Sales & Scale: 5 Proven Ways Real Companies are Using Generative AI to Drive Growth
Explore the power of AI in business: bridging the gap between potential and action.
It's already a well-established fact that Artificial Intelligence (AI), particularly Generative AI, is an excellent catalyst for productivity, research, and automation—a tool driving tangible results in the real world across various industries.
However, the issue is that many business professionals still feel uncertain about where to start. They have heard about AI’s potential to boost revenue or streamline processes, but translating that into action feels overwhelming. Developers, on the other hand, might craft cutting-edge AI tools, only to find they don’t quite fit the unique workflows—like Standard Operating Procedures (SOPs)—that businesses rely on. This disconnect is what we’re here to solve.
This article bridges that gap—with real-world client case studies from AWS, Google Cloud, and Microsoft—to show how AI delivers results across industries. We will explore how companies are utilizing Generative AI to address fundamental challenges and achieve measurable outcomes.
Whether you’re a small business owner looking to save time or a tech enthusiast eager to innovate, these examples will spark ideas you can adapt today. Think of this as your roadmap to making AI work for your business—complete with practical steps and a free tool to kick things off.
5 Strategic Ways Businesses are Using Generative AI
1. Enhance Employee Productivity and Efficiency
AI can improve work quality and speed up routine tasks, such as automating customer support, drafting targeted marketing messages, or adjusting pricing in real time to maximize sales. This allows employees to concentrate on essential tasks and only handle escalations that are too complex for automation. It’s like giving every employee a super-smart assistant that handles the grunt work, leaving them free to focus on strategy and creativity.
Case Study: DoorDash – Revolutionizing Support with Voice-Based AI
DoorDash, the leading last-mile delivery platform, manages a staggering volume of support requests—hundreds of thousands daily—from customers, merchants, and its 2 million+ Dashers. With 37 million monthly active users, quick and accurate support is critical, especially for Dashers on the move who rely on voice interactions. Their existing AI system, built on Amazon Connect and Amazon Lex, was a start, but it fell short. Too many calls escalated to human agents, agent transfers were frequent, and manual testing of new features bogged down the team. The result? Frustrated Dashers, overworked agents, and rising costs.
The Solution
DoorDash partnered with the AWS Generative AI Innovation Center to develop a game-changing solution: a voice-based AI contact center, which was deployed in just eight weeks. Here’s how they did it:
Amazon Bedrock powered the system with Anthropic’s Claude 3 Haiku, a model designed for lightning-fast responses (under 2.5 seconds latency), perfect for real-time voice support.
Knowledge Bases for Amazon Bedrock used retrieval-augmented generation (RAG) to pull answers from DoorDash’s public help center, ensuring responses were accurate and context-rich.
Amazon SageMaker automated testing, scaling capacity by 50x—think thousands of test cases per hour instead of a handful done manually.
Strict data privacy measures ensured no sensitive info leaked, keeping Dashers’ trust intact.
This wasn’t a minor tweak—it was built to handle DoorDash’s massive call volume without breaking a sweat.
The Results
The impact was immediate and impressive:
Agent transfers dropped by 49%, freeing up staff for tougher issues.
First-contact resolution jumped by 12%, meaning more Dashers got help on the first try.
Annual savings hit $3 million by cutting escalations and inefficiencies.
Thousands fewer calls reached live agents daily, letting them focus where they’re needed most.
“Using AWS and Anthropic’s Claude, we’ve built a solution that gives Dashers reliable and simple-to-understand access to the information they need, when they need it.
This has cascading positive impacts on our users and the platform as a whole.”
—Chaitanya Hari, Contact Center Product Lead, DoorDash
DoorDash isn’t stopping here—they’re now adding event-driven features, like AI resolving issues without human input.
Key Takeaway
For businesses with high customer interaction volumes, Generative AI can transform support from a cost center into a competitive edge. Start small—automate common queries—and scale as you see results.
2. Gain a Competitive Edge with Faster Innovation
AI serves as a catalyst for Research by allowing businesses to prototype ideas, launch new features, and pivot quickly, leaving slower competitors in the dust. It serves as an R&D team that never sleeps, churning out solutions at warp speed.
