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Why Your ERP Should Be Cloud-Based in 2026

Cloud-based enterprise resource planning (ERP) has become the preferred deployment model for many organizations, offering greater flexibility, scalability, and accessibility than traditional on-premises systems. In an article from Business.com, author Adam Uzialko explains why businesses are increasingly moving ERP workloads to the cloud and what benefits they can expect from the transition. The article notes that cloud ERP now dominates new deployments, with organizations attracted by lower infrastructure costs, easier maintenance, and the ability to support distributed workforces. Unlike on-premises ERP systems, cloud platforms eliminate the need for companies to manage servers, software updates, security patches, and backups internally. Instead, these responsibilities are handled by the vendor, allowing IT teams to focus on more strategic initiatives. A key advantage highlighted is accessibility. Employees can securely access financial, operational, and inventory data from virtually anywhere, making collaboration easier across locations and departments. Uzialko also emphasizes automatic updates, which help organizations stay current with new features and security enhancements without costly upgrade projects. Security is another major theme. While some businesses still believe on-premises systems offer greater protection, the article argues that leading cloud providers often invest more heavily in cybersecurity, compliance certifications, disaster recovery, and threat monitoring than most organizations can achieve on their own. The article also addresses common concerns around data ownership, internet dependence, and long-term costs, noting that businesses retain ownership of their data and often realize lower total cost of ownership over time. Uzialko concludes that cloud ERP is no longer just a technology trend—it has become the standard for organizations seeking greater agility, scalability, and operational efficiency in an increasingly digital business environment.

 

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AI-Ready Enterprise Data: The Missing Layer Between ERP Transformation and AI

Artificial Intelligence (AI) adoption is accelerating across the enterprise, but many organizations are discovering that enterprise resource planning (ERP) modernization alone does not make them AI-ready. In an article from ERP Today written by Lisa Dodson, it argues that a critical layer is often overlooked: creating trusted, governed, and AI-ready data that connects ERP systems to meaningful AI outcomes. The article explains that many companies have invested heavily in ERP modernization, cloud migrations, and process standardization, yet still struggle to deploy AI at scale. The problem is that AI depends on more than access to data—it requires data that is accurate, contextualized, governed, and accessible across the enterprise. Without that foundation, AI initiatives often produce inconsistent insights, limited business value, or fail to move beyond pilot projects. A key theme is the need for an AI-ready data layer between ERP systems and AI applications. This layer helps unify information from multiple systems, establish governance and lineage, and ensure that AI models operate on trusted business data rather than fragmented datasets. The goal is to provide the context AI needs to support decision-making, automation, and operational intelligence. Dodson also emphasizes that organizations should view ERP transformation and AI readiness as connected initiatives rather than separate projects. Modernizing processes without addressing data quality, ownership, and governance can leave businesses with sophisticated systems that are still unprepared for enterprise AI. Ultimately, Dodson stresses that AI success depends on building a strong data foundation. Organizations that invest in AI-ready enterprise data will be better positioned to turn ERP modernization efforts into scalable AI-driven business outcomes.

 

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Changing the Portal Timeout Property

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To change the TimeoutRedirectDisable property, you need to delete this using ssoconfig and re-add it with the correct value. this needs a restart to take effect.

 

Follow these steps to add the service properties on Infor Lawson Server:

  1. Access the Infor Lawson server with the lawson user
  2. Enter ssoconfig -c and provide the password
  3. Select 5 Manage Lawson Services
  4. Select 10 Manage Service Properties
  5. Select 2 Delete Service Property
  6. Enter the TimeoutRedirectDisable that is set to True
    • Enter the SERVICE NAME: SSOP
    • Enter the service PROPERTY NAME: TimeoutRedirectDisable
  7. Select 10 to Manage Service Properties again (or go back to step 1)
  8. Select 1 to Add new service property
    • Enter the SERVICE NAME: SSOP
    • Enter the service PROPERTY NAME: TimeoutRedirectDisable
    • Enter the service PROPERTY VALUE: False
  9. Select 5 Exit

 

Restart LSF.

Treat every data migration like it will go wrong

Data migrations are often treated as routine technical tasks, but they carry far more risk than many teams assume. In an article shared by software engineer and data analyst Sumit Gundawar on Dataconomy.com, he argues that the safest and most effective approach is to assume that every migration has the potential to fail—and to plan accordingly. A central idea in the article is that data migrations fail quietly compared to application failures. While software bugs typically trigger visible errors or system crashes, data migration issues can silently corrupt records, overwrite values, or introduce inconsistencies without immediate warning. The system may appear to run normally while decisions are being made on incorrect data. Gundawar stresses that this risk is amplified when teams run “one-off” scripts or quick fixes directly in production without proper testing, validation, or review. These shortcuts often bypass the safeguards that exist in application development, even though migrations directly impact the organization’s most critical asset: its data. To mitigate this, he recommends adopting a “failure-first” mindset. This includes rigorous testing in safe environments, peer review of migration scripts, validation checkpoints, and clear rollback plans. It also means treating data changes with the same discipline as production code deployments, rather than ad hoc administrative work. Ultimately, Gundawar reinforces that data migrations should never be assumed safe by default. Organizations that anticipate failure, build in safeguards, and validate aggressively are far more likely to avoid silent data corruption and long-term operational issues.

