Information Week

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

Optimizing Data Center Efficiency: Harnessing Prescriptive and Predictive Power Management for Sustainable IT Infrastructure source

“Virtual Twins, Real Impact: How Integrated Simulation is Driving the Next Generation of Products“ Wednesday, October 30, 2024 at 1:00pm EDT In today’s connected world, more and more products require both mechanical and electromagnetic design.  Increased product complexity, higher performance demands, and the integration of these simulation domains are making the design cycle more involved.  Meanwhile, the market is demanding faster product launches and lower costs. Join us as we explore some of the design challenges companies are facing today, and how simulation is playing a key role in overcoming them.  We will discuss thermal analysis, structural analysis, electromagnetic analysis – and how connected workflows can link these different domains to make the analysis process faster and more accurate. Offered Free by: Dassault See All Resources from: Dassault source

COMMENTARY The role of the CPO — chief privacy officer — is at a crossroads. A rapidly growing number of data breaches, continually evolving regulations, and the increasing complexity of digital ecosystems have made a robust, privacy-first approach to managing data more critical for businesses than ever before. The role of a CPO was once clear-cut: Ensure compliance with privacy laws, manage data collection practices, and mitigate data risks. Now, CPOs are balancing more responsibilities than ever. Privacy has an impact on every realm of the business. So, is a CPO still a CPO, or is the role something greater? And, is it a role that just one person can handle?   The Expanding Scope of the CPO In a recent episode of my podcast, “The Privacy Insider,” Google’s outgoing chief privacy officer, Keith Enright, remarked that the data privacy role has expanded so much, it requires a jack of all trades. In many organizations, the CPO might manage privacy, but also aspects of security, data ethics, and even AI governance. Privacy does play a role in all these areas. But can a CPO — or chief information security officer (CISO), or chief data officer (CDO), or chief AI officer — wear all these hats and have them fit?  Whatever mix of letters that follows the C, many companies are striving for the same goal. They want a member of the C-suite whose mandate encompasses a broader responsibility: Be the steward of data governance, protection, compliance, and ethical use. That someone with any of the above backgrounds could be overseeing all of the above responsibilities shows how intertwined the technologies, data, and risks have become. Maybe that one job should be a more integrated team effort.   Guarding the Wall Together  For example, think about a data breach. Responsibility for preventing a data breach typically falls on the CISO. If a hacker pierces a company’s systems, that’s a security failure. But the reality of a rapidly changing threat landscape is that once you secure against one threat, another one is right behind it. For many companies, data breaches aren’t an “if,” but a “when.” How are you protecting what’s behind the wall? Good data privacy practices are good security. Are you identifying, safeguarding, and minimizing your most sensitive data? CISOs work hard on fortifying the wall, but if someone breaks through and there’s nothing to steal, you’ve contained the immediate damage, and also the reputational and regulatory damage that can follow. Protecting an organization on all sides calls for a tightly integrated strategy.   And Then There’s AI  The rise of AI presents some unique challenges: What are the ethical implications of AI? Can you trust it? What’s the recourse if sensitive data winds up in an AI model? Many companies turn to the CPO for guidance on the ethical use of these technologies, particularly around issues of consent, bias, and transparency. But AI governance is typically the domain of the CISO or the CDO, not the CPO. For now, no one person should own AI, because at this point in time, AI touches everything. Everyone shares the responsibility for using it wisely.  However, CPOs can play an important role in charting a path for AI, aside from ensuring companies use it in a privacy-forward way. The ethics of using sensitive data — and as we are seeing with the European Union AI Act, the consequences of misusing it — are similar whether the offender is human or machine. Clear insight on handling and protecting sensitive data and experience with General Data Protection Regulation (GDPR) readiness can help privacy pros guide the business in managing AI’s complexities.  The CPO as Partner Managing risk in a modern organization is the ultimate balancing act. Sometimes it’s all hands on deck to shore up cybersecurity, sometimes it’s sensitive data protection, sometimes it’s AI. Privacy, security, governance, and the rest are all critical to maintain the balance, no matter what the challenge is. There may be a CPO, there may not be a CPO. Privacy management might be centralized or distributed across the business. But that doesn’t change the importance of data privacy management in helping to shore up system security, define AI governance, build trust, and mitigate risk. The best role a CPO can play is in demonstrating the value of a strong privacy program to make the whole business stronger.  source

