How Companies Are Getting AI Integration Wrong
AI has transformed all aspects of society. None more, however, than workplace dynamics and how companies operate. The future of many companies’ success will depend on how well they integrate large language models (LLMs) into their existing workflows.
The generational divide
Younger people have naturally adopted AI faster. Like many in their early 20s, I first started using ChatGPT the moment it was released. In 2022, it was a fantastic companion for ideation, planning, and much more.
Fast forward a few years and AI is completely flattening the competitive landscape. In 2026, a 22-year-old graduate has the power to launch a software company at a speed and scale that previously required a large team.
The challenge at many firms is getting veteran employees to pick up these skills. Some companies, such as Accenture, are now tying AI adoption to senior leadership promotions.
Resisting AI today is akin to resisting the internet or the smartphone in the 90s. There is a clear danger of falling behind and being outperformed by competitors. Unlike the internet or the smartphone, AI is changing at an incredibly fast rate. Stark changes are not year to year, they are month to month.
The biggest mistake companies make
Some companies are trying to do too much at once, hoping that AI will solve all business problems in one go.
Instead, focus on a specific area where AI can drive positive business outcomes. Get the product into people’s hands and iterate quickly, expanding outward from there.
What is the wrong first move for AI integration?
Companies should avoid dumping an entire knowledge base into an LLM (some of which is outdated) thinking it will be a panacea for all business ills.
This is a mistake because most business problems are ambiguous in nature. Without a clearly defined ROI, you are just guessing at best. Besides that, LLMs can produce confident yet incorrect answers (hallucinate), posing legal and reputational risks.
Smart integration should begin with low-risk tools that don’t require exposing highly sensitive data. Identify one painful workflow, measure the time and cost saved, and then apply AI narrowly. There, you can prove measurable improvement. Only then should you expand.
How to start with AI
One example could be creating an AI bot aimed at helping support teams answer questions faster on the phone. The average phone call drops from 4 minutes to 2 minutes and 30 seconds, and client satisfaction scores increases from 3.4 to 3.8 out of 5. There, you have quantified time saved and improved client satisfaction.
Other examples could include an AI meeting summariser, usage analysis to identify clients at risk of churn, or email analysis to find urgent chains. These are narrow in scope, with an ability to measure its success.
AI is not just a chat bot
The recent shift in AI is from a response-generating chat tool to one that embeds directly into existing applications.
Claude Integration: Claude can connect to Outlook, PowerPoint, and Excel, giving it the ability to search emails and create error-free documents.
GitHub Copilot: An AI code companion embedded in VS Code, useful for autocompletes and debugging.
Cursor: An AI-assisted IDE allowing developers to build applications faster.
Of course, there are many more. The broader narrative is that AI is no longer a separate tool you switch to. Today, AI is being embedded directly into the software companies already use on a day-to-day basis.
AI models are widely accessible. We’ve all played around in ChatGPT. Companies won’t succeed just because they use AI. They will succeed because they make smarter decisions on how to apply it.
What are the risks of AI?
Some business leaders are concerned about the cost of integrating AI. They should, however, consider the cost of not integrating it. The risk of being left behind with this technology is not worth taking.
Yes, AI incurs costs - but here are the questions to ask yourself:
Are human resources freed up to work on more lucrative projects?
Does client churn decrease?
Are you attracting new and larger clients because of more advanced products?
Firms are also concerned about security and data leakage. This is a valid concern. However, if companies decide not to provide AI tools because of security concerns, employees are still going to use them anyway, only perhaps in a less secure manner.
AI and the future of the workplace
The AI landscape shifts fast. For example, ChatGPT 3.5 scored in only the 10th percentile of the bar exam, whilst GPT-4 scored in the 90th. On February 19th, Sam Altman claimed that we could see superintelligence as early as 2028. Superintelligence is the phase of AI where its cognitive abilities surpass those of any human across any domain. At this level, AI would not just assist workers. It would outperform them. At everything.
However, we do not know for certain the future of AI. What we do know is that the businesses that build AI into their workflows now will be better positioned to adapt the fast-moving world of AI than those that don’t.