What We Think

I lost an old friend recently. I’m not going to share any additional information about the circumstances to avoid giving clues to their identity, because they were well known in certain circles, and their family has been through enough.

“We rarely suffer from bad intentions or actors. However, we do frequently suffer from the inadvertent effects of things that were meant to do good. That doesn’t mean we should stop doing them.”

Not a household name, but well-known enough that the death was announced across various media outlets and, of course, social media. I don’t really normally hobnob with folks whose passing merits public comment, so perhaps I am just naïve, but man, the user comments were brutal. You would have thought they were Bernie Madoff.

Now I knew this person really well. They sometimes had an abrasive personality, or maybe that certain kind of douchiness forged by circumstances that push an awkward person into social situations because of their passion for a subject, which leads to an adaptation to be perceived as normal. Think Elon Musk, but before he started down this Bond villain arc.

But definitely not the type of individual whose death should be celebrated, which is, if you think about it, thankfully tiny, even if you believe such a thing is morally acceptable.  But I’m not actually here to debate that part.

Those of us who have been here in the digital space since the beginning, accurately or not, have a sort of parental relationship with it. We feel a certain responsibility for it and the societal charges it has enabled. And where we are is not what I signed up for.

It was supposed to make our lives better, not worse. It was supposed to enable the exchange of ideas, lower the barriers between cultures, and empower global commerce.

There was no pivot point, no active decision to head things in this direction. Mark Zuckerberg had a pretty gross idea in the beginning, but it sure wasn’t to imprison our minds and polarize us to the point where we can no longer recognize each other’s humanity. Nor did Alexis Ohanian, Steve Huffman, or Aaron Swartz. No, we did that ourselves. But it was the unintended consequences of something that was supposed to be incredibly beneficial. Sound familiar?

We rarely suffer from bad intentions or actors. However, we do frequently suffer from the inadvertent effects of things that were meant to do good. That doesn’t mean we should stop doing them.

The architects of social media did not set out to create the engines of exploitation and outrage. Smartphones were not created to monopolize attention at the expense of being present. And yet, incentives, scale, and human behavior have combined to produce these outcomes that few anticipated.

The lesson to be learned here isn’t that innovation is inherently dangerous. The lesson here is that technology rarely changes society in the way its creators and adopters expect it to. My internet brought the world closer together, but it also fragmented us more. It democratized information, but it also simultaneously made discerning reality much murkier. Its benefits were real. But the costs were so much higher than we thought. Much higher than I thought.

AI presents the same challenge. The question is not whether it will be useful. It unquestionably will be. The question is what emerges when billions of people, millions of businesses, and thousands of governments begin adapting their behavior around it.

Where we are today is not an American problem. It is not a Chinese problem. It is not an Israeli problem. It is a human problem. We had better get used to the idea that the future rests on us solving global problems, not tribal ones, or there won’t be one. AI is coming, and nothing can stop it. But we can do our best to anticipate and mitigate as many unintended consequences as possible. AI will need infrastructure to power it, and we need to prepare ourselves for the societal disruptions it will cause. Data centers can be built so as not to harm their surroundings, nor exploit the local population into bearing their costs. Job retraining can be a priority. We can ensure we work ethically and avoid losing control. This should be our modern-day eradication of polio.  We must. There is no sorcerer to repair the apprentice’s mistakes. All it requires is the will. We can do this. My friend certainly thought we could.

Have you done this before? It’s one of the first questions clients ask during a technology selection process. And it’s a fair question. While the answer is usually yes, it’s not really true, is it? We may have implemented X solution for an organization in Y industry, but does that really mean as much as the client thinks it does? Experience matters. Just not in the way most people think. 

I was having lunch with a friend who had recently moved from leading customer-facing digital products into a B2B manufacturer with a much less mature digital organization. We started talking about why consulting teams struggle when they change industries, and it reinforced something I’ve believed for years.

