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Cloud Computing solutions, including Software, Infrastructure, Platform, Unified Communications, Mobile, and Content as a Service are well-established and growing. The evolution of these markets will be driven by the complex interaction of all participants, beginning with end customers.

Edge Strategies has conducted over 80,000 interviews in behalf of our clients in both mature and emerging markets with decision-makers across the full cloud ecosystem- including Vendors, Service Provider and End Customer organizations.

Typical projects include:

  • Identifying target market segments
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  • Developing Value Proposition and Messaging for each customer segment
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  • Designing Activation Programs
  • Building process to reduce churn, build loyalty and measure Customer Lifetime Value
  • Improving the User Experience

We provide current, actionable insight into business decision processes across market segments, from SMBs to Large Enterprises. Our work leverages a deep understanding of the business models of key Cloud Ecosystem participants including:

  • Cloud Service Providers ( CSPs)
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  • Communication Service Providers
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  • MSPs and IT Channels

Our experience allows us to get up to speed quickly on new projects. We are experts in designing and conducting quantitative and qualitative research. Based on our focused findings, we work with our clients to make the decisions necessary to gain early success in a variety of markets, including SaaS, IaaS, PaaS, UCaaS, and mobile/device services.    

 

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News

  • Two-thirds of leaders feel genAI will reshape entry-level jobs

    Generative AI is on track to transform entry-level roles across industries, with 64% of leaders expecting these positions to evolve from creation to review and refinement of outputs within the next three years. The percentage of managers holding a similar view stood at 71%, according to new research from the Capgemini Research Institute. The findings suggest the traditional model of junior employees manually creating content, data, or code is rapidly being replaced by generative AI, which can generate these outputs in a fraction of the time. As a result, employees in these positions will focus more on quality control, critical analysis, and ensuring AI outputs meet business standards, the report added. The findings are based on Capgemini Research Institute’s May 2024 survey that involved 1,500 executives and 1,000 entry-level employees from 500 organizations with over $1 billion in revenue, to explore generative AI adoption. The report revealed that employees expect genAI will manage an average of a third of their tasks (32%) within the next 12 months, indicating a potential productivity boost. This shift is expected to be felt most acutely in roles that traditionally involve manual content creation, data entry, or routine customer service tasks. “Generative AI tools are becoming more adept at assisting with complex managerial tasks, which could challenge the status quo of organizational structure and ways of working,” Roshan Gya, CEO of Capgemini Invent and a member of the Group Executive Committee said in a press note. “This shift allows employees to focus on higher-value activities, unlocking new perspectives and challenging assumptions.” Increased autonomy for junior employees The widespread integration of AI into the workplace will not only change the nature of entry-level work but also grant more autonomy to junior employees. According to the data, 52% of leaders and managers expect entry-level positions to gain greater independence as AI becomes more embedded in daily workflows. For example, in industries such as supply chain and logistics, AI will take on tasks like inventory management and order processing, allowing junior analysts to focus on strategic tasks and project management. As AI continues to manage mundane tasks, junior employees will have more opportunities to make decisions that were previously the domain of higher-level staff. This will enable them to fast-track their careers and assume greater responsibilities early on, the report stated. The report reveals that 51% of leaders believe AI will accelerate the career progression of entry-level employees. With AI automating routine functions, junior employees will gain exposure to more strategic elements of their roles, moving into management positions much faster than traditional career paths have allowed. AI will facilitate this shift by providing employees with the tools and data necessary to make informed decisions and take on supervisory roles. “It should be noted that this shift depends on several factors: clarity on skills requirements at higher levels; the ability of junior employees to develop these skills (often tied to experience, which cannot be fast-tracked); and the availability of opportunities available for the shift,” the report pointed out. It further added, “Organizations must prioritize building the skills and readiness of junior employees as part of a clear roadmap for employees’ journeys to people leadership or functional/technical leadership. This requires proactive steps around talent acquisition, development, skilling, and review and reward mechanisms.” This transformation is already visible in fields such as marketing, customer service, and even technical domains like software development. “We’re seeing AI take over foundational tasks in these sectors, and junior employees are becoming curators of AI work, rather than creators,” the report said. With AI’s involvement, the proportion of managers within teams could expand from 44% to 53%, reflecting a broader move toward specialized roles that focus on managing AI-human collaborations. Productivity gains, but upskilling gaps remain The findings from the report suggest that while AI adoption promises significant productivity gains — potentially saving 18% of time for entry-level workers — there are concerns about the readiness of employees to leverage these tools. Despite the optimism about AI’s role in career acceleration, the report highlights a significant gap: only 16% of employees feel they are receiving adequate training in AI-related skills from their organizations. This gap poses a major challenge for companies that want to fully harness the benefits of AI, especially at the entry level. “Without the proper training and resources, employees won’t be able to maximize the potential of AI,” the report cautioned, urging organizations to prioritize formal training programs to ensure their teams are ready for the AI-driven future​. The path ahead Despite the promise of genAI, adoption remains nascent. While 64% of workers already use genAI tools, only 20% use them daily, the report stated. This gap between AI’s potential and actual usage underscores the need for clearer guidelines, comprehensive training, and better integration of AI tools into existing workflows. As organizations continue to explore AI’s capabilities, defining roles and responsibilities for human-AI collaboration will be key to ensuring accountability and cohesion across teams. With 81% of leaders expecting new roles like AI ethics specialists and data curators to emerge, the landscape of entry-level work is on the verge of a significant transformation. The future of work, it seems, will be less about replacing human effort and more about enhancing it through strategic collaboration with AI.