Businesses looking to increase their sales now have a powerful new tool at their disposal — AI.  AI revolutionizes sales by enhancing traditional selling methods and introducing new capabilities, says Bob Seaton, CTO advisor and solutions architect at technology consulting firm BUILT. “It builds upon established data science techniques, offering advanced customer segmentation, rapid industry insights, and streamlined training processes,” he explains in an online interview. “This approach allows companies to gain crucial information about their customers at unprecedented speeds, fostering more meaningful relationships and providing a competitive edge.”  AI’s core powers include creating natural content, handling Q&As, and automating action-based tasks, says Jared Coyle, chief AI officer with business applications provider SAP North America, in an email interview. “These capabilities open a wide range of opportunities for sales teams by simplifying various stages of the sales process.”  Meanwhile, generative AI (GenAI) is opening new frontiers in sales communication. “This technology enables micro-level customization of messaging, acting as a sophisticated personal assistant to clarify and tailor communications for specific audiences,” Seaton says. “AI not only refines existing sales strategies, but also introduces powerful new tools for personalization and efficiency, ultimately leading to more impactful customer interactions and improved sales outcomes.”  Related:Empowering Sales Leaders to Drive Success with AI In the early stages of selling, when greater personalization is required, AI can equip sales teams with valuable insights for more enhanced interactions, Coyle says. “By providing capabilities like intelligent customer profiles, sales teams can better understand customer purchasing patterns and preferences, as well as gain insight into where a customer is on their journey.”  “As sales teams move beyond the early stages of selling and into active customer interaction, AI can aid in contract management, content preparation, and the generation of custom visual designs tailored to specific target audiences,” Coyle notes. “Predictive analytics, powered by AI, can better forecast customer satisfaction and increase the likelihood of contract signings by enabling better resource allocation and insights.”  Data Matters  AI’s sales effectiveness hinges on its ability to harness vast amounts of data. “AI’s power lies in its speed and capacity to identify trends at an unprecedented scale,” Seaton says. It allows business leaders to recognize patterns hidden in their data, ask more insightful questions, and accelerate growth with targeted actions. AI’s data processing capabilities can also dramatically enhance product development, enabling rapid prototyping and testing. Such efficiency can compress months of testing into days, leading to faster solution creation and significant cost savings. “The key to AI’s utility isn’t just in having abundant data, but in knowing how to leverage it effectively to drive real insights and tangible business outcomes.”  Related:SAP’s Sophia Mendelsohn on Using AI to Scale Sustainability Opportunity and Risks  Businesses that embrace AI will replace companies that don’t, claims Pranav Gupta, a senior data scientist at home improvement retailer Lowe’s. In an email interview, he recommends getting started with AI as soon as possible. “The first step is to answer the question of what is the biggest opportunity area that could lead to a better customer experience or convince customers to buy your product.”  Sales teams that fall behind in AI use risk missing out on valuable data insights, leading to lost sales opportunities and revenue growth, Coyle says. Customer experience will also suffer, due to slower response times and less personalized services, impacting satisfaction and loyalty. Additionally, without AI’s predictive analytics, businesses may struggle with inaccurate forecasts, leading to poor decision-making and resource misallocation, he warns.  Related:How Intelligent Applications Can Boost Sales Not Just Another Tool  AI is not just another tool; it’s a universal accelerator rapidly that redefines every aspect of work, Seaton says. “By leveraging AI to quickly process vast amounts of data and generate insights, companies can respond to customer needs with unprecedented speed and precision,” he explains. “This acceleration allows businesses to present proposals before competitors, giving customers the opportunity to say ‘Yes’ earlier in the sales cycle.”  AI’s transformative power extends beyond enhancing existing processes, Seaton says. “Those who embrace AI as an accelerator will gain a significant competitive edge, while those who don’t risk falling behind,” he warns. “It’s clear that failing to adopt AI technology, especially in sales, means losing out on opportunities and efficiency gains that many competitors already leverage.”  An AI sales tool’s effectiveness can be evaluated by measuring increases in revenue and net promoter score, reduction in customer complaints, and related metrics that typically constitute key performance indicators, Gupta says. He suggests estimating such impacts early, even before an AI-based tool has been deployed. “For example, if you know the accuracy of a model recommending products to customers is 90%, you can make assumptions about the customer funnel and calculate company KPI from the value of the model accuracy.”  To obtain a clearer understanding of a sales tool’s overall effectiveness, Coyle advises conducting A/B tests of multiple AI initiatives to find the one that generates higher conversions and better customer engagements.  Faster Growth  Experts say that AI is the new electricity, Gupta observes. “Soon it will be unimaginable to have a product without some component of AI, just like it’s unimaginable to spend a single day at the office without a computer.”  source

