A critical step in digital transformation is to enable the free flow of information throughout the enterprise. But various forms of friction can obstruct this flow. Friction is anything that slows down information access, information retrieval or information manipulation. One of the most obvious sources is the ongoing use of paper, which is still present in a surprisingly large number of business processes. Other sources of friction include inconsistent data architecture and naming conventions, poorly understood or user-unfriendly applications, the need for manual conversions or manipulations, and answering the same questions over and over again rather than resolving them and operationalizing the answer.

Integrating AI effectively enables businesses to learn and act on information, making predictions, automating operations and optimizing logistics. AI might add $16T to the global economy by 2030, yet 81% of company leaders don't comprehend the data and infrastructure needed. Modernizing information architectures (IA) with AI requires a prescriptive, strategic approach. Successful AI models require data gathering and organization; uniform and open information architecture is needed to prepare enterprises for an AI and multi-cloud environment.

AI adoption is critical for business success in the changing, competitive market. A HIMMS and IBM study found that 64% of respondents said their organizations placed a “critical” or “high” strategic priority on AI. However, many organizations are still reluctant to fully adopt AI into their processes and decision-making for fear of the unknown. “To many who aren’t data scientists, AI is still a black box and that scares us,” says Kelly Combs, Director of Emerging Technologies Risk Services at KPMG. So how do organizations go about establishing trust in their AI? The answer lies in leadership buy-in, automated checks and balances, and access to clean and complete data. Here are three ways organizations can work to establish trust in their AI: 1. Unite your people and processes around strategic AI through education and information architecture Culture and strategy often trickle from the top of an organization down. This is why it is important to rally leaders and…

"Two roads diverged in a wood and I – I took the one less traveled by." It may not be obvious at first, but Robert Frost's poem about standing at the crossroads of choice applies particularly well to the telecommunications industry. With the rise of 5G cellular networks and the need for more agility during COVID-19, Telco companies have to make a crucial decision: stay in the traditional lanes of providing connectivity or evolve with AI-powered digital transformation. For many Telcos, the "road less traveled" via AI isn't just a question of innovation; it's critical to developing new business models that are sustainable and scalable for the future. The Telco industry's AI reinvention lies in three key strategies: monetizing at the edge, saving costs through automation and improving customer engagement.

Clint Coghill co-founded Backstop Solutions Group in 2003 and now leads the Backstop Executive Team as CEO. Thanks to the sheer amount and complexity of data that is generated each day, the office of the chief investment officer has effectively expanded into an intelligence unit in recent years. Data is flowing in from multiple sources, including custodians, fund administrators, consultants and managers, to name just a few, and that data is flowing into inboxes and shared drives in tens of thousands of emails per year. All of that information could live in any number of functional and technology silos within a typical investment firm, while at the same time, complex assets often have life cycles that outlast investment staff, leading to issues around knowledge transfer and data continuity. Machine learning and artificial intelligence (AI) can absolutely be valuable tools in collecting, analyzing, managing and putting that vast amount of information to use.

This article first appeared in IEEE Software magazine. IEEE Software offers solid, peer-reviewed information about today's strategic technology issues. To meet the challenges of running reliable, flexible enterprises, IT managers and technical leads rely on IT Pro for state-of-the-art solutions. Artificial intelligence (AI) is increasingly hyped by vendors of all shapes and sizes-from well-funded startups to the well-known software brands. Financial organizations are building AI-driven investment advisors1. Chat bots provide everything from customer service2 to sales assistance3.

We are entering a new chapter in Artificial Intelligence (AI) adoption in the enterprise. Capabilities are advancing rapidly, case studies emerging every day and research accelerating quickly. Frankly the fight against the Novel Coronavirus and the new normal has changed our dystopian perspective on AI to one that is of a human-machine partnership to combat the invisible enemy. The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business. Based on my own conversations with leaders, partners and practitioners in the past year, AI is now a fundamental element of the next generation business fabric.

IBM Thursday introduced new storage hardware and software aimed at placing its storage at the center of large-scale data requirements for artificial intelligence and analytics workloads. The new offerings are aimed at helping to build the kind of information architecture needed to get the most out of businesses' fast-changing data, said Eric Herzog, IBM's chief marketing officer and vice president of worldwide storage channels. "The new stuff is all about storage solutions for AI, big data and business analytics," Herzog told CRN. "IBM thinks customers need an information architecture to build AI before they can collect and analyze their data and feed it into their AI systems." IBM storage technology has always been an important part of customers' high-performance computing, artificial intelligence and machine-learning infrastructures, said John Zawistowski, global systems solutions executive at Sycomp, a Foster City, Calif.-based solution provider and IBM channel partner. "Why IBM? It's the way they integrated the AI software platform and storage," Zawistowski told CRN. "And the way IBM understands the importance of doing that. And the way IBM technology performs."

Information architecture is the study of how information is created, managed, and organized. The term was coined by Richard Saul Wurman in 1976 when he first combined the word'information' and'architecture'. He later clarified in 2004 that he chose to use the word architecture instead of design to help focus on system and structure rather than visuals. Since then, information architecture has been widely used to help structure the growing amount of information on the world wide web. It has evolved from systems and labels to strategic placements that help a user find information and navigate a website effectively.

Artificial intelligence (AI), when integrated correctly, enables organizations to learn and then act on information--powering businesses to make predictions, automate processes and optimize logistics. Although AI has the potential to add almost $16T to the global economy by 2030, 81 percent of business leaders do not understand the data and infrastructure required for AI. Businesses need a prescriptive, strategic approach to successfully modernize their information architectures (IA) with AI. Successful AI models rely on collection and organization of data--a unified and open information architecture is necessary to prime data and ready businesses for an AI and multicloud world. The AI ladder gives organizations a set of guiding principles for the four areas of AI: how to collect, organize and analyze data, and ultimately, how to infuse AI into business practices.