Category New: Artificial Intelligence
Modernizing Healthcare with AI-Driven ERP
Healthcare faces unprecedented challenges: declining reimbursements, workforce shortages, and regulatory complexity converge with rising patient expectations for personalized, transparent care. In this environment, healthcare ERP systems powered by artificial intelligence (AI) are becoming a cornerstone of survival and growth. Modern platforms are redefining how organizations achieve compliance, operational agility, and data-driven decision-making.
Challenges Driving Innovation in Healthcare Operations
Five seismic shifts are reshaping healthcare operations, as identified by a recent industry analysis by Sage Intacct:
- Cloud migration: 89% of healthcare providers now prioritize real-time data access across distributed teams.
- Consolidation: Mergers and acquisitions have tripled since 2020, creating complex multi-entity structures.
- Cost pressures: Reimbursement rates fell 4.2% annually since 2021, forcing 67% of CFOs to adopt granular cost-per-patient analytics.
- Regulatory evolution: 23 states introduced new Medicaid reporting requirements in 2024 alone.
- Consumerization: 82% of patients now compare healthcare experiences to retail standards, demanding price transparency and digital engagement.
Legacy ERP systems can exacerbate these challenges. For one Sage Intacct customer who migrated from QuickBooks, their manual processes were causing 14-day delays in intercompany reconciliations during a merger. Conversely, organizations using AI-enhanced ERP systems report 40% faster decision-making and 30% lower compliance costs.
AI-Enhanced ERP: The Key to Smarter, Faster Decision-Making through Healthcare-Specific Tools
Sage Intacct’s healthcare-specific ERP tools automate compliance through:
Advanced Audit Trail: Tracks every PHI access attempt with user/IP timestamps, reducing HIPAA audit prep from weeks to hours.
Automated Business Associate Agreements: Unlike generic solutions, Sage Intacct signs Business Associate Agreements, mitigating legal risks for eligible clients.
Multi-State Adaptability: The ERP can auto-generate Medicaid/Medicare reports across jurisdictions.
Penalty prevention is quantifiable: Organizations using these tools reduced HIPAA fines by 92% post-implementation, with one surgical robotics firm slashing breach response times by 70%.
Operational Efficiency: From Survival to Strategic Growth
AI-powered ERP systems transform financial operations through:
1. Intelligent Automation
AP/AR Optimization: A home care organization automated 70% of its paper-based processes, enabling remote work during crises.
Dynamic Spend Controls: Role-based dashboards at healthcare practices trigger alerts when departments exceed budget thresholds, cutting variances by 50%.
Multi-Entity Management: A treatment center unified 14 facilities under a single ledger, reducing monthly close cycles by 40% and saving $1 million annually.
2. Predictive Financial Modeling
Machine learning analyzes historical and real-time data to:
Forecast patient volumes with 85% accuracy, optimizing staffing and inventory.
Predict claim denials pre-submission, reducing rejections by 30%.
Model reimbursement impacts of potential mergers, as used by a mental health facility during expansion.
Real-Time Visibility Through Role-Based Intelligence
Custom dashboards align insights with stakeholder needs:
CFOs: Track net revenue per case, days cash on hand, and labor cost ratios.
Clinic Managers: Monitor patient wait times, no-show rates, and supply burn rates.
Board Members: Access consolidated ROIs for capital equipment purchases.
A mental health facility reduced manual Excel work by 90% after implementing dynamic dashboards, enabling real-time budget reforecasting during COVID.
Security in the AI-ERP Era
A modern healthcare ERP such as Sage Intacct includes security features that address critical vulnerabilities:
HIPAA-Certified Encryption: Multi-layered protection for PHI in financial systems.
Behavioral AI: Flags anomalous EHR access patterns in real-time.
Strategic Partnerships for Sustainable Innovation
The Healthcare Financial Management Association (HFMA) Peer Review process—passed by only 60% of applicants—validates Sage Intacct’s healthcare expertise through:
- Customer interviews confirming ROI within 6 months
- Technical demonstrations proving multi-entity scalability
- Legal reviews ensuring ongoing HIPAA/HITECH alignment
As Richard L. Childs, HFMA reviewer, notes: “The Peer Reviewed label reassures organizations that solutions meet rigorous operational and ethical standards.”