  • GenAI surges in law firms: Will it spell the end of the billable hour?

    Saying the legal profession is document-intensive is like claiming that a library is filled with books. Unlike researchers using the Dewey Decimal System, though, lawyers face an ocean of big data from which they must fish out case evidence. For more than a decade, law firms have been using machine learning and artificial intelligence tools to aid the discovery process, helping them hunt down paper trails and digital content alike. But it wasn’t until the arrival two years ago of OpenAI’s generative AI (genAI) conversational chatbot, ChatGPT, that the technology became common and easy enough to use that even first-year associates straight out of law school could rely on it for electronic discovery (eDiscovery). The interest in genAI for legal discovery and automating other tasks is unprecedented, according to Ron Friedmann, a Gartner senior director analyst.     “There’s way more piloting that I’ve seen, especially in large law firms. So, there’s been a lot of expense, especially the allocating of staff and paying out of pocket for licensing fees,” Friedmann said. “Part is keeping up with the Joneses, part of it is marketing, and part of it is just getting over the adoption challenges,” he continued. “In eDiscovery, before the advent of genAI, you needed some training to know how to interact with discovery database. There were a lot of tools, but they all had the same issue: You had to be pretty technically adept to tackle the database yourself.” Law firms and corporate legal departments are adopting genAI for a myriad of purposes, ranging from document discovery and analysis to contract lifecycle management. GenAI can be used to categorize and summarize documents, draft new ones, and generate client communications. A 2023 American Bar Association survey found that over 20% of large law firms (500+ attorneys) are using AI tools, with nearly 15% considering purchasing them. And over the past year, AI adoption in the legal sector has jumped from 19% to 79%, according to legal tech firm Clio. All areas of law will use genAI, according to Joshua Lenon, Clio’s Lawyer in Residence. That’s because AI content generation and task automation tools can help the business side and practice efforts of law firms. However, areas that have repetitive workflows and large document volumes – like civil litigation – will adopt genAI e-discovery tools more quickly. Practice areas that charge exclusively flat fees – like traffic offenses and immigration – are already the largest adopters of genAi.  Lenon said AI is expected to have the most immediate impact in areas like civil litigation, where repetitive workflows and large volumes of documents make it ideal for tasks such as eDiscovery. “Additionally, practice areas that commonly rely on flat-fee billing, such as traffic offenses and immigration, are already leading in the adoption of genAI tools. The efficiency gains in these sectors are driving their early and widespread use of AI,” Lenon said. In legal departments, for example, genAI has allowed workers to query digital contracts and get accurate answers to questions about things like existing clauses. “There are all sorts of things buried inside contracts that once people can easily get access to will have a big impact on how companies operate, the risk they have, and how to mitigate those risks,” said David Wilkins, director of the Center on the Legal Profession at Harvard Law School. Wilkins and other experts say that because law is document intensive, people have long sought ways to use technology to streamline, make more efficient, and cut down on work related to the discovery, creation, and analysis of documents. “What we’re seeing now is lots of use of technologies of various kinds in contract formation and end-to-end contract lifecycle management. That is a huge area,” Wilkins said. Corporate legal departments are not as far down the genAI adoption path as law firms, because companies primarily see those business units as cost centers — so the purse strings are cinched tighter. Nonetheless, legal departments are kicking the tires on genAI. A clear win for pre-trial discovery Multinational law firm Cleary Gottlieb said it has been able to dramatically cull the number of attorneys used for pre-trial discovery and has even launched a technology unit and genAI legal service: ClearyX. In the past, it wasn’t uncommon for 150 or more attorneys to be assigned to a case to