It’s knowing how to walk into an organization, extract real information from people with different, sometimes conflicting, agendas, and translate that information into requirements and a design that holds up

It’s tempting to think technology projects are mostly about installing software and configuring it correctly. And let’s be honest – Sales is not helping us here. But digital ecosystems are rarely the same. Structures for integration, even within the same platforms, are wildly different. The internal processes organized around business functions are distinct by organization. The problems these solutions intend to solve are diverse, and so is the value they generate, even if they seem similar on the surface. Heck, even the personalities and politics within the organization have a significant impact on delivery. 

So, what does matter?  

The fundamentals.  

Requirements. 

Architecture. 

Governance. 

Identity. 

Testing. 

Deployment. 

Change management. 

It’s knowing how to walk into an organization, extract real information from people with different, sometimes conflicting, agendas, and translate that information into requirements and a design that holds up. It’s the understanding that without change management, the best solution implementation won’t move the needle. 

The details of these activities will vary for each engagement, but their core processes do not. These disciplines don’t change because the logo on the building changes. Technology evolves. The disciplines don’t. Do them correctly, and the specific technologies matter less. So, does experience in a particular type of project matter? Of course it does. Experience gives you the knowledge of which nuances to add to certain workshop questions, which pitfalls to avoid, and how to recognize targeted patterns unique to the situation. But without strong fundamentals, none of it matters. 

So, when someone asks, “Have you done this before?”, they’re asking the wrong question. 

A better question is: 

How do you approach the work that every successful project has in common? 

Because platforms change. Vendors come and go. Industries transform. 

Good engineering fundamentals don’t. 

One of the first questions asked in almost every strategy workshop is, “Who are our competitors?” It’s a logical place to start. Understanding the competitive landscape is essential when evaluating products, pricing, positioning, and market share. But it’s not always the right question when you’re designing a process. It doesn’t go nearly far enough, and it’s a mistake to stop there.   

The organizations that create the greatest competitive advantage won’t be the ones who study their competitors most closely. They will be the ones who understand their users, identify the unnecessary friction points, and leverage ideas from wherever the best experiences already exist.

When you’re designing a process, the goal isn’t to outperform your competitors. It’s to remove friction. Regardless of industry, process friction almost always comes from the same places: users can’t find what they need, information is incomplete or inconsistent, there are too many unnecessary steps, the process takes too long, or critical knowledge exists only in people’s heads. 

And the biggest one: Human nature. People naturally choose the path that requires the least effort, the least uncertainty, and the least amount of time. Great processes work with those instincts instead of against them. Your organization or industry does not hold a monopoly on any of these sources of friction. 

Your competitors don’t own the best ideas. They simply share many of the same constraints.  

So why are you trying to solve problems by looking only at a small slice of the available ideas? Your users are not comparing you just to your competitors any longer. Whether they realize it or not, users increasingly compare your processes to the best experiences they’ve had elsewhere. Digital tools and services have raised expectations across every aspect of work and life. As a result, users no longer evaluate processes in isolation. They carry expectations from one experience into the next. 

To be clear, not every process should be as simple as buying a book online. Some work is inherently more complex than others. But complexity should come from the work itself, not from the process surrounding it.  

It’s more than just what your customers see. Friction abounds inside the organization as much, if not more, than outside. It’s not, ‘Well, that’s the job.” Every unnecessary approval, duplicate entry, manual workaround, or search for information steals time from work that actually creates value. Over time, that doesn’t just reduce efficiency, it limits innovation. 

You may be thinking, ‘So what? Isn’t this what AI is supposed to fix?’ Nope. AI can help remove certain kinds of friction, but it cannot repair a fundamentally broken process. Feed AI a process with unnecessary approvals, unclear ownership, or poor data, and it won’t fix those problems. It will automate them faster and at a greater scale than a human ever could. A weak foundation must be repaired before you can build something stronger on top of it. 

