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With concerns about and regulation of artificial intelligence on the rise, it can be easy to focus on questionable practices associated with the recent boom in AI technology. There can be ways to further AI’s use that include best practices, without putting privacy at risk or stirring other fears. Lawmakers at the state, national, and international levels continue to draft policy meant to ensure public safety, protect privacy and ownership of original content, fight misinformation, and a plethora of other concerns AI now raises. But does AI have to be “the bad guy” to be innovative and useful? How much more scrutiny is on the way for AI? Are there uses for AI that do not raise concerns of risk to the public, disruption of society, or harm to creatives behind original content? In this episode of DOS Won’t Hunt, Octavian Udrea, chief scientist with Code Metal; Kjell Carlsson, head of AI strategy with Domino Data Lab; and Sohrob Kazerounian, distinguished AI researcher with Vectra AI, share their perspectives on AI development and use with ethics and regard for the public good in mind. Listen to the full podcast here. source

Generative AI is one of the hottest trends of the century. And also, the most disappointing trend for many companies.   “Productizing GenAI has proven to be incredibly illusive. Commercials for AI services insist you must adopt AI in your business processes in order to remain competitive. Meanwhile, the difficulties in actually productizing GenAI are now starting to have tangible effects on financial markets, as tech companies struggle to justify their capital expenditures on AI hardware and infrastructure. So, what gives?” says Sohrob Kazerounian, distinguished AI researcher at Vectra AI.  Some blame exaggerated hype for GenAI’s failings.   “It was inevitable that generative AI would eventually dip down into the trough of disillusionment — no technology can fulfill that level of hype,” says Saurabh Abhyankar, EVP and chief product officer at MicroStrategy.  Others think the soar-to-crash cycle is common to new technologies where vendors over-promise and customer expectations are not pinned to reality.  “The disillusionment has occurred because many enterprises had inflated expectations from many of the great demos and hype. They thought these models would be as accurate as a spreadsheet or database and that they would be able to incorporate their own data easily. Many enterprises didn’t actually define success criteria for GenAI; enterprises report that 90% of proof of concept projects never make it into production,” says Naveen Rao, vice president of AI at Databricks.  Related:Do’s and Don’ts of GenAI Spend But perhaps more to the point is the fact that far too many organizations have found far too few use cases that GenAI can do.  “GenAI is not a technological Swiss army knife, so to see value, enterprises must focus on what it does well, which is understanding and generating natural language within context,” Abhyankar says.  Even so, most companies stumble on being far too deep in the investment well to stop drinking from it now.   “Firstly, let’s remember that transformative technologies often stumble before they sprint, like you say. Consider the early days of the internet or even electricity — initially dismissed, then indispensable,” says Lars Nyman, the chief marketing officer of CUDO Compute, a platform that supplies the computational horsepower behind many of today’s breakthrough AI programs.  The good news is that GenAI is not a complete loss — and likely not a loss at all — if a few measures are now put in place. Here’s what needs to change to turn the situation around:  1. The chase for more models and features stops  Related:Will Generative AI Replace Developers? Losing interest in more models and features sounds counterintuitive but it actually isn’t. Organizations need time to learn the models and features that exist now to see clearly how they can be leveraged. The rush to constantly upgrade before you get your bearings borders on insanity from a purely business, capture- the-ROI perspective.  “There is an obsession over the glamour of data models and the instinct to constantly fine-tune ML algorithms to chase that elusive ‘perfect’ model for GenAI apps. But here’s the thing: This model-centric approach is fundamentally flawed because it causes data teams to overlook the foundation of effective ML — the data itself,” warns Kevin Hu, the co-founder and CEO of Metaplane and an MIT PhD researcher in machine learning and data visualization.  Get your data right first. Know what you have and define what you need. Then pick a model and features accordingly. In most cases, you’ll find you need more than one AI model so that the combined capabilities perform to your expectations or goals.  2. GenAI breaks its mold  GenAI tools are marvelous. They make so much that’s pretty and so little that’s practical. And one big reason this happens is that despite the heft of the data and the might of the model, GenAI applications largely function as isolated standalones.    Related:The Rise of Autonomous AI Agents “Although GenAI as a standalone application has been intriguing, for it to take hold in the shorter term, the odds are good that it will happen due to the network effect of existing mega platforms: Google, Meta, Amazon, Apple, and the like,” says Eli Goodman, CEO and co-founder of Datos, a global clickstream data provider and a Semrush Company.  “GenAI’s real hook is its integration as a value-enhancing complement to existing everyday products and services. Whether it be Google AI-assisted search, Amazon shopping assistant Rufus, or Apple’s newly launched AI features in the iPhone 16, successful adoption within these already massive consumer systems is where GenAI will transform from exception to rule,” Goodman adds.   GenAI is also expected to become both the UI and the conductor in multi-agent architectures.   3. GenAI shows its true age  GenAI is not new. It just seems like it is because of how OpenAI’s ChatGPT made such an unexpected splash in the market and how fast everyone then jumped on it and other applications. Certainly, OpenAI created new models and ChatGPT was and is an impressive model, and other vendors and open-source projects have followed suit with their own creations. Even so, this AI category is established tech for the most part.    But in all that time, little progress has been made in establishing what to do with it.   “Many companies treat GenAI as a science fair experiment,” says Scott Gnau, vice president of data platforms at InterSystems, a provider of AI-enabled data management solutions for healthcare, financial services, and supply chain and logistics. “Doing it for the sake of doing it because everyone else is and they don’t want to be left behind. They never break out of the theoretical into the practical. Simply deploying GenAI without a clear strategy or measurable objectives will only lead to more roadblocks and further disillusionment.”  Once everyone stops gawking at GenAI like it’s a new toy and sees it as a fairly mature tech among other even more mature forms of AI, then organizations can begin to match capabilities and use cases on a far more sensible basis.   4. Companies begin to routinely break down their strategies and tasks 

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“State of Accounting and Legal Services“ An InformationWeek Report | Sponsored by TeamDynamix Accounting and legal services rely heavily on high-performing IT systems to ensure business efficiency. When technical issues occur—such as unusable endpoints, connectivity failures, or critical application outages—productivity halts. Key Challenges: -High Expectations: ITSM teams must uphold stringent SLAs and deliver exceptional service to meet the demands of accounting and legal professionals.-Compliance and Security: These teams need to implement compliance-ready procedures and ensure top-notch security due to the sensitive nature of financial and legal data.-Varying ITSM Maturity: Survey insights reveal that while some firms exhibit strong ITSM maturity, others face challenges in basic service delivery.-Lean Teams: With lean IT teams, many organizations must achieve more with fewer resources. Explore how ITSM solutions can help your accounting or legal firm enhance service delivery, maintain compliance, and drive profitability. Download our full report for detailed insights. Offered Free by: TeamDynamix See All Resources from: TeamDynamix 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. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. source

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“ESM | Going Beyond IT – Service Management for HR, Facilities and More“ Brought to you by TeamDynamix Embrace a unified approach for managing all your organization’s transactional requests with Enterprise Service Management (ESM) software. Originating from IT Service Management (ITSM), ESM extends its capabilities beyond IT, transforming processes across departments such as HR, marketing, facilities, and legal.Discover the evolution and benefits of ESM:-Centralized Platform: Streamline operations by using a single platform across various departments to handle all service requests efficiently.-Advanced ITSM Foundations: Built on decades of ITSM practices, ESM incorporates asset management, change management, and ITIL standards, ensuring best practices and reliability.-Service Request Efficiency: Simplify request management from basic IT tasks like password resets to complex issues such as performance troubleshooting, all managed through an intuitive ticketing system.-Enhanced Self-Service Portals: Empower end-users with a rich service catalog and comprehensive Knowledge Base, promoting self-directed solutions and reducing the burden on support teams.-Integrated Project Portfolio Management (PPM): With solutions like TeamDynamix, monitor all transactional and project work in a single view, enabling efficient resource planning and workload balance. Transition seamlessly from ITSM to ESM and harness the power of this versatile framework across your organization. Download our whitepaper to explore the essential elements for a successful ESM implementation. Download this whitepaper to understand the key building blocks for going from ITSM to ESM. Offered Free by: TeamDynamix See All Resources from: TeamDynamix source