 

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How Master Data Governance Guides the Path to Actionable Insights

Master Data Management (MDM) is increasingly being used as a foundation for better analytics and operational decision-making, but its real value comes from turning clean data into actionable insights. In an article from SSON Network titled “Master Data Management: Actionable Insights,” the author, Nishchinth Dewar, explains how organizations can move beyond basic data governance and use MDM to actively improve business performance. The article highlights that many companies struggle with fragmented, inconsistent, or duplicate data across systems, which limits their ability to make confident decisions. MDM addresses this by creating a single, trusted view of core business data—such as customers, vendors, and products—so organizations can rely on consistent information across departments and processes. A key theme is that MDM is not just a data cleanup exercise, but a driver of continuous improvement. When master data is accurate and well-governed, it enables better visibility into operations, more reliable reporting, and stronger alignment between business units. This, in turn, supports more informed decision-making and helps organizations identify inefficiencies or opportunities more quickly. Dewar also emphasizes the importance of embedding MDM into broader data governance and continuous improvement initiatives. Rather than treating it as a one-time project, organizations should view it as an ongoing capability that evolves with business needs, ensuring data remains accurate, standardized, and actionable over time. Dewar reinforces that effective MDM is about more than managing data—it’s about enabling better decisions. Organizations that invest in strong master data practices are better positioned to unlock insights, improve operational efficiency, and drive sustained business value.

 

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Fixing “Invalid S3 Location” Errors When Running Athena Queries with Boto3

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If you’ve ever run an Amazon Athena query using Boto3 and received an error stating that the S3 output location is invalid, you may have double-checked everything only to find:

  • The S3 bucket exists
  • The bucket is in the correct AWS region
  • The output prefix is valid
  • Permissions look correct

Yet the query still fails.

This is a surprisingly common issue, and the root cause is often not the S3 path itself.

Why This Error Happens

Athena is a regional service. When you start a query, it runs in a specific AWS region, and both of the following must be in that same region:

  1. The Athena database
  2. The S3 bucket used for query results

What trips people up is that the region used by the Boto3 Athena client determines where the query runs.

If the client is created without explicitly specifying a region, Boto3 falls back to:

  • Your AWS CLI default region, or
  • Environment configuration

If that default region does not match where your Athena database lives, Athena may report errors such as:

  • “Invalid S3 location”
  • “Database does not exist”

Even when the S3 bucket itself is perfectly valid.

The Key Fix: Specify the Region Explicitly

To avoid ambiguity, always create your Athena client with an explicit region that matches your Athena database:

import boto3

 

athena = boto3.client(

‘athena’,

region_name=’us-west-2′  # must match your Athena database region

)

This ensures:

  • The query runs in the correct Athena region
  • The S3 output location is evaluated correctly
  • Athena can see both the database and the results bucket

Additional Best Practices

  1. Always Use a Trailing Slash for OutputLocation

Athena expects the output location to be a directory, not a file:

ResultConfiguration={

‘OutputLocation’: ‘s3://your-results-bucket/athena/’

}

  1. Verify S3 Permissions

The IAM role or credentials running the query must be able to write to the bucket:

  • s3:PutObject
  • s3:GetBucketLocation
  • (Optionally) s3:ListBucket

Athena will create the output prefix automatically, but it must have permission to do so.

  1. Don’t Assume S3 Region Is Enough

Even if your S3 bucket is in the correct region, Athena still fails if:

  • The Athena client is created in a different region
  • The database exists elsewhere

Will Explicitly Setting the Region Break Other Regions?

No. Explicitly setting region_name will not fail just because another region exists.

It only fails if:

  • The Athena database is not in that region, or
  • The S3 results bucket is not in that region

As long as all three align—client, database, and S3 bucket—the query will work.

Final Takeaway

When you see an “invalid S3 location” error in Athena, don’t assume the bucket is the problem.

More often than not, the issue is that:

Athena is running in the wrong region.

Explicitly setting the region on your Boto3 Athena client is the simplest and most reliable fix—and it prevents a lot of head-scratching later.