Environmental, social, and governance (ESG) reporting is hitting the mainstream, as two big tech vendors this week have announced efforts to help enterprise organizations manage their efforts with regard to this major trend. ServiceNow has announced a new integrated ESG solution, powered by its Now digital workflow platform. The technology is designed to help companies work on strategies from enhancing diversity and inclusion to reducing carbon emissions to enabling business resilience, across the enterprise. ServiceNow has also announced the expansion of its alliance relationship with KPMG to deliver ESG-focused solutions and services. Separately, Google Cloud this week launched Carbon Footprint, a new product that gives customers a view of the gross carbon emissions associated with their Google Cloud Platform usage. The announcements come at a time when enterprises are working to improve their ESG initiatives and reporting. Investors, employees, and customers are looking at these efforts to help differentiate who they want to invest in, work for, and buy from in today’s market. Governments are also considering the creation of laws and regulations around ESG reporting and standards for public and private companies. For instance, the US House of Representatives passed an ESG reporting bill over the summer that would apply to public companies. Separately, the US Securities and Exchange Commission has indicated that ESG disclosure regulation will be a central focus of the new SEC Chair. Gartner reported that organizations are ranking regulatory risk related to ESG disclosures higher — it reached the second position on Gartner’s rankings in its Emerging Risks Monitor Report based on a survey of 153 senior executives in the second quarter of 2021. “The survey data partly reflects a global inflection point as ESG disclosures move from voluntary to required,” said Matt Shinkman, VP with the Gartner Risk and Audit Practice. It’s in this environment that Google and ServiceNow made their recent announcements, designed to help organizations with their reporting. “Sustainability is top of mind for every CEO and board,” Jen Bennett, Google’s lead for data and technology strategy for sustainability in the office of the CTO, told members of the media during a conference call. Carbon Footprint is available now to every Google Cloud Platform user for free in the Cloud Console, according to a blog post by Google’s Chris Talbott and Joel Conkling. The tool helps organizations measure, track, and report on the gross carbon emissions associated with the electricity of their cloud usage. “With growing requirements for Environmental Social and Governance reporting, companies are looking for was to show their employees, boards, and customers their progress against climate targets,” Talbott and Conkling wrote in the post. “Using Carbon Footprint, you have access to the gross energy related emissions data you need for internal carbon inventories and external carbon disclosures with one click.” Google said the tool was built in collaboration with customers including Atos, Etsy, L’Oreal, Salesforce, Thoughtworks, and Twitter. The calculation methodology is published to provide transparency to auditors and reporting teams. ServiceNow said its new ESG solution “serves as an operational control tower to help convert companies’ ESG goals into reality by providing visibility and transparency across their ESG programs and initiatives and helping them strategize, manage, govern, and report on these efforts on a single platform.” Components of the ServiceNow solution include ESG management and reporting, project and portfolio management, and integrated risk management tools. “In order for ESG initiatives to be effective, companies must have a complete view of their ESG efforts and performance to know how they are tracking towards their goals,” said Kim Knickle, research director at research and advisory firm Verdantix. “We expect ServiceNow’s new ESG solution will leverage the company’s existing platform to better enable this visibility.” What to Read Next: How CIOs Can Advance Company Sustainability Goals Should Sustainability Be an IT Priority? How to Build a Strong and Effective Data Retention Policy Corporate Diversity Efforts Get Real source