discover all the documents and other content, and it could take them months to complete the task. While Cleary readily admits that genAI isn’t perfect in retrieving 100% of the documents related to a case or always creating an accurate synopsis of them, neither are humans. At this point in the technology’s development, it’s good enough most of the time to reduce workloads and costs. Christian “CJ” Mahoney, global head of Cleary’s e-Discovery and Litigation Technology group, said he was just part of a lawsuit that involved analyzing 50 million documents (15 terabytes of data). “And we had to do it in matter of weeks to find out what we had to provide to the opposing party. “We’re using more complex workflows using AI. I saw a 60-person to 45-person reduction. But on this kind of case, I would have had probably 150 attorneys doing this 15 years ago. Back then, it would just be like ‘OK, guys, here’s a mountain of evidence — go through it,’” Mahoney said. Traditional ways to look through case documents simply aren’t feasible anymore. “You need to incorporate AI into the process for analysis now,” Mahoney said. While his firm has been using machine learning and AI for about a decade, with the introduction of genAI, there’s ubiquity and ease of use, Mahoney said. That has allowed even junior associates to be able to use the tech for eDiscovery and other tasks. “There’s a bit of an expectation that with the advent of genAI, things should be quicker and cheaper,” he said. Carla Swansburg, CEO of ClearyX, the firm’s AI tech subsidiary, said that as recently as a year and a half ago, clients were telling her AI is too risky, but those same clients are now asking how Cleary is using AI to benefit them and make their services more efficient. “Nobody went to law school to do this. I used to go through banker’s boxes with sticky notes as a litigator. Nobody wants to do that. Nobody wants to read 100 leases to highlight an assignment clause for you,” Swansburg said. “The good thing is [genAI is] moving up the value chain, but it’s starting with things that people really don’t want to be doing anyways.” The interest in genAI hasn’t been lost on those selling traditional legal services and software. For example, legal research tools such as LexisNexis, Westlaw, and vLex and legal document automation software from Harvey.ai and Clio have built genAI into their products. Contract lifecycle management and analytics vendors such as Icertis, Sirion, LinkSquares, and Ironclad have also added AI to their lines. The number of proven and routine use cases for genAI in legal fields is limited, however, because of ongoing accuracy and hallucination problems, according to Freidmann. And genAI isn’t always less expensive than using people. “We are still trying to collectively figure out what the economics of it is. I’ve spoken to friends who say in the end genAI took more time and cost than doing it the old-fashioned method,” Friedmann said. “But people are remaining open to it and continuing to experiment.” The death of the billable hour? Over the past two decades, the vast volumes of structured and unstructured data generated through traditional means, such as contracts, records, corporate policies, and so on has been joined by electronic communications — adding new challenges in eDiscovery. Once only a paper chase, legal discovery now involves scouring emails, messaging, social media records — even video and photos — in the lead-up to a trial. Nearly three-quarters of a law firm’s hourly billable tasks are exposed to AI automation, with 81% of legal secretaries’ and administrative assistants’ tasks being automatable, compared to 57% of lawyers’ tasks, according a survey of both legal professionals (1,028) and another adults (1,003) in the U.S. general population, by Clio. Hourly billing has long been the preference of many professionals, from lawyers to consultants, but AI adoption is upending this model where clients are charged for the time spent on services.  