Building a Data-Driven Healthcare Future
The convergence of AI and ERP systems represents healthcare’s next evolutionary leap. Platforms like Sage Intacct demonstrate that when financial, operational, and clinical data unite in a HIPAA-secure cloud environment, organizations gain:
- 35% faster growth cycles through predictive modeling
- 50% reduction in compliance incidents
- 22% higher patient satisfaction via personalized care resourcing
Act Now with Net at Work
Contact us today to speak with an expert about receiving a complimentary Business Health Assessment.
Reducing Manual Work in Finance with Sage Intacct AI
AI in Financial Forecasting: How CFOs Can Improve Accuracy & Efficiency
Financial forecasting allows your organization to stay ahead of the competition. While this process has historically been labor-intensive, this is changing with AI. AI-powered solutions are allowing finance teams to go from a pile of data to a finished forecast more quickly, while creating more comprehensive forecasts, often with multiple potential scenarios.
But not all AI tools are created equal, and there are some hurdles to cross before implementing them.
Here’s what finance leaders can get from implementing AI in their financial forecasting.
What is AI for financial forecasting?
“AI” is a broad term, covering a range of tools and technologies. In the context of financial forecasting, AI tools typically enhance your finance team’s ability to collect and clean data, analyze it for trends, and use these trends in their forecasts. These tools can often analyze data independently, call up specific data points on request, and chat interfaces to turn natural language requests into reports and dashboards.
This is achieved through a broad variety of AI technologies, including:
- Machine learning: This technology allows AI models to learn from large sets of data without needing instructions, continually improving on specific tasks. In financial forecasting, machine learning could allow an AI tool to better understand your organization’s expenses after being trained on years of budgets.
- Natural language processing: This allows AI tools to better understand human language by being trained on examples. They can then be used to analyze written language, generate voice-overs, and even detect the meaning of certain texts.
- Predictive modeling: By being fed historical data, AI tools can create predictive models (like forecasts) that take existing trends into account. This can dramatically accelerate your own forecasting.
- Generative AI: Fed data like images, written text, and more, this technology gives an AI tool the ability to generate its own content. Usually, this is done by responding to user prompts.
- Conversational AI: Conversation tools like ChatGPT rely on other technologies, like machine learning, while giving users an interface that allows users to enter natural language prompts to get a response based on the tool’s data.
- Large language models: This technology answers prompts by making highly accurate guesses about what the prompts require, based on the database it was trained on.
AI-powered forecasting vs traditional methods
There’s one key similarity between AI-powered forecasting and more traditional methods. AI tools, just like the people who use them, can learn from your data over time, becoming more efficient. This puts them a step above traditional forecasting tools that don’t rely on AI.
Deploying AI in forecasting allows finance teams to use more data without necessarily needing to dig through it themselves. When built into existing forecasting tools or FP&A software like Prophix One, AI gives you superior data analysis and trend detection while integrating seamlessly with the features you already use. That leads to better forecasts, dashboards, and more.
Additionally, when you train AI tools on your own data, you get better outcomes than when you rely on general AI tools using general data. Your data will be safer, too.
Applications of AI in financial forecasting
AI can deliver outsized value in your forecasting workflows, but only when deployed intentionally. Simply spinning up ChatGPT and asking it questions about your forecasts can help you save some time on repetitive tasks, but it’s not quite the same as using dedicated tools. Here are just a few ways AI tools can make a difference in your forecasts.
Automation
Forecasting is full of time-intensive manual tasks, like collecting and cleaning data from multiple sources, as well as scrolling through dozens of financial reports to track down that one elusive expense. AI tools like Prophix One can automatically centralize financial data as well as serve up specific data points.
Scenario planning
AI can analyze your existing financial data and produce multiple scenarios in a fraction of the time your finance team can. This saves crucial time you can then use to analyze these scenarios or launch new initiatives from them.
Revenue and cash flow projections
Manually estimating revenue and cash flow projects requires going through tons of data. AI can automatically do this for you, producing projections you can then use in other workflows without having to create them yourself.
Expense management
Tracking, categorizing, approving, and reporting on expenses creates a significant workload if handled manually. That’s why many finance tools already give finance teams ways to automate and streamline this process. AI raises this to another level, allowing your tools to learn about your organization’s expense trends over time, getting better at automatically categorizing and approving expenses.
