslund says.   The biggest benefits of automation are the ability to get products out the door faster, labor savings, real estate savings and higher accuracy levels.   Inventory management is still an issue, and there’s a demand for real-time visibility to minimize overstocking and understocking. A similar concept could be applied for workers so when there’s a spike in orders, workers can be dispatched quickly, such as to pick items to be shipped.  “If we start to have advanced learning and understanding, and then we start