At UpLevel Ops, we take a vendor-agnostic method, adapting to the instruments our purchasers already use relatively than steering them into inflexible ecosystems. This flexibility permits us to develop scalable, efficient AI options throughout varied platforms, together with Google Workspace and Microsoft 365, with a give attention to what works greatest in real-world workflows.
Six months in the past, I constructed a easy planner integration utilizing Microsoft’s CoPilot bot-building options. The expertise was tough, however purposeful. That early proof of idea instructed the platform may evolve into one thing extra highly effective over time.
Just lately, our engineering staff revisited CoPilot to check that assumption. We needed to see if it might match the efficiency of customized GPT-based instruments we’ve deployed efficiently in authorized and enterprise contexts. However what we discovered was not an improved or matured expertise. It was, in some ways, a regression.
This raised a much bigger query: Is the broader enterprise push to standardize on instruments like CoPilot, typically for the sake of streamlining IT and licensing, truly stifling innovation and belief in AI? May this default-first technique be the very factor driving person disappointment and slowing significant adoption? On this piece, we look at what occurs when platform loyalty takes priority over efficiency, and why that trade-off could also be backfiring.
Revisiting CoPilot: Then vs. Now
Our objective in revisiting Microsoft’s CoPilot platform was easy: consider whether or not it might now assist the sort of real-world use circumstances our purchasers depend on, beginning with primary automation, doc dealing with, and data retrieval. These weren’t edge-case calls for. We centered on foundational capabilities, akin to summarizing a doc, triggering a workflow, or retrieving structured info. Even modest performance would have sufficed.
To make clear, our testing was carried out underneath a CoPilot developer license, not inside a totally provisioned enterprise atmosphere. Whereas this restricted our means to check particular tenant-wide capabilities, it additionally surfaced some crucial considerations which may be much more related in an enterprise context.
We started with the brand new CoPilot agent builder, anticipating to see progress from earlier iterations. As a substitute, we discovered a stripped-down, underpowered interface. Brokers lacked action-taking talents, couldn’t combine with programs, and provided no assist for file uploads. Even one thing as primary as accessing and summarizing a public hyperlink failed, regardless of that being customary fare in generative AI.
Most alarming, nevertheless, was what occurred subsequent. Throughout one check, the bot unexpectedly resurfaced process knowledge from a months-old undertaking: outdated planner entries and references to earlier shopper tabs. How that info endured throughout builds and periods stays unclear. However whether or not it was as a consequence of poor knowledge isolation or residual reminiscence, the end result was unsettling. For a software so tightly built-in into Microsoft’s ecosystem, this sort of unpredictable knowledge publicity poses vital dangers, notably in environments that deal with delicate or regulated info.
Nonetheless hoping the brand new interface was simply immature, we tried the legacy CoPilot builder for comparability. It provided extra backend management however was clunky and equally unreliable. We uploaded a doc and ran a summarization process. The bot claimed the file didn’t exist. We repeated the question with the precise filename: It nonetheless failed.
This wasn’t a matter of minor bugs or lacking options. It was a constant incapacity to carry out primary duties. When put next with the pace, accuracy, and reliability of our customized GPTs and GEMs, deployed efficiently throughout authorized, operations, and enterprise workflows, CoPilot didn’t simply fall brief. It lacked the baseline readiness to be thought-about a viable AI software for actual work.
Essential Gaps in Core Performance
Our analysis of Microsoft CoPilot, which utilized its developer instruments to copy performance frequent to our customized GPT brokers, revealed a number of foundational deficiencies. These limitations weren’t tied to bleeding-edge expectations. We had been testing the sort of bread-and-butter capabilities any enterprise AI software ought to have the ability to deal with reliably. As a substitute, what we discovered had been architectural constraints so limiting, they increase critical questions on whether or not CoPilot can meaningfully assist enterprise workflows.
This isn’t only a matter of lacking options; it’s a sign that the software will not be engineered with actual enterprise calls for in thoughts. To assist illustrate the scope of the problem, we’ve grouped our observations into three core classes:
Limitations within the New CoPilot Agent Interface:
- No entry to instruments or action-taking capabilities. Brokers are fully passive; they’ll’t set off actions, launch workflows, or combine with current enterprise programs.
- No assist for file uploads. Content material should be linked by way of public URLs, making it unattainable to make use of any personal, proprietary, or inside paperwork.
- Strict and unreliable hyperlink dealing with. Many legitimate public URLs had been rejected as a consequence of question strings or size. Even accepted hyperlinks often did not load.