Variance analysis and driver-based forecasting
Accurately detecting the factors leading to variance and their weight requires hours of data analysis. Properly basing your forecasts around them can be time-consuming, as well. AI tools can crunch through more data, more quickly, meaning you can identify variance more efficiently.
AI-powered insights
AI insights refer to conclusions, opinions, and trends that AI tools generate based on the data you give them. These can be essential in brainstorming factors that might affect your forecasts, correctly identifying trends, and turning complex reports into simpler insights.
Benefits of AI in financial forecasting
AI tools come with major benefits for just about any workflow, and this is also true in financial forecasting. Here’s what you have to look forward to when implementing AI tools:
- Increased accuracy: When combined with human oversight, AI tools allow finance teams to analyze data more efficiently and prepare more accurate reports.
- Improved risk management: Fully calculating the potential risk of an initiative or financial strategy can be difficult. AI helps build a more holistic picture of these risks.
- Enhanced productivity: By automating routine tasks and processing data, AI tools can free up more time for your finance teams, allowing them to get more done.
- Real-time insights: Asking a human finance team to provide real-time insights for every stakeholder isn’t scalable. But with AI, it can be.
- Cost efficiency: While doubling your finance team might be financially feasible, adding an AI tool to your stack can be more affordable while still allowing for a massive performance boost.
- More data sources and more comprehensive forecasts: AI tools can crawl through more data sources than your finance team in less time, giving them a more holistic view of your organization’s financials, leading to the creation of more robust forecasts.
These benefits create a massive impact in all sorts of financial processes, but you’ll see this chain in reaction in financial forecasting above all. That’s because finance teams that learn to augment their work with AI can better anticipate risks, optimize their organization’s resource allocation, and respond more quickly to market changes. That leads to better financial planning and a more effective overall strategy.
How to implement AI forecasting tools
While AI forecasting tools can lead to noticeable improvements in your forecasting workflows, they need to be implemented the right way. Here are some essential aspects of implementing AI tools you should keep in mind.
Define clear objectives
Before implementing any tool, you need specific, measurable goals. This is no different with AI. Are you primarily concerned with improving the accuracy of your forecasts? Will your main metric be the time saved by finance teams? Or do you want to identify variables and business drivers more effectively?
Select the right AI tools
Not all AI tools are created equal. Some are too general for your needs, while others aren’t quite feature-rich enough. A dedicated FP&A tool like Prophix One, with built-in AI features, is usually an ideal choice.
Integrating AI with existing systems
When you deploy an AI tool, you should ensure it works effectively with your existing tool stack. Otherwise, you’ll spend more time and budget on sourcing and setting up software integration platforms than you’ll gain from using AI in the first place.
Balance AI-driven insights with human expertise
AI isn’t a replacement for your finance team. It can give them access to more insights, more quickly, but it will never know your organization as well as the people who work there. Human team members should always be involved in your forecasting processes.
Ensuring data quality in AI forecasting
The effectiveness of an AI tool’s output depends on the quality of the data you feed it. Unlike humans, AI can’t differentiate between good data and bad data, adjusting its approach accordingly. AI needs accurate data—and human oversight—in order to work effectively. Here are some data quality measures you can put in place to give your AI tools the best data possible.
- Robust data management protocols: Standardizing the way you collect, process, and clean data across data sources and departments can prevent issues that would require lengthy audits in the future.
- Regular data audits and validations: Reviewing existing data can reveal data management processes that require improvement, while validation ensures that more of your data is free of faults.
- Strategies to address data gaps or inconsistencies: Having pre-defined processes for identifying and solving data health issues means your data will get healthier and more robust over time, rather than devolving.
- Strong data security measures and access controls: You don’t necessarily want to restrict access to your data sources, but the more individuals have access to them, the more likely they are to introduce errors.
- Ongoing staff training and data literacy initiatives: Improving data literacy across your organization can prevent data errors and improve data management protocols.
Step into the future of finance: Get started with AI forecasting
The right AI tool can completely transform the way your finance teams operate. They can process more data in a fraction of the time it would usually take them, build more comprehensive forecasts, and contribute to a more data-driven business strategy. Even better, it empowers them to make data accessible to more stakeholders, leading to better decisions throughout the organization.
