Inside, how modern companies are putting new AI tools to work in the name of expediting services for their clients.
AI is no longer just a back-office experiment; it is becoming the engine driving how businesses meet client expectations. When the technology was first unveiled to the world in late 2022, it was met with widespread skepticism and even outright resistance in many circles. Today, however, just a few years later, AI has become the norm, with widespread integration across numerous industries.
Companies are now deploying AI to eliminate inefficiencies, close knowledge gaps, and free their teams to focus on the high-value work clients actually need. Nonetheless, it is crucial to note that the leaders doing this well are not chasing the technology; they are solving specific problems with it, and building human oversight into every step.
How AI Is Reshaping Client Workflow Efficiency
CodeFreeIQ, founded by Abi Odedeyi, helps businesses implement AI by starting with the people closest to the work. The firm uses a pre-implementation questionnaire to map daily tasks and identify the highest-impact pain points before building any automation.
Abi explains, “I am not doing it top to bottom. I am doing it bottoms up. Because you will find out that it is not the senior management that is actually doing the major work. It is the departments, the different teams.”
This bottom-up strategy has produced measurable results: one e-commerce client recovered €100,000 in unpaid invoices in 60 days, while a property management firm automated its entire maintenance ticket workflow.
“It is very important to let AI do the things that are repetitive and mundane, while you let the humans do the reviews. That human in the loop is very important because without that, AI can send a generic email to anybody or respond in a way that is not fully representative of your brand.”
Abi further argues that involving frontline staff from the start is the difference between AI that sticks and AI that stalls.
“We are moving from just asking questions to actually having AI doing these things for us. The agents are taking it off our hands, and they are basically doing it themselves.”
Breaking Language and Knowledge Barriers With AI
Layer 3 Labs, led by Jonathan Teplitsky, works with global organizations facing communication and expertise gaps that slow down client delivery.
Jonathan elaborates, “Putting a document into Google Translate loses a lot of meaning. When you use something like OpenAI or Claude, it retains much of the meaning, resulting in greater understanding. We built a tool where you can take the source documents and chat with the document within the portal, so headquarters can both communicate better and make better decisions.”
The firm builds AI-powered portals that handle nuanced translation and allow users to query source documents directly, delivering over 50 percent in time savings for teams operating across more than 30 countries.
Jonathan further explains that, “We take all of the documents they built over the last 50 or 100 years and create a brain where a junior analyst can go in and ask questions against this set of documents without sounding stupid to a partner that you might not want to ask.”
A knowledge-transfer application creates a searchable brain from decades of firm documents, giving junior staff instant access to expertise that previously sat locked with senior partners.
Jonathan sees this as the beginning of a broader democratization of professional advice, saying, “I am really excited that normal people who do not have a tech background are going to have access to advice that they would not normally have because it is too expensive or it takes too long to get. This kind of decentralization of advice that normally comes from professionals is really exciting for small businesses and just for normal people.”
Meeting Clients Where Their Data Is
DeepMetis, founded by Ferdinand Biere, takes a diagnostic approach to AI implementation.
“Ideally, it works so in the background that there are just certain aspects of work that do not need to be handled at all anymore. The human works in the same interface as before, minus this chunk of work. The value generation just happens faster.”
The firm’s discovery process identifies what clients actually need, not just what they say they want, by finding higher-leverage opportunities based on objective data. A common barrier is what Biere calls “media breaks,” points where incompatible data formats between systems create manual work.
Biere details, “They know what they want, but they do not know what they need. For about half of them, there are lower-hanging fruits; something with a bigger leverage that could be set up significantly faster and get a bigger return.”
The firm’s 6- to 8-week implementation process targets these bottlenecks and delivers payback within 12 to 18 months. AI runs in the background within familiar interfaces, reducing friction without disrupting how employees already work.
“The whole context of background agents is currently emerging; agents that are long-running, that can be given objectives, and then they work on those objectives. I think this is going to be really interesting as the large language models get better.”
Scaling a Lean Team With AI Automation
Gratitude Driven, led by Marina Wyss, runs on a team of six part-time members who collectively equal about 1.5 full-time employees.
“We are six people, but everyone is part-time; we are kind of like one and a half full-time people. We have an active and growing YouTube channel and a community of 200 members. We have an agent system set up that works with Notion, Claude, and various MCPs to track where we are at, send reminders, and automatically ingest meeting notes into our project tracking system,” Marina says.
A system of roughly 20 AI agents automates market monitoring, content research, and project management, enabling the team to serve a 200-member community and a growing YouTube channel.
This frees up human workers in a unique way, as Marina details. “I would love to spend more time thinking about big picture stuff and then doing the human-only things like writing, responding to my community, or that more one-on-one work.”
Marina draws a deliberate line at interpersonal work: AI handles the volume, but community replies and coaching stay human. She argues that AI-generated responses in those contexts are generic and undermine the authentic connection on which the organization is built.
“I did for a little bit, have an agent helping me draft replies to community comments. I did that for a week or two, and I was like, this is not a good use case for AI. By definition, these models are going to regress to the mean, and people are not coming to me because I give them a default response.”
AI as a Quality Check
Kobiton, led by CEO Frank Moyer, addresses one of the most persistent problems in mobile software testing: device fragmentation.
“The variation in mobile devices puts so much stress on enterprise mobile developers, because what works fine on a Pixel 6 breaks on a Pixel 10. And the number of changes coming from development has increased by seven times since a year ago.”
As the number of device configurations grows and development velocity increases, traditional test scripts break more frequently. Kobiton’s AI identifies interface elements by user experience rather than HTML identifiers, creating tests that hold up across device variations.
Frank details, “Every Fortune 100 company we work with has a mandate to use AI to generate code. But there is no quality check there. What Kobiton does is provide a Claude plugin that allows you to quickly verify the quality of what you are generating; to make sure that the code that is generated is not AI slop.”
A cloud plugin also validates code generated by AI tools like Claude, preventing poor-quality outputs from reaching production. One health insurance client accelerated automation adoption by three times using the platform.
Frank further states, “I believe they are going to create an app store for AI plugins. Just like Google takes 30% off apps and Apple takes 30% off apps, we are going to have AI plugins that Anthropic provides to the market. I think that happens by the end of summer.”
Final Thoughts
The thread running through each of these companies is discipline. AI is a bold new tool, and one whose uses are still being tested and redefined on a near-daily basis. Each of these tools leverages AI to target a specific problem: a manual bottleneck, a language barrier, or a broken test cycle. As such, they limit AI’s capabilities while further concentrating its output.
As tools become more capable and agent-based systems take on longer-horizon tasks, the businesses positioned to benefit most will be those that have already learned how to direct AI with precision and keep human judgment close to the output.