Case Study: UKG – Turning Data into Decisions with Conversational AI
UKG, a global leader in HR and workforce management, serves over 75,000 organizations. Their challenge? Managers and employees were drowning in raw data—workforce trends, engagement metrics, you name it—but turning that into actionable insights was a slog.
Manual processes were slow, siloed, and couldn’t keep up with the demands of hybrid work and culture-driven management. UKG needed a way to make data talk, fast.
The Solution
UKG partnered with Google Cloud, jumping on Vertex AI’s Large Language Models (LLMs) early. They wove this tech into their Human Capital Management (HCM) suites to create:
Conversational AI: Employees could ask questions naturally, like “What’s driving turnover this quarter?” and get clear answers.
Unified Search AI: Insights from across the platform were now just a quick query away, eliminating the need to dig for relevant results.
Data Fusion: LLMs merged UKG’s proprietary AI with Great Place to Work® datasets, delivering deep, context-aware insights on team sentiment and operations.
The Results
The payoff was a leap in decision-making power:
Managers made faster, data-driven calls, cutting analysis time dramatically.
Employees engaged in more productive conversations, thanks to instant insights.
Leaders could predict downstream effects—like how a policy tweak might shift morale—before acting.
“Our collaboration with Google Cloud will help employees and leaders make better decisions, have more productive conversations, and anticipate how today’s choices can impact tomorrow’s operations and workplace culture overall.”
— Hugo Sarrazin, Chief Product & Technology Officer, UKG
“This partnership helps build great, technology-forward workplaces and ensures that teams have access to the leading technology they want to engage with every day.”
— Thomas Kurian, CEO, Google Cloud
Key Takeaway
Innovation isn’t just about new products—it’s about rethinking how you use data. Generative AI can turn your existing info into a strategic weapon, especially if you’re in HR, ops, or analytics.
3. Prevent Fraud and Manage Business Risks Proactively
Risk is a constant in business, but AI flips the script. It spots anomalies, flags threats, and even simulates “what if” scenarios, helping you stay one step ahead of fraud, compliance issues, or operational hiccups.
Case Study: GitLab – Securing Software Delivery with AI-Powered DevSecOps
GitLab, trusted by over 50% of the Fortune 100, powers secure software delivery for millions. However, as demand for speed increased, so did the risks. Manual vulnerability reviews slowed development, and traditional security tools couldn’t keep pace.
Their 2023 DevSecOps report revealed 62% of developers already used AI for code testing—GitLab needed to meet that energy with a solution that balanced speed, security, and compliance.
The Solution
GitLab teamed up with Google Cloud, tapping Vertex AI’s foundation models to launch Explain This Vulnerability. This feature:
Analyzes code vulnerabilities in natural language, explaining them clearly.
Suggests fixes right in the DevSecOps pipeline, cutting resolution time.
Uses data isolation to keep sensitive code secure and compliant.
Built under Google’s Built with Google Cloud AI program, it joins tools like “Explain This Code” and “Code Suggestions”, making GitLab a leader in AI-driven development.
The Results
GitLab’s aiming for a 10x efficiency boost in DevSecOps:
Teams catch and fix vulnerabilities faster, reducing exposure.
Collaboration between developers and security pros tightened, cutting friction.
Trust in code quality and compliance soared.
“Our partnership with Google Cloud enables GitLab to offer private and secure AI-powered features, while maintaining customer data in our cloud infrastructure.”
— David DeSanto, GitLab’s CPO
Key Takeaway
If your business handles sensitive data or complex workflows, AI can harden your defenses without slowing you down. Think of it as a proactive shield, not just a reactive patch.
4. Reduce Operational Costs Through Automation
Automation is the low-hanging fruit of AI, reducing costs in areas such as marketing, support, and content creation. Generative AI can write emails, manage chats, or summarize reports, all while keeping quality high and budgets low.
Case Study: Newman’s Own – Scaling a Nonprofit with Microsoft 365 Copilot
Newman’s Own, a nonprofit that donates 100% of its profits to children in need, operates leanly. Their small team juggled legal briefs, marketing campaigns, logistics updates, and financial reports—routine tasks that ate into time better spent on their mission. CEO David Best summed the company’s needs up: “We need to keep growing, operating efficiently, and staying relevant to consumers and their needs.”