The history of innovation is full of organizations borrowing ideas from outside their own industries. Some of the best process innovations don’t come from competitors at all. They come from organizations solving similar human problems in completely different industries. The Great Ormond Street Hospital for Children in London was looking to improve its handoffs from surgery to intensive care. Their search for a better process took them to what seems like an unlikely place: Formula 1. By studying how pit crews coordinate dozens of specialized tasks in seconds with remarkable consistency, they adapted the same principles to surgical handoffs. As a result, handoff outcomes improved to the point where the U.S. Agency for Healthcare Research and Quality (AHRQ) includes it in its patient safety resources. 

So, when you are ideating requirements, instead of ‘How is my successful competitor doing this?’, spend more time on ‘Who handles this scenario well?’. Look for organizations that have mastered the problem you’re trying to solve, regardless of industry. Then ask why it works and whether the underlying principles apply to your own challenge. 

The organizations that create the greatest competitive advantage won’t be the ones who study their competitors most closely. They will be the ones who understand their users, identify the unnecessary friction points, and leverage ideas from wherever the best experiences already exist. The goal isn’t to have the best process in your industry. It’s to create a process so intuitive that your users stop thinking about it altogether. The best processes aren’t remembered because they’re innovative. They’re remembered because they remove friction so effectively that users stop noticing the process altogether. 

Study your competitors to better understand your industry. Study everyone else to improve it. 

That’s the audit SCIGON runs before anyone touches automation: find the friction, fix the foundation, then decide what’s worth automating. Worth a conversation. 

What image pops into your head when you hear the word salesman? Is it a tired middle-aged man lugging around a suitcase door to door? A shady guy on a used car lot with a bad tie? Or maybe a bunch of slackers sitting in a paper company office in eastern Pennsylvania. 

The challenge isn’t creating customer relationships. It’s translating decades of relationship knowledge into digital experiences that customers actually want to use. 

What do they all have in common? Direct human interaction. For decades, that model powered B2B sales. And for good reason. Industrial products are complex, relationships matter, and trust is earned over years.  

What else? They don’t exactly exude modernity, do they? Whether your image is of Willy Loman or Jim Halpert making in-person visits or phone calls, it doesn’t jive with today’s idea of analytics-driven conversion funnels. Yet, this is how most of the B2B world operates.  

But the way buyers discover, evaluate, and purchase products has changed dramatically. The question is whether industrial manufacturers’ sales processes have changed with them. For much of the manufacturing sector, the commercial side of the business simply hasn’t evolved as quickly as the operational side. Many manufacturers can tell you the exact location of a pallet on the warehouse floor, but they still can’t let a customer reorder that same pallet online.  

If you remove the B2B technology sector and focus on manufacturing, their sales processes are startling. According to Salesforce, fewer than half of manufacturers have a fully implemented CRM, a foundational technology for modern sales organizations. And even those that have one implemented, 43% consider user adoption their biggest challenge, according to Gartner. Don’t ask about marketing automation. 

When eCommerce capability is considered, the picture gets even more surprising.  Shopify’s analysis of the manufacturing market shows that only 7% of the $14.85 trillion in manufacturing sales happens through modern online commerce channels. While awareness is growing, adoption still trails buyer expectations. Only 47% of manufacturers even consider B2B digital commerce a strategic initiative for improving customer experience, according to IDC. Heck, the SANA’s Manufacturing & E-Commerce Benchmark Report reveals that only 39% of manufacturers are planning an eCommerce project to help remediate the issue. And 71% still take more than a day just to put together a quote.  

You may be thinking, well, that’s not great for new purchases, but the B2B business model is different. The vast majority of B2B manufacturing businesses are repeat orders, and eCommerce is not a critical channel like it is for retailers. This is an outdated assumption, and that’s exactly why this matters. McKinsey’s Quarterly research indicates that only 15% of buyers want to speak with a salesperson when repurchasing the same product or service. They want saved orders, pre-negotiated pricing, availability, delivery dates, and a 1-click reorder process. They want salespeople involved when expertise matters, not when they need last month’s invoice. That same article reports 62% of industrial buyers prefer digital reordering, but only 10% of industrial OEMs provide that self-service reordering capability. Their competitors could drive a delivery truck through that gap.  