- Incapacity to summarize or parse paperwork. Even when hyperlinks had been embedded straight into the chat, the agent typically claimed it couldn’t entry them.
- Unprompted resurfacing of outdated knowledge. Most regarding, the agent started referencing planner duties and shopper tab names from months-old check periods, indicating potential knowledge persistence points that violate expectations round sandboxing and data boundaries.
Extra Points within the Legacy CoPilot Builder:
- Cumbersome configuration. The setup course of for legacy brokers is outdated and requires guide effort with little steerage or built-in automation.
- File add failures. Even when paperwork appeared to add efficiently, the agent was unable to search out them by title or entry them in any respect.
- Non-functional auto-build instruments. The platform’s “auto-build” choice did not generate usable bots, even for easy summarization use circumstances.
Limitations Shared by Each Interfaces:
- Lack of contextual reminiscence. Bots failed to keep up coherence throughout a thread, routinely “forgetting” earlier directions or person inputs.
- Failures on primary duties. Doc summarization by way of both file add (legacy) or hyperlink (new) was unreliable and often non-functional.
Collectively, these points prolong far past first-release quirks. They level to deeper systemic design issues that undermine enterprise readiness. The platform lacks consistency, interpretability, and clear knowledge boundaries that authorized, operations, and safety groups require. Worse, as a result of CoPilot is marketed as a deeply built-in a part of the Microsoft atmosphere, its failure modes might carry outsized dangers, particularly when tied to delicate enterprise info.
When in comparison with extra open, modular fashions like customized GPTs or Google’s GEMs, each of which we’ve deployed with success throughout authorized and operational workflows, CoPilot doesn’t simply path. It fails to satisfy the essential threshold for enterprise-grade AI reliability. Till these structural points are resolved, counting on CoPilot as the muse for enterprise AI efforts might do extra hurt than good.
Classes in Belief and Technique
What’s changing into more and more clear within the enterprise AI area is that efficiency failures not solely sluggish adoption but in addition undercut belief, credibility, and long-term momentum. When IT groups default to platforms like Microsoft CoPilot merely for comfort or ecosystem alignment, however these instruments underdeliver, the implications ripple far past person frustration.
In accordance with a current Accenture business report, 28% of C-suite leaders cite limitations with knowledge or know-how infrastructure as the most important hurdle to implementing and scaling generative AI. Nonetheless, many of those limitations seem like self-inflicted, stemming not from an absence of obtainable know-how however from inflexible platform decisions and entry restrictions. The truth is, 68% of staff report their employers don’t present full, unrestricted entry to AI-based instruments, regardless of excessive demand and reported use.
This atmosphere, one through which the “official” instruments aren’t succesful and the succesful instruments aren’t formally supported, creates a disconnect that slows progress and seeds doubt. AI turns into a compliance legal responsibility as an alternative of an innovation driver.
We’ve seen the distinction firsthand. At UpLevel, purchasers who’ve adopted versatile, well-matched instruments, akin to customized GPTs or GEMs, and carried out them thoughtfully have constantly seen acceleration, not hesitation. Adoption rises. Groups have interaction. Use circumstances develop organically. Importantly, enthusiasm inside pilot applications tends to extend over time, as customers see what’s doable and construct on early successes. We’ve additionally noticed that adjoining groups and even exterior companions often ask to be included, not due to top-down mandates, however as a result of they see the instruments are efficient. Success invitations participation.
In contrast, when groups are pressured into utilizing underperforming instruments like CoPilot for the sake of standardization, the technique typically backfires. AI isn’t judged in a vacuum; it’s judged by outcomes. If the primary expertise with enterprise AI is irritating or unreliable, it turns into that a lot tougher to re-earn confidence later.
Belief stays the cornerstone of AI success. It’s earned via transparency, constant efficiency, and responsive implementation. IT leaders hoping to scale generative AI successfully should be keen to ask a tough query: Are our software decisions constructing belief, or quietly eroding it?
The Continued Worth of Customized GPTs and GEMs
If there’s a silver lining to the constraints we encountered with CoPilot, it’s the reaffirmation of what’s already working: pairing Microsoft’s robust automation spine with clever, adaptable AI fashions akin to OpenAI’s customized GPTs and Google’s GEMs. This hybrid method continues to outperform extra inflexible, closed-loop instruments, delivering dependable, scalable leads to real-world workflows.