If you’re ready to see Prophix One in action, then now is the perfect opportunity to register for Net at Work’s upcoming webinar.
Title: From ERP to AI-Powered FP&A Excellence: Unlock the Full Power of Sage X3 with Prophix
When: Thursday, September 11 from 2 – 2:45 pm EST
Webinar Registration: Click here to reserve your spot today.
Webinar Description: Join this webinar to see how Prophix One FP&A Plus transforms your data into automated, AI-powered financial intelligence. Whether you’re in Finance, IT, or an Analyst role, you’ll learn how to scale smarter decision-making across your entire organization.
Topics we’ll be covering include:
- Automate planning, forecasting, and reporting across entities
- Cut manual effort and budget cycles by up to 50%
- Ensure data security with Microsoft Azure hosting & role-based access
- Use AI responsibly to uncover trends and run what-if scenarios
- Work in Excel with two-way sync and auto-generate PowerPoint reports
Rather check out Prophix One on your own time?
Click here to watch a self-guided demo. You will get immediate access to their 8-part demo and see how over 3,000 companies drive progress with Prophix One.
Note: Content for this blog post was originally posted on prophix.com on August 28, 2025.
Blog was originally published on Prophix’s website on 8/28/25.
From Audit Risk to Audit Ready: Transforming Exemption Certificate Management with AI
AI is reshaping how teams manage exemption certificates. Join this web session where our partners and tax experts at Avalara will show how Exemption Certificate Management uses AI-driven validation, predictive insights, and intelligent workflows to reduce audit risk and improve compliance accuracy.
Adapting to the New Age of AI-Powered Cyber Threats
When you log in to your computer on a Monday morning and see that ransomware screen demanding payment, you should realize that the attack didn’t start that weekend. As Net at Work CISO Michael Powell explains, “To stage an attack, it’s not uncommon for a threat actor to have been in the environment up to 90 days.”
For weeks or months, threat actors may have been cataloging your data and exfiltrating files. With U.S. ransomware attacks up 149% year-over-year as of early 2025, understanding how these attacks work has never been more critical.
Most attacks follow predictable patterns. Once you understand the playbook, you can build defenses that work.
In this article you will learn:
- Why even well-funded cybersecurity efforts struggle to keep pace with evolving threats
- How the “double extortion” ransomware model puts organizations at risk even with backups
- Why AI has made business email compromise nearly impossible to detect
- Simple defensive strategies that dramatically improve security posture
- How to evaluate readiness and find the right security partners
Why This Keeps Happening
“Why is it hard? Why are we still trying to solve this problem?” Powell asks. His answer: “We’re effectively in an arms race.”
Organizations invest heavily and close vulnerabilities. Yet as one gap closes, attackers adapt. Cyber attacks per organization increased 47% in Q1 2025, reaching 1,925 weekly attacks on average.
The real challenge is asymmetry. Powell explains, “There could be more people trying to attack your organization than you have to play defense.”
The Ransomware Reality
During the 30-90 day reconnaissance phase, attackers aren’t randomly grabbing files. “They pull a file listing and then based on the file names and the file structures, they go for the information that they think is pertinent,” Powell explains. They systematically identify personally identifiable information, financial records, and commercially sensitive data, then slowly exfiltrate copies.
When attackers are ready to strike, timing matters. “We often see spikes around weekends, around evenings, around holidays.” Powell notes, “This is because reconnaissance and encryption take time.” They choose moments when you’re least likely to respond quickly.
The Double Threat
Even with robust backups, you face what Powell calls the “double extortion threat.”
“Your data is encrypted, and you need to decrypt it to continue to do business,” he explains. “But the threat actor knows these days people put reasonable technology controls in place. They’re betting you have backups, so they add a second pressure point: pay up, or we leak everything.”
The consequences go beyond embarrassment. Depending on your location and the data involved, you may face legal obligations to notify affected individuals. The average ransom demand in 2024 was $4.32 million, but legal costs and reputational damage can dwarf that figure.
Nearly one in five small businesses that suffered a cyberattack filed for bankruptcy or closed. This isn’t an IT problem; it’s a serious business survival issue.
