SharkNinja is one of the few global consumer goods companies successfully deploying AI at scale. With over $5 billion in revenue, global operations and continuous expansion, the company faces extraordinary service demands. Rather than treating AI as a future experiment or cost-cutting measure, SharkNinja has embedded it throughout customer service, operations and business decision-making.
The result is a comprehensive set of practical use cases that simultaneously improve experiences for customers, agents and the wider business. Speaking in a recent webinar, Damian Hall, Senior Director of Global Consumer Experience at SharkNinja, who leads technology and knowledge for SharkNinja’s customer experience (CX) organisation, explained how the company thinks about AI deployment:
“This is an empathy release. These technologies are enabling us to spend more time doing the things that matter most to our consumers.”
Working in partnership with Zoom, SharkNinja has moved beyond experimentation to production-scale AI implementation. Here are the 10 main ways the company is using AI today.
1. Agent Assist: removing cognitive overload
One of SharkNinja’s earliest and most impactful AI use cases addresses a fundamental challenge in contact centres. AI listens to customer conversations in real time, automatically creates call notes and categorises cases without manual agent input.
This eliminates the need for agents to simultaneously listen, talk, type and classify. Instead, they can focus entirely on understanding the customer and resolving the issue properly. Over time, this becomes “business as usual,” making agents’ jobs simpler rather than more stressful.
2. Real-time knowledge support
AI supports agents by surfacing relevant information at the exact moment it’s needed. By analysing live conversations, the system suggests appropriate product information, troubleshooting steps or next actions, while calls are still in progress.
For a company with hundreds of products and constant innovation, this capability is critical. Agents no longer need to search multiple systems or rely on memory when supporting complex or older products. The knowledge comes to them automatically based on the conversation context.
3. Business intelligence: daily feedback to product teams
Every customer support conversation at SharkNinja feeds back into the wider business. AI-generated summaries are shared with engineering and product teams, often daily.
This allows SharkNinja to rapidly identify faults, design issues or recurring problems. In some cases, factories can be contacted immediately to implement changes. Damian explains the philosophy:
“Our job in the CX team is to be the squeaky wheel. Every consumer sentiment, every great experience, every terrible experience has to get back into the business so we can fix them.”
This transforms customer service from a reactive function into a direct driver of product improvement. The company now has visibility into consumer behaviour across markets, channels and time zones, including out-of-hours demand and regional differences. This data fundamentally changes how the business makes decisions, from staffing and opening hours to product design and digital investment.
4. Quality assurance at scale
Traditionally, quality teams can only review a small sample of calls. SharkNinja now uses AI to assess 100% of voice and chat interactions against consistent quality scorecards.
The AI evaluates elements such as greeting quality, issue resolution and tone. Human teams then calibrate and coach based on the results. This gives far better visibility into performance across the entire operation, in every market, regardless of language or cultural variations.
Damian notes that setting this up “Really isn’t that difficult. You’ve got to be very thoughtful about what you’re measuring, the questions and the scorecards you’re building. But actually, it kind of goes back, and that’s done in real time.”
5. Supervisor assist: zero escalations as the goal
AI also supports supervisors by flagging conversations that may need immediate intervention. This might be triggered by negative sentiment, prolonged silences or repeated customer frustration.
Instead of discovering issues after the fact, supervisors can step in immediately in real time, whispering advice, joining the call or resolving the issue before it escalates. Damian is clear about the ambition: “Why do we ever have any escalations? It’s because we don’t get to them soon enough. So we need to be able to see that in real time to go and deal with that.”
This represents a shift from managing escalations to preventing them entirely.
6. Digital concierge: intelligent self-service
On the customer side, SharkNinja uses AI as a digital concierge. Rather than forcing customers through rigid menus or FAQ structures, AI helps guide them to the right content, product or journey based on what they ask.
This is especially valuable when customers don’t know their exact product model or how to describe their issue. AI bridges that gap and gets them to the right place faster. The system uses retrieval augmented generation (RAG) to access SharkNinja’s extensive knowledge base and deliver accurate, contextually relevant responses.
7. Warranty automation: AI that does real work
One of the clearest examples of AI “doing real work” is in warranty journeys. Customers can be guided through structured troubleshooting without having to call an agent. Once completed, the information flows directly into SharkNinja’s systems.
If a replacement part is needed, it can be triggered automatically. Customers avoid lengthy phone calls, while agents are freed up to handle more complex or emotional issues.
Damian emphasises the importance of this approach:
“To be truly successful in this space, AI has to do stuff. Pointing to knowledge articles is helpful, but is that true containment? It’s difficult to tell. If you’re trying to build a business case, that straight line from containment to operational impact is very difficult to draw.”
The warranty journey represents genuine end-to-end automation. Getting a customer to a troubleshooting journey is valuable, “but actually then just saying you’ve got to call us at the end of that, there’s kind of no point.”
8. Transactional automation: order tracking and returns
AI handles common transactional queries end-to-end where possible: “Where is my order?”, cancellations and returns. These journeys are automated completely, allowing customers to resolve issues quickly without speaking to anyone.
Crucially, customers can still reach a human if automation doesn’t work for them. AI is used to reduce effort, not block access. This philosophy runs throughout SharkNinja’s AI strategy.
9. Sentiment analysis for agent wellbeing
AI tracks customer sentiment in real time and after interactions. This data is used to improve service quality and to protect agent wellbeing.
