Lessons from enterprise document workflows on what makes AI work at scale

About Templafy Talks 

Templafy Talks is our ongoing webinar series focused on document automation, AI, and how people actually work in modern organizations. Each session brings together live conversations, real use cases, and practical lessons from enterprise teams. 

In this session, we explored what AI maturity really looks like in large organizations. We discussed why documents are such a useful way to assess AI maturity, and how factors like governance, data readiness, and user experience shape whether AI delivers real value or creates new problems.  

Speakers  

• John Tiniakos, Senior Principal Customer Success Manager, Templafy 
• Christian Lund, Co-founder, Templafy 
• Oliver Gudmandsen, Senior Enterprise Account Manager, Templafy 

As AI tools become more powerful and easier to access, the real value of AI depends less on having the tools and more on how well they are used. 

AI maturity is about how reliably an organization can use AI in everyday work, at scale. When AI is used without proper planning or guardrails, results can be inconsistent and compliance risks can increase. 

Our discussion showed that AI maturity develops over time. Organizations move through clear stages, starting with experimentation and later moving toward more structured use. Early on, teams test possible use cases, and over time the focus shifts to data quality, governance, and fitting AI into existing workflows. 

This is why AI maturity is now a business issue, not just a technical one. Without a shared understanding of what good AI use looks like, organizations may invest heavily in AI without seeing lasting results. 

Where AI maturity becomes visible: everyday documents

“There’s a massive gap between how users see themselves and their maturity. Some folks are really far along…but they’ll say we don’t really have a structure yet, we’re not that mature. While other customers have maybe done a pilot of some sort and thought they were very advanced.” 

Oliver Gudmandsen

Senior Enterprise Account Manager, Templafy  

Many organizations believe they are further along with AI than they actually are. Teams may be experimenting with tools or running pilots, but those efforts often sit at the edges of the business rather than inside core workflows. 

That gap shows up quickly when AI is not designed to scale. Individual teams might see short-term gains, but without shared standards, results are inconsistent. Some employees rely heavily on AI, others avoid it, and output quality varies widely, leading to overall friction.  

“In companies with 100,000 employees, everyone is [creating documents.] That makes it a very good place to assess AI maturity.” 

Christian Lund

Co-founder, Templafy

This is why documents and presentations are often a good place to start, both with assessing AI maturity and improving it. Unlike dashboards or niche tools, documents are created by almost everyone, every day. They’re high-volume and high visibility, making them an honest reflection of how mature your AI usage really is. 

When AI maturity is low, it shows up in documents through uneven messaging, outdated content, and extra manual work. As maturity improves, documents become easier to create and safer to share.  

Data readiness is the quiet blocker of AI success 

AI maturity often stalls for reasons that have little to do with the AI itself. In many cases, the real constraint is your data.  

For AI to produce reliable results, the information behind it needs structure, context, and clear ownership. When data is outdated, scattered, or poorly managed, AI reflects those problems in its outputs. This shows up clearly in document workflows, where small errors or inconsistencies can quickly spread across teams. 

A key theme in the session was the need to reset expectations. Most organizations are not fully ready for AI, and that’s normal. Improving data readiness is often less about adding new technology and more about doing the basics well. Clearly defining approved content, trusted data sources, and how information should be used gives AI the foundation it needs to work effectively. 

AI should adapt to users, not the other way around

“Users shouldn’t need to become prompt engineers…The companies moving fastest are the ones putting AI tools in people’s hands that don’t require new behavior. People use them almost without noticing.” 

Christian Lund

Co-founder, Templafy

One of the clearest signs of AI maturity in an organization is how easy it is for employees to use it correctly. When AI tools require people to learn new interfaces, figure out complex prompts, or change how they work, adoption drops quickly. 

Mature AI fits naturally into existing workflows and is guided by clear intent. Instead of asking users to explain how AI should work, the system already understands the rules, context, and goals of the task. 

Governance makes AI usable at scale 

“For a long time, AI was exciting because of speed. Something that took three hours suddenly takes twenty seconds. But speed alone isn’t enough. You also need quality, accuracy, and consistency.” 

Christian Lund

Co-founder, Templafy

Speed and automation are valuable, but without clear limits, they can introduce risk just as quickly. When AI creates content at scale, even small mistakes can spread quickly. Mature AI adoption takes this risk seriously and builds governance directly into the workflow, rather than adding it as a separate review step. 

The discussion made it clear that governance is not about slowing teams down. It’s about building trust. When rules for brand, legal, and approved content are built into AI systems, people can work faster without questioning the results. 

Understanding the five AI maturity stages

If you’re following along with the full webinar, you have the chance to complete the AI maturity assessment and get your score. Based on your answers, your organization will fit into one the five different stages:  

Learn more in the full AI maturity assessment guide

What AI maturity looks like in practice

“What we mean by AI maturity is where our customers, or the market in general, are on that journey. A while back, everyone agreed that AI is something you have to have. For enterprises, it’s a prerequisite now. But what that actually means looks very different from company to company.”

Christian Lund

Co-founder, Templafy

AI maturity often stalls for reasons that have little to do with the AI itself. In many cases, the real constraint is your data.  

For AI to produce reliable results, the information behind it needs structure, context, and clear ownership. When data is outdated, scattered, or poorly managed, AI reflects those problems in its outputs. This shows up clearly in document workflows, where small errors or inconsistencies can quickly spread across teams. 

A key theme in the session was the need to reset expectations. Most organizations are not fully ready for AI, and that’s normal. Improving data readiness is often less about adding new technology and more about doing the basics well. Clearly defining approved content, trusted data sources, and how information should be used gives AI the foundation it needs to work effectively. 

Watch the full Templafy Talks session 

This article highlights just a few of the insights shared during this Templafy Talks session. To explore the full discussion and see real examples, watch the complete session on demand.