AI is killing white collar work faster than enterprises can adapt
A document automation executive argues that lawyers, bankers and consultants face a fundamental reckoning as AI eliminates the analyt
The grinding analytical work that fills the days of lawyers, bankers and management consultants is disappearing, not gradually, but at a pace that most enterprises have neither anticipated nor prepared for.
Artificial intelligence (AI) is eliminating the middle layer of knowledge work, and the professionals who have built careers on it are running out of time to adapt.
The shift is more disruptive than previous waves of automation because it strikes at cognitive rather than manual labor. Unlike factory automation or robotic process automation, which displaced workers at the lower end of the skills spectrum, the current wave is arriving from the top.
“It is pretty naive to say that transition of work is not happening. There is work that used to require a human to be involved which is not the case anymore,” Christian Lund, co-founder of Templafy, told TechJournal.uk in an interview.
“What remains super important is two things: the direction setting, meaning how to ask the question and what you want to achieve, and the ability to assess the quality of the output,” he said. “Everything in the middle, all the analysis and putting together decks and documents, that is what is going away.”
“All the highly paid, sought-after, highly educated people, including lawyers, bankers, accountants and consultants, will need to reposition themselves to play a new role, with AI being stronger and stronger. It is very hard to compete with because AI has all the brainpower imaginable. You have to be able to use it rather than produce it,” he said.
He added that the shift had also overturned a long-held assumption about the nature of questions. The old saying that there are no stupid questions, only stupid answers, was in his view now completely wrong. With AI, the quality of output depends entirely on the quality of input, making precise prompts the most critical human skill in knowledge work.
Templafy was founded in Copenhagen in 2014 and is now headquartered in the United States. It helps large organizations automate the generation of business documents, presentations, emails, spreadsheets and contracts, serving more than four million knowledge workers including clients at Adobe and across the Big Four accounting firms, major banks and leading law firms.
The company’s customer base spans North America and Europe in roughly equal measure, concentrated in industries with high proportions of knowledge workers: investment banks, hedge funds, accounting firms, law firms and large consultancies. Templafy has around 150 employees, of whom approximately 45% are engineers.
Control lost to AI
The interview covered enterprise AI adoption and the growing gap between what AI promises and what it delivers at scale.
The displacement of knowledge workers sits alongside a parallel crisis inside the organizations that employ them. Enterprises have lost control of how AI is being used, and the consequences, including compliance failures, inconsistent outputs and reputational risk, are becoming impossible to ignore. A PwC survey found 56% of companies are seeing no return on their AI investments.
“The main concern about adopting AI in general is that things go rogue, that they have no control,” Lund said. “They used to be able to apply control by putting a piece of software on people’s computers and saying: this is what we want you to do. ChatGPT changed that. It has been democratized, which in an enterprise context is pretty disturbing.”
He said enterprises had gone through a rapid sequence of responses: initial excitement at what AI could do, followed by alarm at losing control, then a more pragmatic search for tools that could deliver productivity gains without exposing the business to risk. The old model of controlling employee behavior through prescribed software workflows was gone.
“Enterprises want to make sure that as users get stuff done, they inject themselves in the middle to make sure it is done in a way the business can live with, that does not put liability and risk on them. More productivity and efficiency without the risk is what they are looking at,” he said.
Against that backdrop, Lund identified three areas where Templafy distinguished itself from general-purpose AI tools and large language model (LLM) providers: quality, consistency, and cost. On quality, he said general AI tools could produce visually impressive outputs but failed at the specificity required for enterprise documents.
“We have been using the term: is it a toy or is it a tool? It is impressive what can be done if you provide a prompt to an LLM, but it is hard to use for much. If you are doing a proposal, for example, you cannot have margins of error. It has to be accurate every time,” he said.
On consistency, free-form prompting inevitably produced divergent results across a workforce. Ten users trying to produce similar documents would ask slightly different questions of potentially different models and get very different outputs. On cost, he described a model-routing approach that assigned different AI models to different parts of a workflow.
“Let’s have Claude do the brain work, where it is really difficult: the research, the analysis. But we might use a different model to actually produce the deck, because it is a lot cheaper,” he said. “We are distributing the workload the way you would in a non-AI world, where you use different skill sets and different pay grades for different tasks.”
Patenting the instruction layer
To address the control problem at its root, Templafy has developed a patented approach to generating what Lund called “instruction books” for AI.
Rather than relying on individual users to write effective prompts, the system captures a user’s intent and dynamically generates a detailed, contextually rich instruction set passed to the AI model before it begins work.
“If you want to get to highly reliable results when you build business documents with AI, you have to be very instructive,” Lund said. “We have a patented way to build instruction books for AI. We can build these dynamically on the fly, so instead of eight words from a user, it would be extrapolated into 30 pages of very specific context, including data, so the AI can do a much better job.”
The technology draws on Templafy’s decade-long history in document template automation. The company has also built Model Context Protocol (MCP) integrations, allowing it to operate as an embedded capability within Claude, Gemini, Microsoft Copilot, and other leading AI environments, so users need not switch platforms to access its document-generation tools.
“We prefer to support users working where they already work. Businesses are picking one or two of the bigger LLMs and saying: that is our starting place for work. ” We want to make sure we are just like an ingredient or an enzyme that lives inside that,” he said.
Lund said Templafy was already partnering with major LLM providers including Microsoft and several newer entrants, and expected those relationships to deepen significantly. He said major LLM providers were increasingly seeking specialist partners to bridge the gap between general-purpose AI capability and enterprise-grade use cases.
“We are not trying to compete with the LLMs. What we want to do is make Claude, OpenAI, Gemini and Copilot better than they are today by filling in the gaps,” he said. “If you go bottom-up and have a very product-led approach, you will get a lot of users very quickly, but it is difficult to sustain your business. If you want to make a good business on AI, you have to get closer to the actual use cases.”