4 gen AI success stories

Gen AI is a form of AI that outputs content — text, code, voice, images, and videos — from prompts. Since the release of DALL-E for images in 2021, and then, the following year, ChatGPT for text, and Midjourney and Stable Diffusion for images, companies have been eager to find out how gen AI can transform their services, workflows, and processes by better answering customer questions, speeding up employee tasks, writing code, or even helping scientists model the 3D structure of proteins. Research firm MarketsandMarkets predicts the gen AI software market will grow from $71 billion in 2025 to $891billion by 2032, a CAGR of just over 43% throughout that period. Here are four examples of how organizations are using and succeeding with gen AI today. Husqvarna brings gen AI to the factory floor Swedish industrial manufacturer Husqvarna is leveraging gen AI to reduce unplanned downtime on the factory floor. The company, which makes outdoor power products including robotic lawn mowers, chainsaws, and trimmers, has created the AI Factory Companion, a gen AI copilot that helps technicians and operators diagnose and resolve issues with machinery. They can describe the symptoms a machine is experiencing to the copilot and it’ll suggest tests to validate the issue along with possible solutions. “We can’t answer every question yet, but we can address the simple ones, like 20% of the full bucket, and in some cases, we can speed up the time to resolution very quickly,” says Jonathan Wickström, the company’s manufacturing digitalization lead. “If we don’t have the data, we can’t do anything, and it’ll be like that for some time. But now we can improve the common knowledge base instead of just filling in another PDF that no one will look at.” Wickström’s advice: Master traditional search technologies and combine them with gen AI to get the most powerful results. The magic behind a powerful gen AI companion is good search — finding the documents most relevant to a user’s questions. Honeywell transforms with gen AI Multinational conglomerate Honeywell is streamlining its end-to-end processes and reducing employee admin tasks using gen AI. The Fortune 500 company, which primarily operates in aerospace, building automation, industrial automation, and energy and sustainability solutions, uses Microsoft 365 Copilot to aggregate and summarize content, GitHub Copilot for code generation and software modernization, and is testing Microsoft 365 Copilot for sales and finance with Salesforce and SAP integrations. “This is a disruptive technology that’s fundamentally going to change how we work, live, and play,” says Sheila Jordan, the company’s SVP and chief digital technology officer. Jordan’s advice: Success with gen AI is all about having the right data strategy in place. “You can’t have a gen AI strategy without a data strategy,” she says. “The good news for us is that while our data isn’t perfect, it’s in such great shape that we really do run the company with data-driven insights. Now we can layer gen AI on top of that and unleash new knowledge, insights, and opportunities.” Ally Financial finds gen AI success with three guiding principles Bank holding company Ally Financial started its gen AI transformation with two use cases: call summarization and marketing . The call summarization capability was developed to support Ally’s customer care and experience group, and leverages Microsoft Azure and Azure OpenAI Service to produce detailed documentation for each call taken by call center associates. It provides real-time access to summaries of tens of thousands of customer service calls per week. The marketing team capability, on the other hand, uses the LLM chat and prompt functionality on Ally.ai, a proprietary platform for all the company’s AI applications, to help marketers produce creative campaigns. Ally says it’s helped reduce the time needed to produce creative campaigns and content by up to three weeks, primarily with early-stage tasks like research, first drafts, and naming exercises, resulting in an average time savings of 34% compared to typical processes without AI. Both use cases leverage Ally.ai, along with a gen AI playbook to allow the company to incorporate gen AI into its highly regulated business without sacrificing security or governance measures. “What I was very clear on from day one was that while you might be seen as an industry leader and get recognition for adopting gen AI early, even a small misstep would put us back 100 steps from the half step we’ve taken forward,” says Sathish Muthukrishnan, Ally’s chief information, data, and digital officer. “We didn’t want to be in that position, so we needed to protect ourselves.” Muthukrishnan’s advice: Follow three guiding principles when it comes to gen AI: start with focused, intent-based investment and build; have the courage to experiment with new technologies, but with the intent to showcase value in order to bring the entire organization together; have the patience and thoughtfulness to educate the entire company. “Don’t just experiment and invent within the four walls of tech,” he says. “Showcase the value of tech to the entire company, speak their language, and bring them along by educating and empowering the entire organization.” Henkel embraces gen AI as enabler and strategic disruptor German consumer packaged goods (CPG) company Henkel is using gen AI for trade promotion management (TPM) and trade promotion optimization (TPO) . TPM and TPO are key disciplines in the CPG space that involve managing and optimizing all promotional activities conducted with retailers, from discounts to deductions and payments. Henkel worked with the SAP co-innovation team to build a tool that leveraged the Just Ask gen AI feature of SAP Analytics Cloud. “The whole use of trade promotion, which is still a complicated animal, has become much more intuitive for the key account managers,” says Henkel CDIO Michael Nilles. “The key account manager or the salesperson is looking at the trade promotion data and it’s giving really great hints. This just wasn’t possible with traditional machine learning. With gen AI, the AI capabilities have become much more widely usable by people who aren’t PhDs in data science.” Now account managers can walk into a meeting with retailers about the proper discounts to offer during a campaign, and use the tool with natural language to explore options in real-time. The account managers can have a finished plan ready to share by the next day, instead of the months it used to take. Nilles’s advice: Think about building vertical LLMs specific to your business to really take advantage of gen AI. “We believe there’ll be new vertical LLMs emerging, even micro-vertical ones that are domain-specific,” he says. “Having LLMs for the right things will be a huge competitive advantage, and if we don’t do it, we’ll be threatened and jeopardized by others.”