How AI Changed Business Email Compromise
While ransomware grabs headlines, business email compromise (BEC) operates quietly and is equally devastating. BEC was the second-costliest cybercrime in 2023, with nearly $3 billion in losses.
AI has fundamentally transformed the threat. Attackers compromise an email account and download sent items. Previously, analyzing that information manually took time. Now? “You download that information, you throw it into AI, and then you can ask it questions,” Powell explains.
The AI builds a complete profile and generates emails that perfectly mimic executives’ communication. “Where we used to be able to spot those emails with relative ease,” Powell says, “AI helps the threat actor be a lot more convincing with very little additional work.”
With 73% of reported cyber incidents in 2024 being BEC attacks, and organizations with 1,000+ employees facing a 70% weekly probability of at least one BEC attack, this demands constant vigilance.
Your People: Vulnerability and Solution
“Most of the compromises we see occur because a person takes an action,” Powell says. “But they’re rarely doing it with malicious intent.”
Most breaches happen because employees respond to what appears urgent. “Pretty much every phishing test I have ever been a part of, at least one person has clicked the email, and you only need one.”
The solution lies in changing the culture around reporting threats rather than carrying out punishments. “People shouldn’t feel that if they raise a security threat, the IT team is going to pounce on them,” Powell emphasizes.
“Every person is a sensor,” Powell explains. Train people to recognize what normal looks like, then empower them to speak up. Modern training uses gamification: You click something, and then there’s just-in-time training that shows you why that was good, or why that was bad.”
Building Defenses That Work
Effective defense requires layered approaches that create multiple “tripwires.”
“The more visibility you have, the more chances you’ve got of spotting an anomaly,” Powell explains. Each layer, including endpoint detection, email security, patching, backups, network segmentation, increases the likelihood you’ll catch attacks before they succeed.
But technology alone isn’t enough. Organizations need clear procedures. Powell shares an example of a company with excellent technology but no response plan: “Person A looks at person B, they don’t know who’s responsible. Can we turn the system off? We don’t know who approves that.”
His advice: “If you don’t know who to inform in case of a breach, find out.” Conduct tabletop exercises revealing gaps. “Have people sit around a table and practice what they would do if a ransomware email came in.”
Where to Start
Powell offers a straightforward evaluation framework:
- Evaluate what you have. “There are so many times I’ve gone into an organization where they’re paying for something, but they’re using less than 10% of it.” Understand current capabilities before buying new solutions.
- Define risk tolerance. What’s acceptable downtime for different systems? Document thresholds in advance.
- Conduct regular audits. Most insurance carriers require annual assessments and often help with scanning.
- Leverage available expertise. Your insurance company often provides guidance. If working with technology providers, understand what expertise they have that they could bring to bear in the event of an incident.
- Consider cybersecurity as a service. For many businesses, working with a managed security service provider offers specialized skills without building an in-house team. “What they’re effectively doing is delivering the speed, delivering the skills and reducing the cost,” Powell explains.
When evaluating providers, Powell emphasizes fit over features: “Look for the one that suits your business and your processes.” And be sure to verify responsiveness: “There’s nothing worse than receiving a ransomware attempt on a Friday night and then realizing that the partner says they’ll deal with it Monday morning.”
And be sure to check references. “Try to find an organization they’ve worked with and talk to that organization. When you’re buying into cybersecurity as a service, you’re buying into trust.”
Moving Forward with Confidence
Understanding that attacks follow patterns, defenses can be layered effectively, and preparation dramatically improves outcomes will put you in a stronger position. With 86% of cyber incidents involving business disruption, the question isn’t whether to invest in security, but how to invest wisely.
Take the Next Step
Net at Work helps organizations build resilient security strategies that balance protection with practical business needs.
For a limited time, we’re offering complimentary assessments:
- IT Infrastructure Assessment: Comprehensive evaluation identifying vulnerabilities and opportunities
- Email Security Assessment: In-depth analysis of your email security posture—the primary attack vector for both ransomware and BEC
Don’t wait for a breach to discover where your defenses fall short. Contact Net at Work today to schedule your assessment and start building security that protects your business without compromising operations.
Ready to strengthen your security? Contact Net at Work to claim your complimentary assessments and speak with experts who understand your challenges.
Key Takeaways
- Understand the timeline: Ransomware attacks involve 30-90 days of reconnaissance before encryption. Early detection is everything.