In 2023, 19% of law firms reported using AI. Now, 79% of legal professionals are using AI in their practice, according to legal tech company Clio. As AI adoption continues to accelerate in the legal industry, executives may need to rethink key elements of their business, including their billing models. Billable work could be automated by AI, according to experts. This month, Clio released the results of a survey showing that law firms are charging 34% more of their cases on a flat-fee basis compared to 2016. The billable hour will continue, but the frequency of use and types of activities that will be billed hourly will diminish. Automatable tasks will switch to flat fees, with the AI output being reviewed at hourly rates,” said Clio’s Lenon. “The billable hour is unlikely to be fully eliminated, but its dominance in the legal industry is expected to decrease. AI-generated outputs, particularly those requiring human review, may still be billed on an hourly basis. “Ultimately, the billable hour will remain, but in a more selective capacity,” Lenon continued. Clio’s research over 7,000,000 time entries found that 74% of billable legal work activities will be impacted by AI automation. While hourly billing remains predominant in law firms, their clients are driving the shift towards flat fees, with 71% now preferring to pay a flat fee for their entire case, and 51% favoring flat fees for individual activities, according to Clio’s report. In addition, law firms using flat fees benefit from quicker billing cycles and faster payment collection, as they are five times more likely to send bills — and nearly twice as likely to receive payments — as soon as they complete their work for clients. Last year, firms were testing on average as many as three to five genAI models in the hope of reducing workloads, and that also meant employing supportive resources such as innovation teams and knowledge management professionals, Gartner’s Friedmann said. People have been talking about the demise of the billable hour for about 30 years “and nothing’s killed it yet,” said Ryan O’Leary, research director for privacy and legal technology at IDC. “But if anything will, it’ll be this.” However, there are still a lot of issues with genAI that need to be settled before it could automate legal services, O’Leary cautioned — not least of which is how much genAI may cost to use and how accurate and secure it can be. “The cost of using AI may be as much as using an associate,” O’Leary said. Is genAI cheaper and more accurate than an attorney? Along with AI’s ability to perform tasks previously accomplished by attorneys and other legal workers, there remains a big concern over accuracy, security, and hallucinations. As in the healthcare industry, the stakes are high when it comes to client confidential information. “There are big issues around copyright protection and whether these large language models are being trained on copyrighted materials,” Harvard’s Wilkins said. “So, what you’re seeing is a lot of experimentation with trying to build customized AI models and large language models. AI providers claim their models are trained exclusively on legal materials, cutting down on hallucinations.” While law firms are aware of AI’s pitfalls, attorneys are still going to use the technology, Wilkins said, whether or not that’s in line with a corporate policy. GenAI is simply too “transformative” a technology to not use simply because there are risks, he said. One problem in comparing human workers to the technology is that the bar is often set too high for AI, Wilkins said. “I’ve heard people say, ‘We could never use this unless it’s 98% effective and reliable.’ I said, ‘Well, does it have the reliability of sending an associate to a windowless warehouse in Phoenix, Arizona to find documents related to a case? Is that 99% accurate?’” Wilkins said. In the end, whether genAI assists in a task or not, ultimately the attorneys involved will be held responsible for the outcome — good or bad. Whether the technology will replace attorneys and legal aides remains to be seen. “Our experience has been — and we’ve kicked tires on a lot of language models and purpose-designed tools — [genAI tools] are not good enough to replace people for a lot of the work we do,” ClearyX’s Swansburg said. “For something like due diligence…, you often must be [100%] right. You need to know whether you can get consent to transfer something. In other use cases, such as summarization and initial drafting, that sort of thing is a little more accessible. “In my world, it’s not really replacing jobs yet, but it’s changing how you do jobs,” she continued. “So, it’s allowing people to move up the value chain a little bit. It’s taking away rote and repetitive work.” Harvard’s Wilkins placed the adoption of AI by law firms and other legal entities as still being “in the Stone Age” but with massive potential. “The potential efficiencies are great,” he said. “We’re just working out what are the real advantages.”

