Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprises are bullish on agentic applications that can understand user instructions and intent to perform different tasks in digital environments. It’s the next wave in the age of generative AI, but many organizations still struggle with low throughputs with their models. Today, Katanemo, a startup building intelligent infrastructure for AI-native applications, took a step to solve this problem by open-sourcing Arch-Function. This is a collection of state-of-the-art large language models (LLMs) promising ultra-fast speeds at function-calling tasks critical to agentic workflows. But, just how fast are we talking about here? According to Salman Paracha, the founder and CEO of Katanemo, the new open models are nearly 12 times faster than OpenAI’s GPT-4. It even outperforms offerings from Anthropic all while delivering significant cost savings at the same time.  The move can easily pave the way for super-responsive agents that could handle domain-specific use cases without burning a hole in the businesses’ pockets. According to Gartner, by 2028, 33% of enterprise software tools will use agentic AI, up from less than 1% at present, enabling 15% of day-to-day work decisions to be made autonomously. What exactly does Arch-Function bring to the table? A week ago, Katanemo open-sourced Arch, an intelligent prompt gateway that uses specialized (sub-billion) LLMs to handle all critical tasks related to the handling and processing of prompts. This includes detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user’s request and managing the observability of prompts and LLM interactions in a centralized way.  The offering allows developers to build fast, secure and personalized gen AI apps at any scale. Now, as the next step in this work, the company has open-sourced some of the “intelligence” behind the gateway in the form of Arch-Function LLMs. As the founder puts it, these new LLMs – built on top of Qwen 2.5 with 3B and 7B parameters – are designed to handle function calls, which essentially allows them to interact with external tools and systems for performing digital tasks and accessing up-to-date information.  Using a given set of natural language prompts, the Arch-Function models can understand complex function signatures, identify required parameters and produce accurate function call outputs. This allows it to execute any required task, be it an API interaction or an automated backend workflow. This, in turn, can enable enterprises to develop agentic applications.  “In simple terms, Arch-Function helps you personalize your LLM apps by calling application-specific operations triggered via user prompts. With Arch-Function, you can build fast ‘agentic’ workflows tailored to domain-specific use cases – from updating insurance claims to creating ad campaigns via prompts. Arch-Function analyzes prompts, extracts critical information from them, engages in lightweight conversations to gather missing parameters from the user, and makes API calls so that you can focus on writing business logic,” Paracha explained. Speed and cost are the biggest highlights While function calling is not a new capability (many models support it), how effectively Arch-Function LLMs handle is the highlight. According to details shared by Paracha on X, the models beat or match frontier models, including those from OpenAI and Anthropic, in terms of quality but deliver significant benefits in terms of speed and cost savings.  For instance, compared to GPT-4, Arch-Function-3B delivers approximately 12x throughput improvement and massive 44x cost savings. Similar results were also seen against GPT-4o and Claude 3.5 Sonnet. The company has yet to share full benchmarks, but Paracha did note that the throughput and cost savings were seen when an L40S Nvidia GPU was used to host the 3B parameter model. “The standard is using the V100 or A100 to run/benchmark LLMS, and the L40S is a cheaper instance than both. Of course, this is our quantized version, with similar quality performance,” he noted. Another exciting day here Katanemo as we open source some of the "intelligence" behind Arch (https://t.co/9nwakOGPp0). Meet Katanemo Arch-Function, a collection of state-of-the-art (SOTA) LLMs designed for function calling tasks – that meet/beat frontier LLM performance, but… pic.twitter.com/IajF8w3syz — Salman Paracha (Building Intelligent Infra) (@salman_paracha) October 15, 2024 With this work, enterprises can have a faster and more affordable family of function-calling LLMs to power their agentic applications. The company has yet to share case studies of how these models are being utilized, but high-throughput performance with low costs makes an ideal combo for real-time, production use cases such as processing incoming data for campaign optimization or sending emails to clients. According to Markets and Markets, globally, the market for AI agents is expected to grow with a CAGR of nearly 45% to become a $47 billion opportunity by 2030. source

