AiThority Interview with Niken Patel, CEO and co-founder, Neuron7.ai

Niken Patel, CEO and co-founder, Neuron7.ai, talks about how Neuron7’s Smart Resolution Hub enhances efficiencies by integrating with existing workflows and adapting to user feedback, in the AI Q&A: 

———–

Hi Niken, tell us more about Neuron7 and what inspired the platform.

Neuron7.ai is a leader in Service Resolution Intelligence, designed to help complex service organizations resolve issues more efficiently on the first attempt. The founding team of Neuron7 has spent 20+ years in the customer service and customer experience (CX) space, repeatedly encountering the same problem: resolving customer issues in complex service environments is challenging due to knowledge being scattered across various data sources and in the minds of individuals. We realized that it was time to blow up the legacy model of knowledge locked in silos, documents, and people’s heads. We realized that AI could offer a new solution that would be transformational by consolidating this dispersed knowledge, creating net new knowledge, and enabling more effective and scalable issue resolution.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

How has the platform evolved over the years?

From the beginning, we have been hyper-focused on people and accuracy. While most AI solutions analyze data, we knew that it was critical to allow people to share their expertise to achieve the highest levels of accuracy in service. We initially focused on Intelligent Diagnostics, which provides turn-by-turn guidance to resolve any issue, for any product.

We thought that “search” had already been solved, but soon realized that our customers needed help here too. Now, we also provide Intelligent Search that is purpose-built for service, providing exact answers and links to specific rows in tables, sections within massive documents, and exact moments in a video to help service teams resolve issues.

We continue to innovate with an extensive roadmap to deploy product modules that will create comprehensive Resolution Intelligence for enterprises into the future.

Tell us about Neuron7’s Smart Resolution Hub and how it enables end users and partners.

Until today, keyword-based search on hundreds of knowledge base articles, manuals, and documents was the best way the service world relied on to find resolution information. When this approach is challenged by product and issue complexity at scale, it falls short. As Service Council’s recent research showed, 81% of service engineers will phone a colleague when they are stuck, which ties up additional resources and reduces efficiency.

Reimagining this entire approach, Neuron7 brings together knowledge from thousands of people and vast data sources to create a Smart Resolution Hub that helps customers, agents, technicians, and engineers easily find the best path to resolve issues.

Similar to a dynamic navigation system, ​the Hub provides turn-by-turn guidance through the best next steps within your existing CRM, chat, or any other service workflow. There is a feedback loop that makes the AI smarter but also captures expert knowledge in real-time from the company’s best service minds. New resolution steps are captured and are immediately available across the service continuum. By continually learning from your data and people, Neuron7 consistently delivers 90%+ resolution accuracy, which represents a massive improvement over the status quo and translates into millions in cost savings.

Can you share a few thoughts on how new AI innovations are changing CRM, Data, Chat and overall workflows in SaaS?

Multi-channel cloud CX giants created the system of record that captures incidents, cases, and work orders. The next evolution that will be layered on to this will be the system of intelligence. AI is fueling lightning-fast innovation and it’s exciting to see the range of new use cases popping up within existing platforms. In the world of service – issue classification, routing, and automated resolutions for simpler cases are now available out-of-the-box with leading CRM and chat providers. This is showing people the power of AI in their day-to-day professional lives.

And what’s exciting now is that service leaders are clearly ready for this massive AI transformation. Service Council’s recent release of The Service Leader’s Agenda indicates that “service innovation” and “investment in technology” ranked highest in reported priorities for service leaders and “artificial intelligence” ranked highest in the categories of technology investment.

Also Read: Natural Language-Based Scientific Analytics

What are some of the biggest challenges to adopting AI-powered tools that you see service teams struggle with?

User adoption and accuracy are critical, correlated success factors for AI. Adoption drives accuracy and accuracy drives adoption If the first 5 AI queries do not yield accurate results, a user is not going to put in the 6th request. Accuracy is paramount (and this is likely why Google won out over Yahoo as the search engine of choice).

Another risk is that an AI tool that is not integrated into the workflow (day-in-a-life) of your existing system of record can add to the silos that already exist. That will lead to user adoption issues and long term accuracy issues. Seamlessly integrating accurate predictions within the day in the life of a user is key.

How can service organizations optimize their use of AI to drive digital journeys and workflows more seamlessly?

The biggest piece of advice we share with potential customers is to identify a key challenge where AI might be able to help in their organization and start testing its capabilities sooner rather than later. Every organization has messy data, multiple systems, competing projects, cynics, and other reasons to delay. But service leaders who are driving AI innovation are seeing huge gains in service quality, efficiency, employee experience, knowledge capture, faster onboarding, and competitive advantage.

The first AI use case helps clarify how data can be captured more effectively going forward and helps service leaders understand what additional use cases will move the needle for their digital journeys and workflows. As I previously mentioned, it’s absolutely critical that any AI solutions are seamlessly integrated within your existing workflows. You simply won’t get adoption if users need to access another system.

If you had to share five of the biggest fears and misconceptions around AI before we wrap up, what would they be?

1. GenAI hallucinates: While GenAI on its own is prone to “hallucinations” and inaccuracies. These can be eliminated with a combination of data governance and guardrails, but most importantly by leveraging GenAI in the right manner.
2. AI replaces jobs: AI makes life easier for service leaders, frontline agents and technicians, and customers, and doesn’t replace them. Our users have responded extremely well to a tool that enhances their productivity immensely.
3. AI is still unproven in the enterprise: We see large companies getting up and running with 90%+ accuracy, no churn, and 250% NRR. AI is real and its something that even the most complex service organizations can use today.
4. Building AI is easy: Building AI solutions that deliver accuracy at scale is an incredibly complex undertaking. Most leading AI companies have been developing their solutions for many years. We routinely talk to companies who have unsuccessfully tried to build their own AI solutions and could have saved themselves a year or two of headaches by finding the right AI partner.
5. Generic AI is enough: Complex service environments have a very specific set of use cases. Generic AI platforms and AI that’s baked into CRMs are great at solving some of these challenges, but customized AI, in combination with Natural Language Processing and Neural Networks, produces results much more accurately and quickly.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]