  • One of the world’s largest book publishers adds AI warnings to its books

    To make generative AI tools (genAI) work as well as possible, tech companies have chosen to train their large language models (LLMs) on large amounts of text, even though doing so could run afoul of copyright laws. Most recently, book publisher Penguin Random House has chosen to include a warning in its books stating the content may not be used or reproduced for the purpose of training AI models. And, according to The Bookseller, the AI warning will not only be added to new books but also to reprints of older titles. The move is likely to spur more publishers to follow suit with similar warnings to their books.

  • Microsoft to launch autonomous AI agents in November

    Microsoft will soon let customers build autonomous AI agents that can be configured to perform complex tasks with little or no input from humans. Microsoft on Monday announced that tools to build AI agents in Copilot Studio will be available in a public beta that begins at the company’s Ignite conference on Nov. 19, with pre-built agents rolling out to Dynamics 365 apps in the coming month,s too. Microsoft first unveiled plans to let users create AI agents in Copilot Studio — its low- or no-code AI development platform — in May with a private preview for select customers.  Generative AI (genAI) agents can be seen as the next stage in the evolution of conversational AI assistants such as Microsoft’s Copilot and OpenAI’s ChatGPT. While AI assistants respond directly to a user’s instructions — such as drafting an email or summarizing a document — autonomous AI agents are triggered by events and can perform more complex, multi-step processes on their own.   For example, a business could configure an AI agent to respond to the arrival of a customer email. At this point, the AI agent can look up the sender’s account details, check for past communications, and then take a range of actions — such as checking inventory or asking the customer for preferences — on its own.  There are a wide range of potential use cases, according to Microsoft, with the ability to tailor AI agents to a variety of tasks, from employee onboarding to supply chain automation. “We think of agents as the new apps for an AI-powered world,” said Bryan Goode, corporate vice president for business Applications at Microsoft.    AI agents can be created via a no-code graphical interface in Copilot Studio, meaning no software development is required, according to Microsoft. Agents can then be published and accessed in a variety of places: from Microsoft’s Copilot AI assistant, on a website, or within an app. The new Copilot AI agents can help take sales orders, for exaample. Microsoft Goode sees a broad appeal for workers outside of developers and IT: “We think everyone will need to be able to create agents in the future, much like how everyone can create spreadsheets or presentations in Microsoft 365,” he said.  “Agents really represent the democratization of AI for many enterprise users who have specific tasks they want to accomplish, but have no desire to become AI experts,” said Jack Gold, principal analyst with business consultancy J. Gold Associates. Microsoft has taken steps to mitigate the impact of “hallucinations” –—a problem that’s exacerbated when AI agents can act independently and are given access to business applications.  For example, agents created for Dynamics will require human approval before carrying out certain actions, said Goode, such as preparing outbound communications. A viewable record of actions taken by an AI agent and why it took a decision is also kept in Copilot Studio. More generally, Goode pointed to improvements to Microsoft’s Azure Content Safety system, which helps “measure, detect and mitigate hallucinations” more effectively, he said. Nevertheless, hallucinations will continue to be a consideration for businesses that deploy AI agents, said Rowan Curran, senior analyst at Forrester.  “Buyers are rightly excited about the potential of agentic AI systems, but the reality of implementation is going to be just as challenging, if not more so, than the current generation of advanced RAG [retrieval-augmented generation] systems,” Curran said. “Having a strong data foundation will be essential for building useful AI agents: data quality and management aren’t problems that can be swept under the rug.” Microsoft is developing 10 pre-configured AI agents for its Dynamics 365 business application suite. These include a “sales qualification agent” for Dynamics 365 Sales, a “sales order agent” for Dynamics 365 Business Central, and a “case management agent” Dynamics 365 Customer Service. The AI agents for Dynamics 365 will be available “over the coming months,” a Microsoft spokesperson said, with pricing and licensing details to be announced closer to the general availability launch.   Microsoft is not alone in building AI agents into its products: other business software vendors are doing the same, from Salesforce, which unveiled its Agentforce platform last month, to SAP and ServiceNow, as well as digital work app vendors such as Atlassian and Asana. “In the next couple of years, you’ll see virtually all enterprise solutions providers deploy agents into their apps,” said Gold. 

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