Every year for the last 26 years, Forrester has convened its Board of Clients — a group of clients (pic to the left) who advise us on our strategy, new products, and research agenda. These meetings have had extraordinary impact on our voyage, helping us to continually improve and to meet the high standards of the companies we serve. The current board includes executives from CITGO, Prudential, NOAA, Bank of America, SAP, and other longtime clients — a fantastic group! One of our traditions is to share books and other media recommendations. Here are this year’s “picks” from the Board: Books: Slow Productivity, Cal Newport Team of Rivals: The Political Genius of Abraham Lincoln, Doris Kearns Goodwin Outlive, Peter Attia Build the Life You Want, Arthur Brooks and Oprah Winfrey My Beloved Monster, Caleb Carr A Soldier of the Great War, Mark Helprin The Future and Its Enemies, Virginia Postrel   Podcasts: Acquired Business Wars Dolly Parton’s America 60 Songs That Explain the ’90s SmartLess The Moth   TV series: Bad Monkey The Repair Shop Drops of God Only Murders in the Building source

David Bowie’s classic song “Golden Years” tells us to “look at that sky, life’s begun.” But retirement’s “golden years” are looking less golden for Americans. Forrester data indicates that many Americans are willing to work past the age of retirement or even work a part-time job in retirement to supplement their income. Preparation for retirement is also greatly lacking. Many Americans don’t own retirement accounts and aren’t seeking retirement planning advice. Instead, they focus on more immediate financial concerns, impacting their ability to save and prepare for life after working.   Our data also shows that only half of Americans believe that they know how to save for retirement. Even worse, the majority of those with retirement accounts say that they are not getting the advice they need. All of this highlights an unmet need for retirement education that wealth management firms can address by driving more awareness among consumers and by training advisors to highlight planning’s importance with their clients. Bowie closes his song on an optimistic note: “Nothing’s gonna touch you in these golden years.” Financially savvy consumers and their advisors know the power of compound returns. They also know that an early start will help retirement savers build a financial buffer that will ease their anxieties about the future. Better to educate and prepare when people are young than to force them into a retirement that is inevitably insecure or, worse yet, isn’t really a retirement at all. Preparation will leave those golden years untouched. Forrester clients interested in discussing our latest retirement report or this interesting data can connect with me via inquiry or guidance session. source

Once again, the banking industry is on the cusp of transformative changes. AI and generative AI (genAI) technologies have the potential to enhance customer relationships, deliver personalized digital experiences, augment customer service, and combat fraud. But they also have the potential to shift the mindset of banking from a product-centric to a relationship-centric approach and earn greater customer trust. While AI and genAI are set to reshape banking, other drivers will ultimately differentiate banks in the future: Banks will be invisible, connected, insights-driven, and purposeful. Tomorrow’s customer will expect banking to be embedded at their point of need, and they will seek banks that are values-aligned and enhance financial well-being. To meet the needs of future customers, leading banks will use technology, capitalize on consumer trust, and leverage insights to deliver the connected products and services that customers value. Banks will expand their role as trusted advisor and harness innovation for sustainable growth. Customer obsession and collaboration will help banks stay relevant and build trust. To remain relevant in a rapidly changing industry, banks must create a culture of customer obsession and foster collaboration. This means using technology to anticipate customer needs and deliver proactive, relevant services, connecting with partners and ecosystems to deliver financial outcomes, leveraging data and insights to create value, and aligning with customer values in a purposeful way. By adopting these characteristics, banks can stay relevant and build trust. Innovation won’t be limited to AI. While AI and genAI hold immense potential, innovation in the banking industry extends beyond these technologies. Banks will design for integration, collaboration, and speed, with experiences that are assistive, agentive, personalized, and anticipatory. Modern tech architecture will allow digital and technology executives to design and deliver modular and composable products and services. Banks will blend traditional products with new solutions that meet customer needs, creating closer connections with customers. Successful banks will leverage trust intelligently and operate in multiple modes. They will curate and connect firms in constellations of value, power products and distribute them via others’ marketplaces, specialize in specific customer segments, make quick decisions, and become adaptive enterprises. By playing to their strengths and embracing change, banks can transform their business models and thrive in a world with blurred boundaries and increasing competition. Banks must prioritize customer obsession, transform their business models, and embrace innovation to build trust with and create meaningful experiences for their customers. By taking an invisible, connected, insights-driven, and purposeful approach, banks can navigate the future of banking and create value for both customers and the bank. To learn more about what leading banks are doing and how to prepare your firm for the future of banking: Clients with additional questions can book an inquiry or guidance session here. source

