Now that the AI panic seems to have subsided, it’s time to reflect on what we’ve learned about AI and the direction it’s taking us. Over the past years, we’ve seen a tremendous amount of hype and significant financial investment in AI. But what have the results been? Despite the buzz, many companies, including high-profile ones like Microsoft, have reported during recent quarterly calls that they have yet to see a meaningful return on revenue from AI investments. Sounds like something awfully similar that transpired in the 90s when another platform shift was taking place. People rushing into a new technology without knowing if the outcomes would directly translate to realized organizational value.
Even though the immediate financial impact may not be apparent, I firmly believe there is ample evidence that AI is still the right path for companies to pursue just like the Internet was. This technology holds the potential to become the next major platform. AI has the power to enhance productivity, especially in roles like computer programming, where the efficiency gains are most obvious. But what about outside of obvious use cases like computer programming and writing clever poems?
We’ve repeatedly heard claims that “AI will 10x your productivity!” But what does that really mean? What does even a 2x increase in productivity look like? As the initial rush to invest millions into AI starts to slow down, we’re seeing a shift towards a more measured approach. The biggest impact we’re witnessing with our clients is through the development of custom AI tools that address specific internal challenges.
I personally believe the market has been misplacing its focus by creating generalized AI products and then trying to force use cases into those molds. The real impact for organizations, I believe, will come from building tailored AI tools and problem agnostic platforms. Most companies with the budget to invest heavily in AI also have legacy systems and established software that have been in place for decades. These complex frameworks demand equally complex solutions.
By developing custom AI tools that address specific, often simple, use cases, companies can incrementally enhance productivity. With consistent implementation, these tools have the potential to double or even triple an organization’s productivity over time. Below are some of the custom AI tools we’re currently building, designed to solve problems that directly impact the bottom line and drive organizational efficiency.
Business is more global than ever, which is undoubtedly a positive development. However, one of the significant challenges we face is language barriers. For instance, if you’re an American working with a potential client in Japan, how do you effectively bridge that gap? Up until now, human translators have been the go-to solution. But with the advances in AI, real-time AI translation is becoming a viable option. You might ask, “What about Google Translate?” While it’s a useful tool, it often lacks the ability to grasp the nuances of local languages. For example, English is the primary language in both the U.S. and the U.K., yet without an understanding of regional nuances, words can easily be misconstrued. In business where effective communication often times determines success or failure, context is extremely important. An AI translator, trained not only on the primary language but also on the subtle cultural nuances of a client’s locale, can have a profound impact on the relationship. This technology not only breaks down language barriers but also enables real-time, meaningful conversations with your client.
A natural progression of this type of technology, especially for multinational clients, is in document translation. While most AI models excel in natural language processing, recent advancements have led to models capable of recognizing and interpreting PDFs and documents in context. This is a significant leap because, unlike humans who learn through sight, smell, and touch, AI is now incorporating imagery to add a layer of complexity that written language alone cannot convey. International companies often spend millions translating documents manually. The reality is, these human translators sometimes struggle to fully grasp the content they’re translating and may resort to tools like Google Translate, which can lead to suboptimal results, especially when critical information is at stake. By fine-tuning AI models on similar documents and the specific translation language, these models can outperform human translators. The result is more accurate translations, enriched with context, reduced dependency on manual labor, and potentially millions saved on the expense sheet.Sales is the lifeblood of any organization. Without sales, companies simply cannot survive. In large organizations with established businesses, the volume of RFP (Request for Proposal) inquiries can be overwhelming. Filling out custom RFPs is often a tedious and time-consuming process that can take hours. When your sales team is operating at full capacity, this task can detract from their ability to pursue warmer leads and close deals. The pressure to quickly turn around RFPs can also lead to inaccuracies, which can harm the business. So, what’s the solution? Should you hire more sales reps or enhance their productivity with AI? A custom AI tool can be developed as a solution. An intelligent agent trained on previous RFPs with a deep understanding of company data. If sufficient public data about the client is available or the potential client provides enough context about themselves and their needs, this AI agent can accurately suggest which products would be the best fit for that organization. Furthermore, this agent can generate RFPs on behalf of the sales reps, pre-filled in the native RFP format, ready for client review. The benefits are clear. Hours of valuable time are returned to the sales team, potential customers receive prompt responses, and the company saves costs by not needing to expand the sales force.
The final tool I want to discuss is more complex but could have the most significant organizational impact. Imagine a framework of AI agents specifically designed to replace knowledge workers. These agents are built to consume information on whatever they’re trained in, retain that knowledge, and then act on it in the same way a human expert would. It might sound like something out of Black Mirror, but these agents essentially become specialists in their respective fields and collaborate as a team to carry out tasks. It’s important to emphasize that this is designed to be an agnostic platform—these agents can learn and develop deep knowledge and context about whatever problems they’re solving and tasks they’re performing. Take, for example, a McKinsey researcher whose role is not only to gather information but also to write articles based on that research. With a team of trained AI agents, you could have one agent dedicated to scraping the internet for relevant data, another agent tasked with writing and correctly sourcing the information, a third agent responsible for formatting the papers according to company standards, and finally, an editing agent. Each agent is trained on different sets of information but works together towards a common goal. A paper that might take a McKinsey consultant weeks to research and write could be completed in just a few hours. Over time, these agents would accumulate expertise specific to your business, achieving a depth of knowledge that few in your company could match. Just as people become experts through learning and experience, these agents are built to do the same.
As we navigate this next platform shift, the key to successfully navigating it, lies in how we leverage AI’s potential. The tools and frameworks discussed here are not just futuristic ideas, they are the foundation of a more efficient, productive, and globally connected business landscape. By embracing AI not as a one-size-fits-all solution but as a customizable, problem-solving partner, organizations can not only survive but thrive in the face of these technological changes. The real challenge isn’t in adopting AI, but in how strategically and thoughtfully it is integrated into the DNA of a company’s operations. Those who recognize AI’s potential, not just in theory but in practical, tailored applications, will be the ones who lead this new era, turning disruption into opportunity, uncertainty into a competitive edge and a true productivity multiplier.
