The “series A” round was led by Andreessen Horowitz (a16z), with participation from Y Combinator and strategic industry investors, including RyderVentures. It follows an earlier, previously undisclosed, pre-seed round raised 1.5 years ago, that was backed by Array Ventures and other angel investors.
“Our mission is to redefine the economics of the freight industry by harnessing the power of agentic AI,ˮ Pablo Palafox, HappyRobotʼs co-founder and CEO, said in a release. “This funding will enable us to accelerate product development, expand and support our customer base, and ultimately transform how logistics businesses operate.ˮ
According to the firm, its conversational AI platform uses agentic AI—a term for systems that can autonomously make decisions and take actions to achieve specific goals—to simplify logistics operations. HappyRobot says its tech can automate tasks like inbound and outbound calls, carrier negotiations, and data capture, thus enabling brokers to enhance efficiency and capacity, improve margins, and free up human agents to focus on higher-value activities.
“Today, the logistics industry underpinning our global economy is stretched,” Anish Acharya, general partner at a16z, said. “As a key part of the ecosystem, even small to midsize freight brokers can make and receive hundreds, if not thousands, of calls per day – and hiring for this job is increasingly difficult. By providing customers with autonomous decision making, HappyRobotʼs agentic AI platform helps these brokers operate more reliably and efficiently.ˮ
Many AI deployments are getting stuck in the planning stages due to a lack of AI skills, governance issues, and insufficient resources, leading 61% of global businesses to scale back their AI investments, according to a study from the analytics and AI provider Qlik.
Philadelphia-based Qlik found a disconnect in the market where 88% of senior decision makers say they feel AI is absolutely essential or very important to achieving success. Despite that support, multiple factors are slowing down or totally blocking those AI projects: a lack of skills to develop AI [23%] or to roll out AI once it’s developed [22%], data governance challenges [23%], budget constraints [21%], and a lack of trusted data for AI to work with [21%].
The numbers come from a survey of 4,200 C-Suite executives and AI decision makers, revealing what is hindering AI progress globally and how to overcome these barriers.
Respondents also said that many stakeholders lack trust in AI technology generally, which holds those projects back. Over a third [37%] of AI decision makers say their senior managers lack trust in AI, 42% feel less senior employees don’t trust the technology., and a fifth [21%] believe their customers don’t trust AI either.
“Business leaders know the value of AI, but they face a multitude of barriers that prevent them from moving from proof of concept to value creating deployment of the technology,” James Fisher, Chief Strategy Officer at Qlik, said in a release. “The first step to creating an AI strategy is to identify a clear use case, with defined goals and measures of success, and use this to identify the skills, resources and data needed to support it at scale. In doing so you start to build trust and win management buy-in to help you succeed.”
The “D&B Ask Procurement” product works by synthesizing vast datasets and providing intelligent recommendations, according to Dun & Bradstreet, which calls itself a provider of business decisioning data and analytics.
It was built with IBM’s watsonx Orchestrate and watsonx.ai technology with support from IBM Consulting, and connects to Dun & Bradstreet’s business risk, financial, and firmographic data and insights. It then uses a conversational chat interface to provide advanced reasoning capabilities and autonomous decision making, helping teams to query critical supplier insights, expedite analysis and reporting, and identify suppliers for engagement, the partners said.
“One key point of entry for Gen AI adoption is AI assistants, and together IBM and Dun & Bradstreet are collaborating to bring clients new innovations within the procurement domain,” Parul Mishra, Vice President of Product Management, Digital Labor at IBM, said in a release. “With D&B Ask Procurement, an AI assistant built on the foundation of watsonx Orchestrate, users can seamlessly complete tasks and automate complex processes with natural language, helping drive efficiency, cost-savings and higher productivity.”
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."
A growing number of organizations are identifying ways to use GenAI to streamline their operations and accelerate innovation, using that new automation and efficiency to cut costs, carry out tasks faster and more accurately, and foster the creation of new products and services for additional revenue streams. That was the conclusion from ISG’s “2024 ISG Provider Lens global Generative AI Services” report.
The most rapid development of enterprise GenAI projects today is happening on text-based applications, primarily due to relatively simple interfaces, rapid ROI, and broad usefulness. Companies have been especially aggressive in implementing chatbots powered by large language models (LLMs), which can provide personalized assistance, customer support, and automated communication on a massive scale, ISG said.
However, most organizations have yet to tap GenAI’s potential for applications based on images, audio, video and data, the report says. Multimodal GenAI is still evolving toward mainstream adoption, but use cases are rapidly emerging, and with ongoing advances in neural networks and deep learning, they are expected to become highly integrated and sophisticated soon.
Future GenAI projects will also be more customized, as the sector sees a major shift from fine-tuning of LLMs to smaller models that serve specific industries, such as healthcare, finance, and manufacturing, ISG says. Enterprises and service providers increasingly recognize that customized, domain-specific AI models offer significant advantages in terms of cost, scalability, and performance. Customized GenAI can also deliver on demands like the need for privacy and security, specialization of tasks, and integration of AI into existing operations.
Progress in generative AI (GenAI) is poised to impact business procurement processes through advancements in three areas—agentic reasoning, multimodality, and AI agents—according to Gartner Inc.
Those functions will redefine how procurement operates and significantly impact the agendas of chief procurement officers (CPOs). And 72% of procurement leaders are already prioritizing the integration of GenAI into their strategies, thus highlighting the recognition of its potential to drive significant improvements in efficiency and effectiveness, Gartner found in a survey conducted in July, 2024, with 258 global respondents.
Gartner defined the new functions as follows:
Agentic reasoning in GenAI allows for advanced decision-making processes that mimic human-like cognition. This capability will enable procurement functions to leverage GenAI to analyze complex scenarios and make informed decisions with greater accuracy and speed.
Multimodality refers to the ability of GenAI to process and integrate multiple forms of data, such as text, images, and audio. This will make GenAI more intuitively consumable to users and enhance procurement's ability to gather and analyze diverse information sources, leading to more comprehensive insights and better-informed strategies.
AI agents are autonomous systems that can perform tasks and make decisions on behalf of human operators. In procurement, these agents will automate procurement tasks and activities, freeing up human resources to focus on strategic initiatives, complex problem-solving and edge cases.
As CPOs look to maximize the value of GenAI in procurement, the study recommended three starting points: double down on data governance, develop and incorporate privacy standards into contracts, and increase procurement thresholds.
“These advancements will usher procurement into an era where the distance between ideas, insights, and actions will shorten rapidly,” Ryan Polk, senior director analyst in Gartner’s Supply Chain practice, said in a release. "Procurement leaders who build their foundation now through a focus on data quality, privacy and risk management have the potential to reap new levels of productivity and strategic value from the technology."