I have some exciting news for marketing leaders seeking to innovate on their marketing strategies. Forrester’s B2B Frontline Marketing Survey, 2024, found that frontline marketing teams with advanced lifecycle revenue marketing strategies are more likely to meet or exceed their revenue goals than frontline marketing teams with less mature strategies. Read on for the summary. Lifecycle Revenue Marketing Is Hitting More Revenue Plans I generally work with two types of clients: The first type of client wants Forrester to help them adopt marketing strategies that are already proven standards so that they don’t have to reinvent the wheel or take too many risks. The second type wants Forrester to help them innovate and test new marketing strategies that other organizations haven’t tried or proven yet so they can gain a competitive advantage if it works. I’ve been researching emerging B2B growth strategies for many years now, even before my time at Forrester began. And just a little over three years ago, I started working with progressive B2B marketing leaders who wanted me to help them think differently about their frontline growth strategies. The results led to the concept of frontline marketing and a new strategy called lifecycle revenue marketing (LRM). Both are relatively new B2B constructs: I coined these terms with my colleagues Lori Wizdo and Steve Casey in 2022. And now, using Forrester’s Lifecycle Revenue Marketing Maturity Assessment, we discovered that 22% of frontline B2B marketing leaders in the US (14% in Europe and 10% in Asia Pacific) already have advanced LRM strategies by Forrester’s definition. Those strategies are proving effective, as well. Eighty-seven percent of advanced B2B frontline marketing leaders reported that their team met or exceeded their revenue plans, compared to 77% of frontline marketing teams with beginner or intermediate strategies. Frontline Marketing Leaders Are Still Facing Challenges Despite the apparent extra boost in revenue plan attainment from lifecycle revenue marketing, my research uncovered more potential for frontline marketing to impact revenue. Our research found that frontline marketing leaders still say they need improvements in several areas to significantly boost their impact on revenue growth, including: Improving their relationship with sales. Participating more frequently in strategy formation and revenue planning. Increasing accountability for campaign planning and design. Leveraging revenue development reps to identify buying groups. Removing cross-functional dependencies, which are significant. Read The Report On The Global State Of Frontline Marketing As the B2B buying landscape continues to evolve, frontline marketing and lifecycle revenue marketing are becoming even more important strategic growth drivers for B2B organizations. Forrester clients can read all about the global state of frontline marketing in my new report: Frontline Marketing And Lifecycle Revenue Marketing Are B2B Game Changers. The report is full of data profiling and comparing frontline marketing teams from beginner to advanced. And if you’re a B2B CMO or frontline marketing leader looking to advance your frontline marketing maturity, schedule a guidance session and get started on the path to more achievable growth. source

Presented by Celonis The State of Oklahoma had a $3 billion problem: In 2022, its Legislative Office of Fiscal Transparency found that a full quarter of the state’s $12 billion budget was spent without oversight, posing serious financial and legal risks. Its processes were hopelessly broken. But they found a solution that was not only 200 times more efficient but slashed potential costs by $11.4 million: process intelligence. It’s a technology that is transforming business operations – and it’s proving crucial to successful generative AI as well as the rapidly approaching agentic AI future. “Every organization in every industry runs on a collection of interacting processes – finance, supply chain, sales, marketing – and all have to work well, and they have to work well together, and that’s not easy, since we’re talking about multiple systems and departments and multiple languages,” says Alex Rinke, co-CEO and co-founder of Celonis. “Process intelligence platforms give you full visibility into how these processes are operating, where they’re getting stuck, where you have your bottlenecks, where you have your deviations, where you have process