Supervisors can see when agents are handling particularly difficult calls and ensure breaks or support are provided when needed. The focus is on “releasing empathy,” allowing humans to do what they do best while the technology handles the administrative burden.
Damian notes:
“If you make an agent’s life easy, they enjoy it more. Our service agents are so adaptable that it actually becomes the norm, really, really quickly. So actually, I used to do call notes, now I don’t do call notes, move on. This isn’t perceived as a threat at all, because they know we’re releasing them to be more empathetic, to do more for consumers.”
10. Strategic insight: Understanding what matters
Perhaps the most significant use case sits above all others: insight. AI allows SharkNinja to see patterns across markets, channels and time zones that would be impossible to detect manually.
The company now understands how many consumers want to engage outside traditional contact centre hours. They can see behavioural differences between the US and UK markets. They know which product issues are emerging and where.
This visibility has “changed the narrative in our business,” according to Damian.
“We can now get 100% of what our agents are doing. We now see what a big proportion of our consumers are doing and asking questions about. It allows us to see the difference between our different markets.”
This accelerates the ability to advocate for consumer experience and enables the business to make more informed decisions about core infrastructure and business processes.
Key success factors: Speed, testing and relationships
Several factors have enabled SharkNinja’s success with AI at scale:
Speed of iteration: Damian emphasises “test and learn, test and learn, test and learn at a rapid pace.” The company is comfortable working in ambiguity, launching capabilities quickly, measuring results and adjusting. As he notes: “I certainly don’t know what my team’s gonna look like next year. But what I do know is that we’ve got a shopping list of things that we want to achieve because our consumers tell us those things.”
Contact centre first: SharkNinja realised substantial benefits within the contact centre before deploying consumer-facing AI. This built confidence in the AI models’ capabilities and made it easier to secure support for external deployment. Damian explains: “One of the reasons for our success is that we did a lot within the contact centre first. We really realised a lot of benefits in our contact centre operations space, which gives confidence that actually this is a really capable AI model.”
Internal relationships: Strong partnerships with IT, digital technology, DTC and engineering teams are essential. Damian notes there’s always “a little friction” because CX wants to move fast while governance needs to be maintained, “but we’ve got to drive the pace because we’re the voice of the consumer.”
Starting with proven use cases: Rather than attempting everything at once, SharkNinja selected specific use cases where AI could deliver clear value. This “I know enough to be dangerous” approach allows the team to be “safe enough” without breaking anything dramatic.
Knowledge as foundation: Damian also leads knowledge management for the CX organisation, recognising this as fundamental to AI success: “That’s such an underpinning part of whatever we talk with AI. When you’ve got hundreds of products out there in the marketplace, some of which are a number of years old that require support, that knowledge is critical to allow AI or any other process to deliver that level of support.”
Lessons for other organisations
SharkNinja’s experience offers several lessons for other businesses looking to deploy AI at scale:
AI is an enabler of scale, not a cost-cutting tool: The most successful deployments fill gaps as you grow rather than replace existing capabilities. Damian notes that 95% of organisations struggling with AI “did not realise that if you take out all your ease [easy calls], what happens to your average handle time? It increases disproportionately, and all the calls that are left are really complicated. So actually, it’s not about saving money. It’s about putting the money where the value is.”
Focus on journey containment, not chat containment: Many organisations obsess over containment within a single channel. SharkNinja focuses on whether the entire customer journey is resolved, regardless of how many systems or touchpoints are involved. As Damian puts it: “It’s about journey containment. I think that’s where people focus very heavily on chat containment.”
Expect organisational transformation: Laura Ball, Global CX AI Lead at Zoom, observes that successful AI implementations change how organisations view customer experience: “We’re changing the view of contact centre. We’re changing the view of CX because it isn’t just within a telephone contact centre anymore. It is a journey that touches various parts of an organisation.”
Embrace working in ambiguity: Laura notes that “you’re constantly working in ambiguity. You have a hypothesis of what you expect the design to do. The reality is, you won’t know until it’s in production. You have to be really comfortable working in risk and ambiguity.”
Measure what matters: Rather than getting lost in traditional metrics, focus on business outcomes. Damian emphasises that the real value is in how AI changes decision-making: “It’s changed the narrative in our business because we can now get 100% of what our agents are doing. We now see what a big proportion of our consumers are doing.”
The key takeaway
SharkNinja’s AI implementation demonstrates what’s possible when technology is deployed strategically at enterprise scale. With more than 10 distinct use cases spanning agent support, customer self-service, quality assurance and business intelligence, the company has transformed how it serves customers while supporting rapid business growth.
The approach is neither about replacing people nor chasing hype. It’s about using AI to remove friction, reduce effort and improve decision-making across the organisation. By starting in the contact centre and expanding carefully, SharkNinja has turned AI into a practical tool that benefits customers, agents and the business as a whole.
For a company growing at double-digit rates quarter after quarter, adding new countries and product categories at a pace, AI has become essential infrastructure rather than an optional experiment. As Damian summarises: “How do we serve our consumers better as we’ve grown and scaled tremendously? You can’t do that without engaging technology and enabling technology, because it just gets too disparate, too complicated, too big. Humans can’t control absolutely everything.”
Watch the full webinar to see exactly how SharkNinja applies these AI strategies across teams, workflows and customer touchpoints to support a $5B business.
