Ask someone what the AI revolution looks like, and they will probably mention chatbots, copilots, or something from a Super Bowl ad.
The truth is, the AI that is transforming industries is far less glamorous. It is not built to entertain or impress; it is built to work. Quietly, reliably, and at scale.
I call it “boring AI,” and I am here to champion it.
While most of the attention is focused on the flashiest use cases or largest models, the organizations seeing real returns are the ones embedding AI into the places that matter most: in the back-end processes, administrative workflows, and cross-system complexities.
In other words, the places where work actually happens.
Flashy AI Fails, Boring AI Delivers
There is a widening gap between AI’s potential and its performance. An August 2025 study from MIT found that 95% of generative AI pilots fail to deliver measurable ROI. Dig deeper into the data, and it becomes clear the root of this failure is not a technology problem. It is a design and deployment problem.
The research described a “Gen AI Divide,” representing the 5% that embed AI into complex, high-value processes versus the 95% that bolt generic AI tools onto low stakes use cases and get low returns. Most companies prioritize visibility over impact when deploying AI, such as in sales, marketing, or chat interfaces, neglecting back-end processes and procedures where efficiency and risk reduction actually show up on the balance sheet.
Perhaps most damningly, the study also reported a sharp discrepancy between employee and executive opinions. While executives often describe internal AI tools as “very successful,” employees when asked report zero usage. AI looks impressive in demos and steering committees, but it does not get efficiently integrated into daily work.
The gap is also visible in how companies talk about AI publicly versus privately. According to September 2025 reporting from the Financial Times, enthusiasm for AI is high and nearly universal. In regulatory filings, however, the tone is more cautious. Cybersecurity risks, legal exposure, reliability concerns, and fears of failed implementation dominate.
That is another clue as to why there is such a lack of clarity around where AI should be applied. As one analyst quoted in the article put it, many AI investments today are driven less by a clearly defined business purpose and more by “fear of missing out.”
Operational impact beats hype every time.
AI Works Best When Embedded
AI delivers real value when it is embedded in core systems and multi-system workflows. ERP applicability is the clearest example.
For decades, ERP systems have been mission-critical for running finance, supply chain, and various operations, yet notoriously rigid. Generic AI tools struggle because they lack long-term context, proper customization, and reliable integration with existing data models and controls.
Embedded, process-centric AI flips this dynamic. It adds automation and coordination capabilities that unlock value that has been trapped inside ERP systems for years.
This is where AI quietly solves problems that once felt impossible: reconciling data across ERP and CRM, coordinating workflows across inventory and procurement, auto-generating financial insights from scattered data sources, or orchestrating case resolution across multiple platforms.
It is not glamorous. It is not a demo. But it is transformative.
Doing Regular Work with Superhuman Efficiency
In the rush to chase flashy AI breakthroughs, it is easy to overlook the everyday wins that actually move the needle. Most organizations do not need more AI experiments; they need AI that gets to work, reliably solving real problems and improving routine processes to free up capacity.
Many of the most effective enterprise AI use cases today are highly specific and operational. Take Acclaim Autism, a behavioral healthcare provider specializing in Applied Behavior Analysis (ABA) therapy for children with autism. Like many healthcare providers, Acclaim Autism faced a common challenge: Clinicians were overwhelmed by paperwork, buried in administrative work. Scheduling, documentation, billing, and follow-ups were manual and consumed time better spent with patients.
By using a private AI solution to automate the extraction of essential patient information, including date of birth, diagnosis codes, and provider details, these workflows now run quietly in the background. The results are anything but boring: Patient intake waitlist times were reduced by over 80% to less than 30 days.
Wins like this happen because AI is improving the operational systems like the ERPs, scheduling tools, and billing and inventory platforms staff rely on every day. It is AI embedded directly in the work.
Why Boring Builds Trust
When it comes to AI governance, no news is good news. If a customer is denied a loan without explanation, or a regulator finds discriminatory patterns, it is front-page news and a reputational crisis. If AI systems are governed properly with auditable models, human oversight, consistent data use, no one hears about it.
This shows up clearly in corporate disclosures. Companies are increasingly explicit about AI-related risks: cybersecurity threats, legal exposure, regulatory uncertainty, and the danger of relying on systems that do not behave predictably. Finance leaders in particular are wary of tools that promise productivity but introduce control gaps.
In the Acclaim Autism behavioral healthcare example, documentation accuracy and compliance are non-negotiable. Every note must be logged correctly. Every session must be tracked. Every claim must meet strict regulatory standards.
There are no headlines when this works, and that is the point. The absence of drama means AI systems are working safely, risk is managed, compliance is maintained, and both customers and regulators are confident.
Low-code platforms give organizations the structure and transparency to make AI governance as boring as it should be. With built-in audit trails, human review, and data controls, organizations are not scrambling to retrofit governance after deployment. It is embedded from day one.
Real Promise of AI: Freedom from Boring Stuff
AI is not here to replace work. It is here to liberate us from the most frustrating parts of it: the repetitive tasks, coordination chaos, and gaps between systems that slow everything down.
That is how the numbers will add up. Companies are investing heavily in AI, and the only way to justify that investment is by embedding it where it delivers repeatable, measurable value. That means thinking beyond the surface, starting with processes, and, yes, celebrating boring AI.
Boring AI is serious AI. It succeeds when it disappears into the workflow, and it fails when it asks us to change how we work. It is backed by actionable data, and it delivers real value.
Editor’s Note: What This Means for ERP Insiders
Enterprise AI succeeds when it is embedded inside core workflows. Tools that require users to step outside ERP systems, duplicate data, or bypass established controls struggle to gain adoption, regardless of technical sophistication. The most durable AI value comes from automation that quietly accelerates finance, supply chain, and operational processes without changing the nature of the work.
ERP competitiveness is increasingly shaped by integration depth and process orchestration. As experimentation gives way to operational scrutiny, platforms and ecosystems that can coordinate AI across ERP, CRM, and adjacent systems, with governance built in, will separate themselves from those offering isolated experiences. Architecture, not novelty, is becoming the decisive factor.
The clearest returns from AI are in unglamorous improvements. Faster closes that reduce friction, fewer exceptions that end manual effort, stronger compliance that mitigates risk, and lower operational variability that opens capacity may not generate headlines, but they define measurable success. In ERP environments, the AI initiatives that matter most are the ones that fade into the background because they consistently work.
—Mark Talbot is director of architecture and AI at Appian.