So why is this a big deal? They’re still successful and moving product, right? Not exactly. The disparity between what buyers expect and what these organizations are providing is getting worse. If it’s apparently not keeping manufacturing leadership up at night, it should. 

Thomas Industrial Survey research has consistently shown that industrial buyers increasingly use digital channels during supplier discovery. In fact, 73% of industrial buyers visit a supplier’s website before making a purchasing decision, and they expect real information: specs, CAD files, and certifications, not just a brochure. In addition, that same McKinsey report reveals that B2B buyers use an average of 10+ channels during their purchase journey and increasingly expect the ability to move seamlessly between digital self-service and human interaction. 

And it doesn’t stop there. Gen Z and millennials are projected to represent roughly two-thirds of the workforce within the next few years, according to Deloitte. These workers are accustomed to digital-first experiences and increasingly use AI and digital tools in their daily work. And we’re talking individuals who grew up with Amazon, online banking, Uber, and who are now early adopters of AI. BCG’s research shows younger buyers strongly favor self-service when the task is straightforward. And Gartner reports that 38% of Gen Z and millennials said they are likely to give up on resolving a customer service issue if it cannot be solved through self-service. The personal supplier relationships and tribal knowledge that power today’s industrial sales will remain valuable, but they will no longer be enough on their own. 

Taken together, this paints a grim portrait for those B2B companies that don’t adapt. Fortunately, there is still time, and B2B has some built-in advantages. Ironically, manufacturers may have an easier path than consumer brands. Unlike B2C, manufacturers have full control over their product data and can create the data foundation from within.  They already know their customers. They already have years of purchasing history. They already have trusted relationships. They already know which products customers buy, how often they buy them, and what pricing they’ve negotiated. Consumer companies spend billions trying to acquire that information. 

Manufacturers already own it. 

The challenge isn’t creating customer relationships. It’s translating decades of relationship knowledge into digital experiences that customers actually want to use. 

This isn’t about replacing the salesperson. It’s about elevating the role. Modern CRM, digital commerce, marketing automation, CPQ, and AI don’t eliminate relationships; they eliminate administrative friction. They allow sales teams to spend less time generating repeat quotes, tracking down order status, and manually processing reorders, and more time identifying new opportunities, solving customer problems, and proactively strengthening relationships before customers start looking elsewhere. 

The manufacturers that succeed won’t be the ones that eliminate sales relationships. They’ll be the ones that amplify them. 

The future of industrial sales isn’t digital instead of human. It’s digital where convenience matters and human where expertise matters. 

For decades, manufacturers have competed by removing friction from production. The next competitive advantage will come from removing friction from purchasing. 

That transformation starts with data, not because data is the end goal, but because every modern commercial capability depends on it. CRM, CPQ, AI, customer portals, marketing automation, and digital commerce are only as effective as the information behind them. 

That’s the work we do at SCIGON, turning purchasing history into systems customers can actually use. Worth a conversation. 

Ah, the excitement of a greenfield project. It’s almost like the smell of Spring when everything is just about in bloom, and there’s the promise of life begun anew. Your team has a pretty good idea of the business goals; they’re researching best-of-breed solutions to achieve them and looking forward to learning new technologies. It’s one of the most enjoyable times for a project team. But somewhere between starting to write the RFP and vendor demonstrations, it’s time to pump the brakes. Hard.

Have you seen an enterprise software contract recently? It reads like a menu written in a language that’s still being invented, with editions stacked on tiers, stacked on add-ons, each one priced differently depending on three other things you also did or didn’t buy.

Why? Because the right solution may not be one of the members of the assumed short list. It may not have the new feature that has your team members salivating. It might not even have everything needed to meet every requirement of the matrix that hasn’t been prioritized yet. But it has one major advantage over all of the others – your organization already owns it.

Don’t believe it? Here’s a challenge: walk into any large enterprise organization and ask where the list of all the software currently licensed is. You’ll be met with a blank stare. OK, fine. Ask who you can talk to who owns those licenses. Again, blank stare. Even if you’re lucky enough to find a person who owns one, it’s guaranteed that they don’t own all, or even most of them.