Why organizational change management is critical to ERP transformation success

Enterprise Resource Planning (ERP) transformations are often viewed as technology projects, but their success depends just as much on people as it does on software. In an article from The Manufacturer, it argues that many ERP initiatives fail to deliver lasting value because organizations underestimate the scale of change required across employees, processes, and ways of working. The article uses the example of Litehouse Foods, which implemented a new ERP platform to support growth and improve operational scalability. While the technology provided a foundation for modernization, the company quickly realized that system implementation alone would not guarantee success. Preparing employees for new processes, responsibilities, and workflows became equally important. A key theme is that Organizational Change Management (OCM) should be treated as a strategic workstream rather than a supporting activity. Effective change management helps organizations align teams, build confidence, encourage adoption, and create long-term operational value from ERP investments.

The article highlights four practical steps for success:

  • Define change as a strategic workstream — Establish ownership, governance, and objectives from the start.
  • Engage stakeholders early — Involve cross-functional teams in shaping future processes and identifying risks.
  • Prioritize communication and training — Help employees understand what is changing, why it matters, and how it affects their roles.
  • Track and manage change activity — Use structured governance to drive accountability and consistent adoption.

ERP transformation is a business transformation. Organizations that invest in structured change management are better positioned to improve adoption, align teams, and realize long-term value from their ERP investments.

 

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ERP Modernization: Why Finance Leaders Are Moving to the Cloud

Finance leaders are increasingly turning to cloud enterprise resource planning (ERP) as they face growing pressure to deliver real-time insights, improve agility, and support strategic decision-making. An article from TechFunnel explains why cloud ERP has become a key priority for finance organizations in 2026. The article argues that many legacy ERP systems were built for a different era—one focused on recording and reporting data rather than supporting real-time business decisions. As market conditions, compliance requirements, and operational complexity continue to evolve, finance teams need faster access to information and greater flexibility than traditional on-premises systems can provide. A major driver behind cloud adoption is real-time visibility. Cloud ERP platforms help finance teams unify data, automate routine processes, and gain immediate insight into cash flow, performance, and forecasting. The article also highlights the growing role of AI and automation, which are helping organizations streamline activities such as financial close, invoice processing, and predictive analysis. Another key theme is compliance. Modern cloud ERP systems increasingly incorporate regulatory reporting, auditability, and sustainability tracking into core workflows, reducing the need for manual processes and disconnected tools. The piece also notes that successful modernization requires more than technology. Data governance, reducing legacy customizations, and investing in change management are critical factors in achieving long-term value from a cloud migration. Ultimately, the article positions cloud ERP not as an IT upgrade, but as a strategic finance initiative. Organizations that modernize successfully can spend less time managing systems and more time driving business performance, forecasting, and growth.

 

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How to fix Lawson RQC Account Cost Capped

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You may sometimes find that your account cost is capped or budget exceeded, causing an error in Lawson RQC. Refer to these simple and easy steps to learn how to fix Lawson RQC Account Cost Capped.

In RQC (Lawson Requisition Center), the cost is capped at 502.86 as shown in the screen shot below.

 

To adjust this, in the Lawson portal, go to RQ04 and Inquire on the Requester.

Then set these two flags to “Yes” to allow overriding costs (see screen shot below).

That is all there is to it! Cap should be lifted now in Requisition Center for the requester.

 

The Next Era of ERP Will Look Nothing Like the Last One

The enterprise resource planning (ERP) landscape is entering a period of significant change, driven by artificial intelligence (AI), automation, and the growing demand for business agility. In a recent article from Solutions Review by writer, editor and analyst William Jepma, it argues that future ERP systems will be far more intelligent, connected, and adaptable than the platforms organizations have relied on for decades. A key theme of the article is the shift away from traditional ERP systems that primarily served as systems of record. The next generation of ERP is expected to function as a system of intelligence, using AI and automation to provide recommendations, streamline workflows, and support real-time decision-making across the enterprise. Jepma also highlights the move toward more modular and composable architectures. Rather than relying on large, monolithic platforms, organizations are increasingly adopting flexible ecosystems that allow them to integrate specialized applications and services while maintaining a connected data foundation. This approach helps businesses respond more quickly to changing market conditions and evolving customer expectations. Another major takeaway is the growing importance of data quality and governance. As AI becomes more deeply embedded in ERP processes, organizations will need accurate, trusted data to generate reliable insights and automate decisions effectively. Without strong data foundations, even the most advanced technologies may fail to deliver meaningful business value. Jepma also suggests that ERP implementations will become more focused on continuous evolution rather than large-scale, once-a-decade transformation projects. Businesses will increasingly adopt incremental improvements, cloud-based innovation, and AI-driven capabilities as part of an ongoing modernization strategy. Moreover, the future of ERP is not simply about managing transactions. It is about creating intelligent, adaptable platforms that help organizations make faster decisions, automate complex processes, and respond to change with greater speed and confidence.

 

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