After a very long winter, the Artificial Intelligence (AI) spring is here. Or, so it seems over the last three years. AI has the potential to impact many areas of human life - personal, social, health, education, professional. In this paper, we take a closer look at the potential of AI for Enterprises, where decision-making plays a crucial and repeated role across functions, tasks, and operations. We consider Agents imbued with AI as means to increase decision-productivity of enterprises. We highlight six tenets for Agentic success in enterprises, by drawing attention to what the current, AI-Centric User paradigm misses, in the face of persistent needs of and usefulness for Enterprise Decision-Making. In underscoring a shift to User-Centric AI, we offer six tenets and promote market mechanisms for platforms, aligning the design of AI and its delivery by Agents to the cause of enterprise users.

Over the past year, veteran software engineer Jay Prakash Thakur has spent his nights and weekends prototyping AI agents that could, in the near future, order meals and engineer mobile apps almost entirely on their own. His agents, while surprisingly capable, have also exposed new legal questions that await companies trying to capitalize on Silicon Valley's hottest new technology. Agents are AI programs that can act mostly independently, allowing companies to automate tasks such as answering customer questions or paying invoices. While ChatGPT and similar chatbots can draft emails or analyze bills upon request, Microsoft and other tech giants expect that agents will tackle more complex functions--and most importantly, do it with little human oversight. The tech industry's most ambitious plans involve multi-agent systems, with dozens of agents someday teaming up to replace entire workforces.

NVIDIA AI Enterprise 2.1 is now generally available for all enterprise users. Today, the global technology leader NVIDIA announced the most advanced version of its AI-powered data and analytics software for enterprise users. The new AI suite would enable users to fully-optimize their IT and Low Code DevOps processes in a highly scalable AI-based environments. These include applications across bare metal, virtual, container, and Cloud environments. The latest NVIDIA AI Enterprise 2.1 is part of NVIDIA's AI enterprise suite.

It's the key to nimbly adapting to the sometimes seismic shifts in business climates that unexpectedly arise. But according to Indico Data CEO Tom Wilde, it's something altogether else that could very well be of even more importance to firms today. "All organizations, it doesn't matter what industry you're in, recognize that their unique ability to codify the work that they do is a competitive advantage," Wilde explained. "That codification comes from the kind of investments they made in technology and the employee experience and customer experience." Investments in composable AI solutions enable the sort of codification Wilde referenced while allowing firms to build applications, workflows, and business processes with a modular approach that's rapidly interchangeable to suit the particularities of any use case--or business condition--that arises.

In a follow-up to new compute, network and data service offerings announced by Amazon Web Services (AWS) CEO Adam Selipsky, AWS vice president of AI, Swami Sivasubramanian, pulled the covers off some updates to database, machine learning and serverless offerings. Taking a cue from Selipsky's theme of simplifying AWS' array of services in order to make them easier to consume for developers and enterprises, Sivasubramanian announced three new updates to AWS' plethora of database offerings. They include a new managed database service for business applications that allows developers and enterprises to customise the underlying database and operating system; a new table class for Amazon DynamoDB designed to reduce storage costs for infrequently accessed data; and a service that uses machine learning to better diagnose and remediate database-related performance issues. The new managed database service, Amazon RDS (Relational Database Service) Custom, is aimed at customers whose applications require customisation at the database level and thus are responsible for administrative tasks such as provisioning, database setup, patching and backups that take up a lot of time, Sivasubramanian said. Amazon RDS Custom automates these administrative processes while allowing customisation to the database and underlying operating system these applications require, Sivasubramanian said.

Data science has surged to the forefront of the data ecosystem, with demonstrable business value derived from the numerous expressions of Artificial Intelligence currently being adopted in the enterprise. It represents the nucleus of the power of predictive analytics, and the extension of data culture throughout modern organizations. Consequently, data science trends are more impactful than those in other data management domains, which is why its increasing consumerization (beyond the realm of data scientists) is perhaps the most meaningful vector throughout IT today. "You can't find the data scientist talent to build models? Well, how about if those models can be built by a business analyst with one mouse click and one API call?" asked Oliver Schabenberger, CTO and COO at SAS, in conversation with AI Business.

I usually write about how to integrate and launch ML/AI in consumer-facing products. However, a large part of my job is building ML/AI developer tools, some of which are open sourced. In this field there is a proliferation of startups whose tagline is a random pick from all permutations of the words deep learning, platform, enterprise, deployment, training, scale, democratize. Their offerings span from data acquisition (data annotation by humans) to data science workbench environments and hosted model deployment. After speaking with many startups and investors about deep learning developer tools, I felt that it would be useful to share some of my thoughts more broadly.

Google is adding four new security measures to protect Gmail business users from spam, phishing, data loss, ransomware, and other workplace security threats. "Email attacks are constantly evolving, and the email attack vector is by far the preferred way for attackers to gain access to enterprise data," says Gmail product manager Sri Somanchi. "We see all kinds of attacks, including phishing, malware, and ransomware attacks." Machine learning is a common theme in today's updates. Google reports about 50-70% of the messages Gmail receives are spam, and machine learning helps block it with over 99.9% accuracy.

To complement existing offerings like Sparkling Water and Steam, H2O.ai is releasing Deep Water, a new tool to help businesses make deep learning a part of everyday operations. Deep Water will open up new possibilities for the TensorFlow, MXNet and Caffe communities to engage with H2O.ai. This also means that the GPU is set to become a greater part of business operations for the entire Fortune 500, not just tech companies. SriSatish Ambati, CEO of H2O.ai, says his company has found a sweet spot with predictive analytics. Ambati gave me the example of an insurance provider using H2O to analyze images of roofs and provide insights for preventative maintenance.

Enterprise cloud usage has been in the forefront of big players for the past few years. Amazon, IBM, Google and Microsoft are expanding their offerings to serve better the enterprise users and their needs. Google announced a set of machine learning based services focused on enterprise users. Similar to upcoming Amazon EC2's Elastic GPUs and Microsoft's Azure N-Series, powered by NVidia GPUs, Google will soon offer cloud based GPUs with per minute billing focused on Machine Learning tasks. Google slashed pricing for its Cloud Vision API to 1/5, offering face, label, OCR, company logos, explicit content and landmark and image properties recognition through off the shelf algorithms and their API.