- Prepare for double extortion: Even with backups, data leaks trigger legal obligations and reputational damage.
- Take AI seriously: BEC attacks now use AI to perfectly mimic writing styles, making traditional detection nearly impossible.
- Build culture, not just controls: Encourage reporting without punishment. Every employee is a sensor who can spot anomalies.
- Layer your defenses: Multiple security controls create “tripwires” that increase chances of catching attacks early.
- Rehearse your response: Tabletop exercises reveal gaps and build muscle memory for critical decisions.
- Leverage external expertise: Insurance carriers and managed security providers offer prohibitively expensive skills and resources.
Sources
- TechTarget, “Ransomware trends, statistics and facts,” 2025.
- Check Point Research, “Q1 2025 Global Cyber Attack Report,” May 2025.
- Spacelift, “50+ Ransomware Statistics for 2025,” July 2025.
- Fortinet, “Ransomware Statistics 2025,” 2025.
- The SSL Store, “Business Email Compromise Statistics,” March 2024.
- Hoxhunt, “Business Email Compromise Statistics 2025,” March 2025.
- LastPass, “Protect against business email compromise in 2025,” May 2025.
- Palo Alto Networks Unit 42, “Extortion and Ransomware Trends,” April 2025.
AI in the Wild: What’s Actually Happening vs. What Everyone’s Saying
By Peter Conway, Senior Partner Success Manager and Eric Sluss Fractional CIO & Advisory Net at Work
You’ve heard the pitch. AI is going to transform every business, automate everything that moves, and make half of your clients’ staff redundant by Q3. The slides are polished. The case studies are curated. The ROI projections are optimistic.
Here’s what’s actually happening.
What’s working
Clients are finding real value in AI, but it’s quieter and more specific than the headlines suggest. Automating repetitive workflows. Drafting first-pass communications. Pulling insight from data that used to sit untouched. The wins look like time saved, faster responses, and clearer decision-making.
Done right, that’s meaningful impact.
What’s not working
What’s failing is buying tools because they feel inevitable. AI deployed without governance, data discipline, or change management becomes shelfware or produces outputs no one trusts.
The biggest mistake isn’t technical. It’s strategic. What problem are we solving, and for whom?
Then there’s security. Free consumer AI tools are already being used inside client environments on company data, with no oversight. That’s not hypothetical. Steering clients toward secure, enterprise-grade AI is not upselling. It’s due diligence.
A word on the enterprise playbook
Enterprise AI lessons don’t automatically translate to SMBs. When a Fortune 500 restructures around AI, it’s news. When a forty-person company tries the same thing, it’s risk.
For SMBs, AI should amplify their best people, not replace them.
Where you come in
MSPs are uniquely positioned to lead this conversation. You know the environment, the workflows, and the bottlenecks. The MSPs gaining ground are helping clients slow down, ask the right questions, and build the guardrails that make AI safe and effective.
That’s not a technology conversation. It’s a business one.
The takeaway
AI adoption is real. So is the hype. The MSPs who understand the difference and lead with context, governance, and outcomes are earning trust and long-term advantage.
Speak to an AI specialist. Contact Net at Work to explore how to guide AI adoption with clarity, security, and real business outcomes.
Are Your Employees Quietly Increasing Your Risk Exposure Through AI Tools?
Thoughts on AI Security: What I’m Hearing in the Field
AI adoption is accelerating across businesses, but as I’m sure you’re telling your clients, so is the risk profile.
In my conversations with partners and through my reading on industry trends, I’m seeing some interesting patterns emerge. I wanted to share what I’m hearing, just to plant some seeds as you evolve your own stacks for 2026.
From the early days of on-premises servers to cloud migration, and now to the AI revolution, I know that business owners (your clients) tend to oscillate between two emotions: FOMO (Fear of Missing Out) and just plain fear.
They want the productivity of tools like Copilot and ChatGPT, but they are, or should be, terrified of their proprietary data walking out the door.
The opportunity I see for you isn’t just in reselling AI licenses; it’s in selling the safety architecture that makes AI possible. The shift seems to be moving from the “Wild West” of experimental AI to a disciplined, “Defense-in-Depth” approach.