issues, and then remediates those issues.” For instance, in a matter of months, process intelligence further helped the State of Oklahoma pivot to reviewing state purchases in real-time, so staff are able to serve their state and be transparent with taxpayer dollars. And across the pond, the NHS (National Health Service) in the U.K. used process intelligence to eliminate 1,800 appointment cancellations each week just by shifting when the appointment reminder goes out, uncovering ways to reduce the waiting list by around 5,300 patients in eight weeks by optimizing the patient journey, and realized an estimated savings of £2.8M a year along the way. In other words, instead of the business equivalent of throwing spaghetti at the wall and hoping something sticks, process intelligence revealed where process changes or AI solutions could offer profound results. “Process intelligence provides business context – a true understanding of where, in any end-to-end process, we need to apply a change, and identifies the places AI can have the biggest impact for our customers, for their bottom line, for their green line, for their people and their productivity,” Rinke adds. “Without visibility into a process, you’re tossing AI at a problem just because you want to use AI. You’re not actually moving the needle. Process intelligence is the only way to achieve ROAI – return on AI investment.” Why process intelligence is the key to AI To understand the challenges of enterprise AI, consider how it differs from consumer AI. Both rely on a wealth of data to operate correctly. However, consumer AI not only has the whole internet of data at its proverbial fingertips, that data also includes resources like Wikipedia, which offer crucial context for how all those individual data points are connected, and why. “Consumer AI models are very good at cases where they’ve seen a lot of examples on the internet. They’ve seen millions of example bar exams or code so they can pass the bar exam or code a website,” Rinke says. “But enterprise AI doesn’t get trained with examples of a company’s unique processes – how it makes products, pays suppliers, makes contracts with customers. That information is scattered across all these different systems, with no central repository of rules, desired processes and who’s responsible for what. All that is implicit in the organization.” The Celonis Process Intelligence Platform makes that knowledge explicit, and pulls together all that enterprise data sitting in IT systems such as ERP and CRM across the organization in many different form factors. The Celonis solution in particular gives that raw enterprise data what amounts to the Wikapediaesque cognate it needs to ground AI in business and process context. It provides the connective tissue that gives organizations the insight they need to identify powerful AI use cases and feeds AI with the process insights it needs to be useful, scalable and reliable. For instance, integrating process intelligence with generative AI means that answers to gen AI prompts are furnished using real-time process data and knowledge. And process intelligence can unlock the major benefits of AI agents, the next evolutionary step for AI, that are able to independently perform a series of interlinked tasks and make autonomous decisions along the way. Eventually networks of agents will be able to talk to one another to complete entire processes – for instance, getting a marketing deliverable reviewed and approved by legal, then releasing it to a customer channel, monitoring metrics and delivering a report. But that’s a lot of moving parts, with a lot of potential points of failure when organizations leap into agentic AI with their eyes closed. Process intelligence helps organizations identify the kinds of clearly defined and narrowly scoped problems AI agents are best at solving. That helps eliminate inconsistent responses or hallucinations, and the number of potential and actual dropped steps is slashed significantly when a process intelligence platform can monitor, track and flag agent decisions. AI and the process intelligence platform At the center of the Celonis Process Intelligence platform is the Process Intelligence Graph (PI Graph). Using process mining, it extracts process data from transactional systems (e.g., ERP, CRM, HCM) and brings them together into a data layer—a living digital twin of the business processes. The PI Graph combines this digital twin with a knowledge layer—the context mentioned above (i.e., what makes something “good” or “bad” for the organization) defined by KPIs, benchmarks, process models and so on. In short, it knows how processes run across the entire enterprise and shows people how they can run better. For example, in order management, a user can dig into an order process in progress, see how it’s related to the returns process, how it impacts the invoicing process, how it informs the sales process and so on. And to manage it all, the platform offers capabilities like dashboarding, app building, real-time monitoring, workflow automation, orchestration, alerts, root cause analysis and process optimization. In