That’s why, for its initial deployment, Bosch Power Tools is using agentic AI to assist human agents, not replace them — and is keeping humans in the loop as a safety precaution.
“The users will be our agents,” says Victor Nguyen, the company’s project lead for genAI in business operations. End customers won’t be exposed to the new agentic AI systems directly. “We’ll have autonomous AI agents supporting our human agents.”
Bosch is using Cognigy.AI as its AI platform, which supports integration with multiple back-end AI models. “At the moment we’re using [OpenAI’s] GPT 4.0 and [Google’s] Gemini,” says Nguyen. “We’re quite flexible.”
It’s also integrated with the company’s CRM system and ticketing system. “We have also integrated it with a translation service, so we can translate email text or document attachments,” Nguyen says.
The system is currently in the second pilot phase, he says, and will be used by live human agents for real cases starting in May. In June, it will be deployed to the first customer service center, out of 23 at the company.
The eventual goal is to have the platform be widely used across the company, he says. “Bosch is such a huge company; Power Tools is just one division,” he says. “We will join forces with other Bosch groups to make it a scalable solution. We’re closely collaborating with our central IT to make sure this is globally scalable.”
The biggest challenge, he says, isn’t the agentic technology but the lack of company-wide standardized processes.
“In Germany, say, there might be a different process for changing an order than if someone in Latin America was doing it,” he says. “And there are different systems being used. We reviewed screens and made sure we standardized them as much as possible, though there will always be some country-specific stuff.”
Nguyen recommends that companies looking to roll out agentic AI for customer service start standardizing data and systems as soon as possible.
“Most people think that AI is the solution, that AI will fix everything,” he says. “That’s not the case. The first homework to do is to get the good data, good quality data, and make sure it’s maintained. It’s not just a one-time task to upload the data somewhere.”
AI agents for document processing
Enterprises have been using chatbots to process documents for years. Generative AI is good at, say, summarizing, or pulling out specific information.
But with agentic AI, an entire document-focused workflow can be automated.
Marketing firm Route Three Digital recently built an AI agent for a customer using Google’s Vertex platform and Gemini genAI models to automate a process that used to take seven days as the client’s users collected documents and information to create a proposal.
“We wrote the code and scripted it to capture all the pivotal information into one master document, then use Gemini to clean up the text and make it more readable,” says Sharmilla Singh, the company’s chief marketing and operations officer.
It’s still not completely foolproof, she says, and there is still a human involved to review the final document and tailor it as needed. But when the tool launched last year, the client saw a multi-day workflow reduced to a few hours.
The next step, she says, is to have an AI agent that does everything. “The goal is to remove the human,” she says.
Marketing is a relatively low-risk use case for agentic systems, Singh says. “It’s not going to take down your company.”
Other use cases for AI in marketing include search engine marketing and online advertising. “If you don’t stay on top of new methodologies, you could lose market share,” she says.
Agent democratization
Google’s Vertex AI is just one of many AI agent building platforms that’s trying to make it easier to build and deploy AI agents. In April, Google also announced that its Agentspace platform, first launched in December, now has a no-code agent designer and pre-built agents for tasks like deep research and idea generation.
Google has also launched an agent marketplace and opened it up to partners. As of this writing, there are 138 agents offered on the platform, from companies like Deloitte, VMware, Amdocs, Palo Alto, Wipro, and Dun & Bradstreet.
But Google is just starting to catch up to the 800-pound gorilla that is Microsoft’s Copilot Studio. It has already been used by more than 160,000 organizations to build agents, said Charles Lamanna, Microsoft’s Corporate VP of Business and Industry Copilot, in a March update. More than 400,000 custom AI agents have been created in the previous quarter alone, he added.
Other companies offering AI agents include AWS, with its Bedrock Agents, as well as Salesforce, ServiceNow, Workday, and SAP.
What’s more, AI model makers are beginning to bake agentic capabilities into their core products. OpenAI, for example, just announced two new reasoning models with agentic AI functionality and tool use built right in. In the future, businesses may not even need third-party agents or agentic platforms.
But agentic AI technology is still so new that “it’s a little too early to get any real feedback from enterprises” about their experiences with it, says Gartner’s Nag. “I don’t think it’s ready for prime time yet, or even if it’s ready for prime time, it’s not something that people are adopting wholesale.”
And there’s still a lot of healthy skepticism about the technology, he says. “I think that will be mitigated over time and you’ll see it become more pervasive in various functions — IT operations, sourcing, procurement, finance, and a whole bunch of other things.”
“It’s not there yet,” he adds. “But it’s becoming a little bit more real.”
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