Bridging the Knowledge Gap in Manufacturing: Securing Institutional Expertise for the Future

The manufacturing sector is at a crossroads. While investments in new technologies and infrastructure are essential, they must also be utilized to preserve and share institutional knowledge. By adopting a comprehensive KM strategy that includes centralized data management, digital innovation, and a culture of knowledge sharing, manufacturers can safeguard their expertise and secure long-term success.

Navigating the IT Landscape: Balancing Hybrid Cloud and Cloud Repatriation to Stay in Control of Your Infrastructure

To secure their data, certain industries have strict regulations regarding data storage, requiring data to be kept in specific geographic locations or under stringent security measures. This is yet another reason for organizations nowadays to switch back to on-prem resources.

Experts predict AI will continue impacting KM in 2025

AI continues to disrupt the knowledge management space and experts in the field predict that it's a trend that still hasn't reached its full potential, yet. In 2025 there's more room for improvement.

Integration impasse: Why organizations can’t wait for data integration before deploying AI

The need for comprehensive data management will always be important, and there are many other benefits of digital transformation, but CIOs don't need to delay GenAI projects until the completion of a giant data centralization effort. By adopting a more flexible approach that incorporates GenAI and next-generation BI tools, businesses can navigate the complexities of modern data ecosystems while driving innovation and maintaining a competitive edge in an AI-driven world.

The transformative role of AI in the next generation of records management

While there are many ways AI will disrupt and advance the records management process, these four key applications will make the biggest impact: automating document classification and tagging, records retention and data hygiene, leveraging natural language processing for record analysis and predictive analytics for records management.

Navigating the risks and challenges of AI (quickly): Create an AI governance program

A strong AI governance program is essential to ensuring compliance and reducing risk. An equally important benefit is that by developing the governance program at the same time the AI application is being developed, issues can be identified early, thus avoiding system redesign or rework on the tail end.

Democratizing software development with no code/low code

By enabling greater productivity and accelerated software development timelines, no code/low code is on the rise.

What you should know about cross-border data transfer laws

Multinational companies are generally aware of data transfer laws, but smaller ones just embarking on looking beyond country borders may not be.

Microsoft’s Copilot: A force multiplier for KM

Generative AI (GenAI) applications will increasingly transform organizations' IT platforms. Companies of any size that opt to create robust apps on their own, however, are in for a protracted, complex, and expensive experience.There's a better way: Buy into what I call a GenAI ecosystem from a vendor in whose tech you are already invested. These ecosystems are comprised of the sum of services customers mostly need to build and launch robust apps.

KM and AI: Experts look at what lies ahead for 2024

AI and dreams of its potential rocked this past year as companies moved quickly to embed and offer their own version of chat assistants, predictive and generative AI, and more

Decoding the data deluge; use ECM to navigate the unstructured content labyrinth

Obtaining contextual knowledge about how users create and interact with unstructured data is an essential business practice to plan for data storms effectively. Enterprises need to understand that it's not enough to know these user activities but also must take remediation action when necessary.

Avoiding the wrath of illegal data

To mitigate the risks associated with illegal data, organizations need to implement data governance policies and procedures to ensure that all data is properly documented, stored, and maintained.

Accounts payable is at the heart of company success— but is yours skipping a beat?

With a solid strategy for AP automation, businesses can expedite invoice processing rates by as much as 90%, which equates to a 400% increase in employee productivity. But how do you get there?

Best P2P practices and their benefits

Using P2P automation solutions, organizations can maximize the productivity of the whole procurement team and streamline the whole process in the most effective way.

Why SaaS visibility matters

With the increase of decentralized workplaces and remote and hybrid work models, SaaS has overtaken on-premises apps and become critical to any company's success

5 Knowledge graph insights from KMWorld Connect 2021

Knowledge graphs continue to make a splash in the KM space

The solution for the third-party data challenge

Now is the time to perform an audit to understand where your organization is at risk and institute a robust first-party data policy

Analytics in action: Where do you land on the data maturity curve?

These five stages can help define priorities and smart steps for long-term business impact 

Why there has never been a better time to go paperless

If there is any hesitation about going paperless, here are three of the top reasons to start making the transition today

MDM and data governance: What comes first, the chicken or the egg?

In a very real sense, data governance is the "business function" of MDM, controlling how data is created, collected, and used