I’ve come across two different architectural philosophies that might be worth considering. Again, you know your tools best, but here is how some in the industry are framing it:
Layer 1: The Foundation (Locking the Doors)
The consensus seems to be that before you turn on Copilot, the “House” needs to be clean. If permissions are messy, Copilot might expose sensitive data more quickly.
- The “Automation” Approach: I hear good things about tools like Inforcer or Nerdio for those who prioritize speed. The idea is to apply a “Gold Standard” security baseline instantly. I’m told their Drift Detection is a key feature for catching accidental security holes.
- The “Microsoft Native” Approach: If cost is the driver, using Microsoft 365 Lighthouse combined with SharePoint Advanced Management (SAM) seems to be the play. SAM helps you find and reduce oversharing and, with features like Restricted Access Control and Restricted Content Discovery, can keep sensitive sites like “Executive Comp” out of tenant-wide search and Copilot results while you review and fix permissions.
Layer 2: The Data Guardians (Stopping the “Copy-Paste” Breach)
We all know employees love to paste things into ChatGPT to “clean it up.”
- The “Browser Guard” Approach: Tools like Nightfall AI or Polymer seem to act as sanitizers right in the browser. They redact sensitive info (like credit cards) in real-time, so the AI gets the context without the secrets leaving the laptop.
- The “Microsoft Native” Approach: For those deep in the Microsoft ecosystem, Purview appears to be the standard. By labeling documents and setting Data Loss Prevention (DLP) policies, you can theoretically govern or block (often via ‘block with override’ or alerts) sensitive content being pasted into unapproved apps, depending on configuration and browser support.
Layer 3: The Watchtower (Shadow AI Visibility)
You can’t secure what you can’t see, and “Shadow AI” is growing.
- The “Network” Approach: Platforms like Zscaler or CrowdStrike are obviously heavy hitters here for monitoring and blocking traffic to risky AI sites.
- The “Microsoft Native” Approach: I’ve read that Defender for Cloud Apps now has a specific “Generative AI” filter that can discover thousands of AI apps running in an environment, allowing you to block the dangerous ones.
The Net at Work Perspective
My background is in ERP and line-of-business applications, so I look at this through the lens of data integrity.
AI is a powerful engine, but you provide the chassis, brakes, and steering wheel. By implementing these types of security layers, you aren’t just “securing” your clients; you are giving them the confidence to innovate. It transforms AI from a risky gamble into a reliable business asset.
I’d love to hear your take on this—are you leaning more toward third-party tools or sticking with the Microsoft native stack?
Note: The insights shared above reflect general industry observations and partner conversations. They are not official Net at Work recommendations or endorsements of specific tools or configurations. Please evaluate all solutions based on your clients’ unique requirements and consult vendor documentation for implementation details.
What Sage Intacct’s New Agentic AI Actually Does: A Practical Feature Breakdown
Here’s a number worth celebrating: according to Gartner, 59% of finance leaders now say their teams use AI, and the organizations seeing the biggest wins are those that pair the right tools with clean, well-structured data. That’s exactly the opportunity Sage Intacct’s newest agentic AI features open up. And with the right preparation and a partner who knows how to get your data foundation right, your finance team can be among the leaders turning AI from a buzzword into measurable results.
In this article, you will learn:
- How Sage Intacct‘s new Finance Intelligence Agent eliminates Excel exports for variance analysis
- Why the AI Import Agent finally solves the CSV import nightmare
- How AI-driven line-level matching transforms accounts payable processing
- What steps to take before implementing these agentic AI features in your organization
You Can Finally Stop Exporting Everything to Excel
We’ve all been there. You get a question from the CEO about why marketing spend jumped 20% last quarter, and suddenly you’re clicking through five different reports, exporting data to Excel, and building pivot tables to find the answer.
The Finance Intelligence Agent changes this completely. Instead of navigating through complex report builders, you can simply ask: “Why did our marketing expenses increase by 20% in Q3?” The system understands your chart of accounts structure and dimensional setup, then digs into the actual transactions to surface meaningful insights.
What makes this different from basic reporting is context awareness. The Agent might discover that a $15,000 trade show booth expense was accidentally coded to the wrong department, or that a new software subscription started mid-quarter. It presents the root cause analysis you need rather than just dumping raw data on your desk.