For the sake of argument, let’s say everyone who owns a software license in the organization still works there, and you have managed to assemble them all in a single room. Ask them what you are entitled to use in their contract. Again, a guaranteed blank stare. Sure, they will probably know what was used in the project it was originally procured for, but that’s not remotely close to the full picture.

Have you seen an enterprise software contract recently? It reads like a menu written in a language that’s still being invented, with editions stacked on tiers, stacked on add-ons, each one priced differently depending on three other things you also did or didn’t buy. As these companies grow through acquisition, the contracts have become impenetrable even to the veterans who negotiate them for a living.

So, here’s the key: Before the RFP, before demos, before the team falls in love with a feature, spend the effort to find out what you already own. Take the time to understand the full picture, not just the piece that was used on the project it was originally bought for. Find the dormant modules, the switched-off features, and the capabilities buried in an agreement that nobody’s read since it was signed.

It’s tedious work. Nobody gets promoted for it. But the incumbent doesn’t have to win on features. It only has to do enough, at a fraction of the total cost of standing up something new. Licensing, infrastructure, integration, and upskilling: choosing a new platform means paying for all of those again. The one you own has already paid for most of them.

That’s the calculation that actually belongs in the business case. No magic quadrant can tell you that.

There’s no denying that artificial intelligence is the belle of the ball, well, the belle of ALL the balls. Apparently, it is going to make your (insert buzzword here) a world-class growth engine that will (insert another buzzword-heavy phrase here).

But there is a dirty little secret that affects every organization – particularly large enterprises. AI doesn’t know the quality of your data.

But there is a dirty little secret that affects every organization – particularly large enterprises. AI doesn’t know the quality of your data. It will loudly proclaim a convincing, authoritative narrative, even if the underlying data it is based on is absolute trash. And let’s be honest here – most of the time it is.

For most organizations, defining KPIs and aligning customer touchpoints with the data collection to support them is an afterthought – if it’s even a thought at all. This is a significant problem in a single system, but modern digital ecosystems are a patchwork of systems that need to be integrated and normalized, snowballing the issue. This is nothing new, of course. Most senior leadership don’t understand analytics or data science. They view it as just a cost center that should be as lean as possible, rather than the key to defining success and understanding which efforts are most effective at executing strategy and driving revenue.

For decades, technologists have acknowledged the phrase ‘Garbage in, garbage out’. But in today’s frenzied AI FOMO environment, it’s being ignored. Unfortunately, this is incredibly dangerous. It can lead to basing an entire digital strategy on a confident, persuasive argument that is no better than a hallucination. Layering on agentic AI makes this infinitely worse.

In the old days, broken data would reveal itself in a wonky dashboard. An analyst would dig into the issue and identify it. AI doesn’t work that way. It’s designed for ambiguity and to fill in the gaps. In other words, AI isn’t going to tell you it’s wrong. It will invent a reason why it’s correct. The combination of both agentic AI and leadership making autonomous decisions based on a flawed picture of the landscape is inherently hazardous. This synthetic authority is a critical risk to an organization. It’s not only a challenge to big swings – smaller, frequent decisions are arguably even more of a problem as they slowly compound distortions of reality until its entirety is flawed.

Does this mean organizations shouldn’t adopt AI? Of course not. It means that AI should not be operationalized before an organization’s data house is in order and prepared for the roadmap ahead.

But what does that mean? It means a strong data strategy with solid governance. Data is not just a byproduct of operations. It’s a product and an asset whose management, curation, and documentation should be a top organizational priority.

Most of us in the technology space are familiar with the old project management adage that you can only pick 2 options: Good, Fast, or Cheap. Up until now, that was ironclad. For the first time, that tradeoff is becoming negotiable, but perhaps not in the way one would imagine.