Many businesses are now at the stage of their artificial intelligence journey where they’re working toward implementing the technology at scale. The foremost step for these executive teams is to appoint a chief AI officer (CAIO), so it’s not surprising that the number of CAIOs has almost tripled in the past five years, according to LinkedIn.  Certainly, CAIOs bring superior domain expertise and a deep understanding of opportunities and risks, which is invaluable for the successful deployment of AI technologies. Yet, for the entire organization to truly realize its benefits, implementation should never be done in isolation, but rather in collaboration.   In fact, collaboration shouldn’t kick-start once you want to implement a project. A healthy open dialogue to understand the goals and aims of stakeholders is essential. The CAIO needs broader buy-in to be successful, and this isn’t just from a courtesy perspective. Involving colleagues at the board level will ensure that there is greater investment in a project’s success from those who feel equally responsible for it — and excited by what it can achieve.   Implementation Starts With a Solid Foundation  The fact is, responsibly and reliably incorporating AI throughout a company is not a one-person job, or even a one-team job. Due to the complexities involved with AI adoption, success depends on the CAIO teaming up with almost everyone across the C-suite.   Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI Starting at the foundational level, a project of this significance requires a concerted effort to address and assuage the key concerns and hopes of executives. Looking at the boardroom, this would include:  The chief information officer (CIO): Implementing AI at scale requires an in-depth knowledge of the infrastructure that will be required for a seamless integration. This means working together with the CIO to effectively integrate AI tools with existing systems and databases.  The chief data officer (CDO): Infrastructure needs extend to high-quality data,   which is critical for training AI models. Aligning with the CDO will be essential, not only to ensure good data is available, but also to establish governance processes that protect the integrity and quality of the data.   The chief financial officer (CFO) and chief operating officer (COO): Any large-scale initiative requires considerable capital investment and operational discipline to track returns on a multimillion dollar spend. So, engaging with the CFO and COO is vital when assessing the business case for the technologies and evaluating whether the technologies are addressing the right problems given the organization’s overall strategy.   Related:Curtail Cloud Spend With These Strategies Humans at the Center  People are fundamental to every transformation, including AI-powered transformation. In fact, research by the EY organization and the University of Oxford’s Saïd Business School highlights that by placing humans at the center of a transformation, the likelihood of success increases more than 2.5 times, from 28% to 73%.   This is where the chief people officer comes in, fostering excitement about change, engaging talent with the transformation strategy and organizing critical training that will equip people with the skills they’ll need. They can also give insights into how the new technologies will affect workplace culture.   Additionally, the CAIO should engage with the chief diversity officer to involve diverse talents with the deployment of new AI systems. It’s essential to minimize the risk of bias and discrimination being embedded within these systems and make sure that everyone in the organization has an opportunity to participate equitably in driving innovation.   Understanding the Impact on Your Clients   External audiences are equally important to consider as many organizations are investing in AI to improve their customer experience. So, the CAIO can team with the chief marketing officer to create more personalized and memorable brand experiences that enable the organization to build deeper and longer-lasting relationships with its customers.   Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness If the organization has a chief ethics officer, the CAIO can work with them to draw up and apply ethical guidelines for AI development. This is critical since companies that demonstrate transparency, fairness, and good governance on AI will build confidence and resilience, positioning themselves favorably with consumers and regulators alike.  Tone at the Top  Perhaps the most consequential is the chief executive officer (CEO), who will have final say on the overall business strategy, investment decisions, and where AI will fit in. They set the tone at the top around AI deployment and can talk candidly about the challenges and opportunities that exist.  As EY research highlights, almost all (99%) of global CEOs said they planned to make significant investments in GenAI, with 69% reallocating capital from other investment projects to fund these investments. This level of commitment will help to instill confidence among employees and other stakeholders, encouraging them to buy into the role of AI in the future workplace.   At EY, AI is baked into all aspects of our new global strategy, All in, and this is intended to shape our client’s future with confidence. Businesses looking to follow a similar path need their CAIO to be the crucial enabler of AI-powered transformation across the organization. Translating the technical capabilities of AI into actionable business strategies for different functions is the table stakes. True success only happens if the rest of the executive team gets on board.   source