The Solution
Newman’s Own adopted Microsoft 365 Copilot, a virtual coworker embedded in Office apps like Outlook, Word, and Teams:
Marketing: Generated social posts and trend responses, tripling output.
Legal: Drafted briefs and simplified jargon, like a junior associate.
Logistics: Cut news summary time from a morning to 30 minutes.
Finance: Sped up insights to stretch mission dollars further.
Accessibility: Boosted confidence for employees with dyslexia.
The Results
The numbers tell the story:
Saved 70 hours/month on industry summaries.
Saved 50 hours/month on marketing briefs.
Launched 3x more campaigns monthly.
Boosted engagement, retention, and team inclusivity.
“Using Copilot and AI to its fullest potential so we can be smarter than our competition is a key piece of our recipe for success.”
— CEO David Best
“It’s hard to believe a piece of technology has this kind of human impact. But it does.”
— Bruce Wallace, Chief People Officer
Key Takeaway
Even small teams can punch above their weight with AI automation. Start with repetitive tasks—such as drafting and summarizing—and watch your efficiency soar.
5. Improve Customer Experience at Scale
Customers crave fast, personal service, and Generative AI delivers just that—think chatbots that feel human, emails tailored on the fly, or content that hits the mark every time. It’s about scaling connections, not just transactions.
Case Study: Skylark Group – Bringing Warmth Back with AI Robo
Skylark Group, which operates over 3,000 restaurants, including Gusto, has rolled out digital menus and robotic waiters to streamline operations. But it came at a cost: less human interaction left customers feeling disconnected.
The team hesitated to adopt AI, fearing “hallucinations” or a cold, robotic vibe. Director Yutaka Ikeda said, “The purpose of the Co-Store Manager is to enhance customer experience with exceptional human service while using generative AI to complement it.”
The Solution
Skylark built AI Robo with Azure OpenAI Service (GPT-4o):
Offered chat and voice recommendations in Japanese and English, pulling from real-time menu data via Azure AI Search and Cosmos DB.
Used RAG to fine-tune responses daily, keeping them accurate and warm.
Added a human-in-the-loop QA process to give AI Robo a personality—complete with a backstory—making it engaging, not mechanical.
“Azure OpenAI demonstrated better comprehension than other services and grasped the subtleties of Japanese. You really feel like you’re speaking to a human staff member.”
— Yoshie Fujimoto, Team Leader, Menu System Design
The Results
The human touch returned, with a twist:
Customers bonded with AI Robo, giving it nicknames and playing quizzes, driving repeat visits.
Staff embraced AI, pitching ideas like kitchen assistants or recipe recall.
Upselling and innovation spiked as fear faded.
“Some customers have become regulars because of the opportunity to chat with AI Robo. They say ‘Thank you,’ ‘I’ll be back,’ and even play quizzes with it.”
— Manami Nakazaki, UI/UX Designer
Key Takeaway
AI can enhance, not replace, the human element. Build it with personality and watch it deepen customer loyalty.
Get Started with AI Today
Feeling inspired? Google Cloud’s Generate a Solution is a free tool to help you brainstorm AI solutions for your business. Just describe your challenge—say, “How can I speed up customer support?”—and it’ll suggest tailored ideas to explore. Check it out at https://cloud.google.com/ai/generative-ai#what-problem-are-you-trying-to-solve.
Want a deeper dive? Reach out to me directly—I’d love to help you pinpoint where AI can make the biggest impact for you. Let’s turn potential into action, together.
Loved this! seeing real-world case studies is the perfect antidote to decision fatigue. It moves the conversation from a vague "what if" to a more tangible "what's next".
Here's some more LLM applications I really enjoyed reading about:
https://www.evidentlyai.com/blog/llm-applications?utm_source=convertkit&utm_medium=email&utm_campaign=%F0%9F%91%A9%E2%80%8D%F0%9F%8E%93%20LLM%20Eval%20Course%20Day%201:%20LLM%20benchmarks%20-%207841176