Countless leaders envision AI dancing through their development ecosystem, magically creating new products with little need for the challenges that come with human designers or developers. Currently, that is a dangerous pipe dream. AI will change over time, but organizations that do not maintain oversight by skilled individuals will spend far more than they save to fix what they create.

The organizations that gain the most from AI over the next several years will not be the ones chasing fully autonomous software development. They will be the ones applying AI surgically to high-friction operational bottlenecks with measurable ROI.

But there is an area where AI is a paradigm shift that will enable success today. Ironically, one of AI’s clearest near-term enterprise use cases may sit in one of the least glamorous areas of software delivery: QA. Yes, that team that is always giving developers defects they cannot reproduce, or that finds a critical defect right before the team is supposed to ship.

Automated testing is not new. Teams have been rolling it into their release pipelines for quite a while now. However, while it helps speed up both testing for new features and regression testing, it has a few major drawbacks: creating and maintaining automated tests is both expensive and time-consuming; changes to functionality or UI can easily break the tests; and scripts and test data need to be recreated repeatedly.

As a result, to make automated testing a viable advantage, an organization requires a sizeable, dedicated team of automation engineers not only to create the initial tests but also to continually hunt down issues that break tests and fix them. It’s an expensive proposition.

Not anymore. How do we know this? Easy. We built it. Imagine this scenario: Mid-sprint, the QA team is ready to test a new feature. With the press of a button, our platform reviews the user stories, and within minutes, not days, a full suite of test scripts is created, including edge cases. Days later, a developer changes a button within the feature. Previously, that would have been a broken test that would have taken half a day of detective work to fix. With our platform, however, the test heals itself and keeps running. In case of a serious failure, a screenshot and video show exactly what broke and where it is. Triage has been reduced from hours to minutes, and the team ships on time.

For the CTO, that means faster, more predictable releases with less firefighting. The QA manager sees their team focused on actual high-priority issues, rather than manually creating scripts and chasing ghosts. Product and Marketing have an effective, brand-strengthening outcome, and Finance recognizes the value of a small automation team doing the work of a large one.

The organizations that gain the most from AI over the next several years will not be the ones chasing fully autonomous software development. They will be the ones applying AI surgically to high-friction operational bottlenecks with measurable ROI. QA is one of the clearest examples available today. That is exactly why we built TestWyze.

Ready to stop choosing 2 options? TestWyze helps organizations modernize QA with AI-driven automation that scales with the pace of development. Let it help you get Fast, Cheap, and Good.

We get it. Every company on the planet is asking itself and its partners what it can do with Artificial Intelligence. We get asked the question constantly. No one likes the answer: “Well, it depends.” Unfortunately, it’s correct.

Organizations cannot keep spending on AI like this without expecting a revolt, at best. The question is whether they are going to revisit their strategy now or hope that AI gets much better soon enough so the waste won’t matter.

Oxford Economics estimates the US AI enterprise spend was roughly $200 billion in 2025. US companies spent $1 trillion on all R&D in 2025, according to the National Science Foundation. That means AI accounted for 20% of ALL R&D spending for 2025, and it’s increasing rapidly.

That’s a huge number. Obviously, the next question from anyone fiscally inclined is “What value did we derive from that massive spend?” That’s when the consultants look back at them with a blank stare. The ROI on that spend is MISERABLE. Deloitte reports that only 6% of investments had a payback in under a year, and even among the best efforts, only 13% saw returns in that same period. In fact, AI pilot failure rate estimates range from 70% to 95%. By any measure, that’s insane.

Einstein never said, “The definition of insanity is doing the same thing over and over and expecting different results,” but that doesn’t make it any less true. Organizations cannot keep spending on AI like this without expecting a revolt, at best. The question is whether they are going to revisit their strategy now or hope that AI gets much better soon enough so the waste won’t matter.

We are definitely in camp #1. Don’t get us wrong, we are not advocating that AI spending stop. Not at all. We firmly believe that AI will add tremendous value in the long run. However, we do believe the throwing of AI spaghetti at the wall needs to end. Choosing AI use cases based on enthusiasm or fear that you need to do something because your competitors are is not a recipe for favorable business outcomes. It will only make future efforts harder.