For finance teams managing multiple entities or complex project structures, this natural language querying saves hours of manual investigation time each week. (Note: the Finance Intelligence Agent is currently rolling out through Sage’s early adopter program in the US, UK, and Canada, so availability in your tenant may depend on your subscription and rollout timing.)
Data Imports That Actually Work on the First Try
If you’ve ever had a month-end close delayed because a payroll import failed due to a mismatched date format, you know the pain. Traditional ERP systems can be picky about data formatting; one wrong column header or unexpected character can derail your entire import process.
Sage Intacct’s AI Import Agent handles the messy reality of external data sources. When you upload a CSV file from your payroll system or operational platform, you can use plain English instructions to guide the import process. For example:
- “Map the ‘Dept’ column to our Department dimension”
- “Convert any ‘NYC’ entries to ‘New York'”
- “Skip rows where the amount is zero”
The agentic AI understands these instructions and transforms your data appropriately before posting to the general ledger. This eliminates the tedious back-and-forth of fixing spreadsheets, re-uploading files, and crossing your fingers that everything maps correctly.
Accounts Payable That Reads Between the Lines
OCR technology has been around for decades, and most AP automation tools can pull basic information like vendor names, invoice totals, and dates. The challenge has always been matching line items when vendors use different terminology than your internal item codes.
Picture this scenario: Your vendor’s invoice shows “1/2 inch copper tubing,” but your item master calls it “Pipe-Cu-0.5-IN.” Traditional OCR systems throw up their hands and route the invoice to someone for manual review.
The new AI Line-Level Matching in Sage Intacct learns your specific vendor patterns and internal coding conventions. It reads the context of invoice line items, matches them intelligently to your catalog, and applies the correct dimensional coding for projects, departments, or locations. The system handles routine matching automatically and only flags genuinely unusual items that fall outside normal parameters.
This contextual understanding means fewer invoices sitting in approval queues and faster processing times during busy periods.
The Foundation Matters Even More Than the Features
The most important thing to remember is that agentic AI is only as smart as the data structure underneath it. If your chart of accounts resembles a junk drawer, or if your team uses dimensions inconsistently, even the most sophisticated AI will give you confident but incorrect answers.
Think of it this way: If you ask the Finance Intelligence Agent about departmental spending trends, but half your transactions are coded to generic “Miscellaneous” accounts, the insights will be meaningless. The AI can’t magically create clean data from a messy foundation.
At Net at Work, we’ve learned from nearly three decades of ERP implementations that successful AI adoption starts with solid data architecture. Our Sage Intacct specialists work with finance teams to clean up GL structures, standardize dimensional usage, and establish consistent coding practices before turning on advanced features.
As Sage continues to expand its AI capabilities—the 2026 Release 1 update added the Finance Intelligence Agent, AI Import Agent, and AI Line-Level Matching alongside existing Close, AP, Time, and Assurance Agents—this foundational work becomes even more critical. The companies that benefit most from these innovations are those that invested in clean, consistent data practices from the start.
Getting Your Organization Ready for AI-Powered Finance
The rollout of agentic AI features in Sage Intacct represents a significant shift in how finance teams can work—but as the previous section makes clear, what you get out of these features depends entirely on the data foundation you put in. Before turning on natural language queries and automated imports, take a step back and evaluate your current setup. Are your dimensions used consistently? Do your account codes make sense? Can you trust the data that’s already in your system?
If you’re unsure, consider bringing in specialists who understand both the technical requirements and the practical realities of finance operations. The goal is to position your organization to take full advantage of these capabilities from day one rather than rebuilding your data after the fact.
Key Takeaways
- Audit your current Sage Intacct data structure before implementing AI features to ensure accurate results
- Start with simple natural language queries to test the Finance Intelligence Agent’s understanding of your chart of accounts
- Identify your most problematic data imports and test the AI Import Agent with staging data first
- Document your vendor naming conventions to help AI Line-Level Matching learn your specific patterns
- Train your finance team on asking effective questions of AI systems to get meaningful insights
Ready to explore how these agentic AI features could transform your finance operations?
Our Sage Intacct specialists can assess your current system architecture and help you prepare for successful AI implementation. We’ve guided thousands of organizations through ERP optimization since1996, and we understand what it takes to make these advanced features work in real-world finance environments.