In contrast to CoPilot brokers, which stay locked down and laborious to increase, GPTs and GEMs provide the flexibleness that in the present day’s enterprises really want. These fashions will be tailor-made to particular roles, fed curated doc units, grounded in personal data bases, and up to date shortly because the enterprise evolves. They usually don’t simply reply, they adapt. This makes them much better suited to environments the place nuance, accuracy, and transparency matter.
In our deployments, this structure has constantly confirmed its worth. By way of direct integration with Microsoft Energy Automate, we’ve constructed chatbots that may schedule conferences, verify e-mail, help with undertaking administration, and assist process workflows, all tailor-made to the precise wants of authorized and operational groups. These sorts of use circumstances require orchestration via Energy Automate. And mockingly, it’s considerably simpler to attain these integrations utilizing exterior LLMs, akin to GPTs or GEMs, than it’s with Microsoft’s personal CoPilot brokers. The expertise is extra configurable, much less brittle, and way more conscious of real-world calls for.
These exterior brokers function inside clearly outlined knowledge boundaries, respect privateness constraints, and ship reliable efficiency with out requiring fixed oversight or elaborate workarounds. They simply work.
Microsoft’s workflow instruments nonetheless play a vital function. They provide a strong basis for integration and orchestration. However they’re solely a part of the answer. The intelligence layer on high should be smarter, extra adaptable, and safer than what CoPilot presently gives. That’s the place GPTs and GEMs proceed to shine and why we proceed to depend on them.
Finally, this isn’t about chasing options. It’s about selecting instruments that align with how individuals truly work, instruments that scale as belief builds, and instruments that invite adoption relatively than resist it. Till CoPilot can meet that bar, it can stay a cautionary story: a reminder that in AI, outcomes, not vendor alignment, ought to drive technique.
Remaining Ideas: A Name to IT Leaders
Standardizing round a single vendor can appear to be the plain selection. It presents the phantasm of simplicity: fewer programs to handle, constant interfaces, and centralized safety. Microsoft’s narrative round CoPilot faucets straight into that enchantment. However alignment solely is sensible if the platform truly delivers. In its present kind, CoPilot typically creates extra issues than it solves. Counting on it solely means accepting trade-offs that slowly erode belief and stall momentum. Customers lose endurance. Management begins asking tougher questions. The promise of AI feels extra like a advertising and marketing story than a working resolution.
Give it some thought this fashion: When was the final time a enterprise adopted a breakthrough know-how and deliberately selected the slower, clunkier model, even when higher instruments had been obtainable? That’s what’s taking place in a whole lot of AI rollouts proper now. And the price isn’t simply measured in {dollars}. It exhibits up in missed alternatives, lowered confidence, and slower progress throughout the board.
At UpLevel, we’re not strolling away from CoPilot. Lots of our purchasers are being guided towards it by inside IT insurance policies and licensing selections. So we’re persevering with to take a position time in understanding what it could actually and may’t do. We’re taking a more in-depth take a look at the Microsoft enterprise model to see whether or not a extra totally provisioned atmosphere delivers a greater expertise. We’re additionally testing whether or not the use circumstances we’ve already constructed with customized GPTs and different instruments will be recreated inside that ecosystem. And sure, we’ll hold checking again to see if Microsoft resolves the problems presently limiting the platform.
Our objective isn’t to switch CoPilot outright. It’s to construct intelligently round its shortcomings and fill within the gaps with smarter, extra adaptable instruments. CoPilot could also be a part of the stack, however it could actually’t be the entire technique. That’s why we’re asking IT and authorized leaders to make room for extra succesful options: chat brokers that aren’t locked right into a single ecosystem and meet the calls for of actual work. Flexibility isn’t a luxurious on this area. It’s a requirement. Till CoPilot evolves, organizations want to permit for integration with extra superior, configurable chat brokers that ship higher efficiency, a extra seamless expertise, and quicker outcomes. As a result of on the finish of the day, our purchasers don’t simply want AI. They want AI that really works.
Brandi Pack, Director of Innovation at UpLevel Ops, has a various background that spans the authorized, hospitality, schooling, and know-how industries. Over the course of her profession, she has excelled in varied strategic enterprise operations roles at Hewlett Packard Firm, Constellation Manufacturers, and Goodwill Industries. Brandi has a profitable observe document in undertaking administration, coaching, enterprise improvement, authorized operations, and IT companies. She is a thought chief within the rising area of AI within the office, notably because it impacts the authorized panorama.