AI adoption will not be easy. There are many variables that factor into whether an AI effort will succeed or fail. And even with favorable variables, AI is frequently and confidently wrong. A lot. That’s why a far more deliberate strategy is needed.

Our advice to organizations that are frustrated with a lack of AI success and those who are paralyzed by the breadth of the choices is the same: Take your cold shower. Learn to prioritize where to focus your resources. Used correctly, AI can be a strong accelerator for making your business better at what it does well. It cannot fix areas that already don’t work because they are immature.

Figuring out the right workflows for AI adoption does not need to be an exhaustive analysis of every single flow, system, and business unit in the organization. Talking to the right people will quickly reveal patterns across the organization that will allow for a solid 18-month roadmap that lets you focus on what will work now and what should be planned for.

Not sure where to start? We can help navigate through the noise and find your footing. Our AI Reality Check will give you the intelligence and confidence to move forward.

Ah, marketing pixels, those simple little JavaScript snippets that your marketing team gives you the night before a new campaign is supposed to launch. The ones that always work perfectly out of the box, never require customization, and always come with a clearly defined retirement date. Exactly those.

Not having tight control over those tiny little scripts in your tag management system can cause huge headaches, including privacy and regulatory compliance liability, broken conversion tracking, site performance issues, downstream retargeting failures, and security risks.

When was the last time you thought about them? How many of them are sitting in your tag management system right now? How many of them are broken because the event they depended on was changed months ago? Or worse, how many of them are still silently sending customer information to a third party with whom you no longer have an agreement in force?

Admit it, you’re just like almost all the marketing technology teams out there, you have no idea. Does your CISO know this? How about Legal? We understand. Analytics and MarTech are rarely in the forefront of teams’ planning. You love the idea of governance, but your team is always spending so much time dealing with last-minute requests like these, making sure new functionality is covered, and running ad hoc reporting analyses that it never happens.

But it matters. A lot. Not having tight control over those tiny little scripts in your tag management system can cause huge headaches, including privacy and regulatory compliance liability, broken conversion tracking, site performance issues, downstream retargeting failures, and security risks.

These tags often collect customer and behavioral data, whether they’re being actively provided or not. Lack of governance can expose organizations to violations of constantly evolving regulations, including HIPAA, CCPA, GDPR, and numerous others that are enacted each year. Not knowing what is being collected and where it’s going can, at best, lead to a fire drill when your organization is accused of infringing on this legislation, and, at worst, lead to actual penalties.

Most major e-commerce organizations rely heavily on third-party advertising and marketplace platforms to drive revenue. Broken conversion tracking causes a nightmare to reconcile, which erodes trust with both the retailer and the platform, and makes marketing spend attribution that much harder to determine.

It’s well documented that slower page load times negatively affect site conversions and SEO rankings. Industry studies show that a single second delay can reduce conversions by up to 20%. Too many, poorly configured, or broken tags can demonstrably cause slower page load times, leaving your team a prime scapegoat for the sales team’s failure to meet their quarterly goals.

Finally, these tags are designed to allow 3^rd parties to run code on your site without involving your engineering teams. This means they can download other scripts or perform other functions you may be completely unaware of. Your legal department will love hearing this reinforced when they ask for a list of everything that is being served on the site and what they are doing.

A good governance system alleviates these risks by making sure organizations have a registry that, at a minimum, tracks:

• Tag inventory and documentation
• What is being tracked
• How long it is tracked for
• Where the data goes
• Data layer standards
• Quality engineering procedures

But even a good governance system is only effective if it’s accurate. It’s also critical to periodically audit your tag management ecosystem to ensure that these procedures are being followed. Not sure where to start? It’s understandable. Looking at an overgrown garden is always daunting. We can help you take the first step by conducting an audit of your current state and creating a good governance program to keep you running smoothly. We can even handle your entire tag workflow, so your team can focus on other priorities.