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When much of human activity moved online during the height of the pandemic, tech companies were thriving. Call it the COVID tech bubble. Now we’ve hit the COVID tech bust. By the second half of 2022, tech companies had initiated significant layoffs — something that had followed an extended period of frenzied tech hiring and attention to employee experience. Standard explanations for the cuts were that companies hired too many during the pandemic and they were looking at the specter of a recession in the months ahead. It sounds a lot like the dot-com boom and bust of yore. Not all companies are impacted equally. It’s the ones that hired at an accelerated rate during the boom that seem to be hitting the brakes right now. At the same time, IT pros with cybersecurity, cloud, and data analytics/machine learning skills have remained in high demand so far. In this space, InformationWeek will document some of the more significant layoffs, updated regularly. Be sure to check back. Here’s a look at the biggest tech layoffs so far: Related:2023 IT Salary Report: Pay Increases Despite Economic Pressures October 2024 Tech Layoffs Kyte, October 25, 2024 announcement. Layoff of 40% of workforce. Amid an ongoing effort to survive after exploring a sale earlier this year, the rental car startup is exiting almost all of its major markets in the US and has cut between 40% and 50% of its workforce, according to TechCrunch. Engineering, consumer, and growth product teams were impacted most. Kyte is reducing its operations to focus only on San Francisco and New York City (including Jersey City) as it works to reach profitability in the next 18 months, CEO Nikolaus Volk said to TechCrunch. Other major markets that Kyte has been pulling out of reportedly include Atlanta, Chicago, Boston, Washington, D.C., Philadelphia, and Seattle. “In a capital-constrained environment, where capital is super expensive, we have to focus on our strongest markets,” Volk said. Upwork, October 23, 2024 announcement. Layoff of 21% of workforce. To maintain profitable growth, “increase efficiency, and accelerate innovation for its customers,” Upwork is reducing its total workforce by 21%, according to a company news release. The cuts are expected to generate approximately $60 million in annualized cost savings. “We are making ourselves a more streamlined and efficient organization, continuing our successful focus on durable, profitable growth and delivering value for our customers and shareholders,” said Hayden Brown, president and CEO, Upwork. According to the release, Upwork plans to flatten the structures of its teams as it leverages more automation and third-party services to simplify processes and operate more efficiently at scale. Venminder, October 21, 2024 announcement. Layoff of 100 people. Just seven weeks after announcing that it had been acquired by Ncontracts, the Kentucky-based tech company is laying off 100 employees due to redundancies, according to The Business Journals. The cuts were confirmed in a WARN notice filed by Venminder and are expected to begin on December 15, 2024. CapWay, October 17, 2024 announcement. Layoff of entire workforce. Founder Sheena Allen announced operations at Capway have been shut down in a LinkedIn post on Wednesday, according to TechCrunch. The Y Combinator-backed fintech reportedly sought to bring financial services to those in banking deserts, which are people in communities, often rural, that have no nearby physical bank branch to obtain a checking account. The term also applies to people who have difficulty going to a bank, such as lower-income, older, or disabled individuals. Allen told TechCrunch the company started to wind down last year and waited this long to announce after a possible acquisition fell through. Allen reportedly cited the fact that the fintech industry took a huge reputational hit after the hacking of Evolve Bank & Trust and the collapse of Synapse. Hundreds of millions of dollars of consumer funds were frozen as a result of the Synapse’s collapse. In response, many banks that looked to partner with fintechs, required the fintech to have a certain amount of money in the bank. During this time period, CapWay struggled to adjust and find a banking partner. The company also struggled to raise more money to meet potential partners’ money-on-hand requirements. “I am proud of the work we were able to accomplish, but honestly disappointed that we couldn’t complete the mission,” Allen wrote on LinkedIn. “I feel strongly that there is still much work to be done in the financial inclusion space, so it won’t be the last you hear of me in regard to fighting for economic equality.”  Meta, October 16, 2024 announcement. Layoff total TBA. Employees across various departments, including WhatsApp, Instagram, and Reality Labs are reportedly being laid off at Meta, according to The Verge. Meta opted against mass, companywide layoffs to initiate smaller cuts that seem to align with the restructuring of specific departments. According to the report, some Meta employees have started posting on social media saying they’ve been laid off. “Today, a few teams at Meta are making changes to ensure resources are aligned with their long-term strategic goals and location strategy,” company spokesperson Dave Arnold said in a statement shared with The Verge. “This includes moving some teams to different locations, and moving some employees to different roles. In situations like this when a role is eliminated, we work hard to find other opportunities for impacted employees.” Stay tuned. Gigamon, October 15, 2024 announcement. Layoff of 69 people. Starting on New Year’s Eve, the cloud computing company will layoff 69 employees from its Santa Clara headquarters, according to SFGate. The cuts were announced in a WARN filing with the state on Thursday, which is required in the event of mass layoffs. According to the report, several engineers, directors, managers, and a few analysts and accountants will be impacted by the restructuring. In addition, the WARN document breaks down Gigamon’s restructuring plan: 34 workers will permanently lose their jobs on Dec. 31, followed by 17, eight, and 10 at the close of the next three months. ByteDance, October 11, 2024 announcement. Layoff of 500 people.