At Navigate Ventures, we support the founders of the leading AI enabled Enterprise SaaS  platforms solving the biggest problems in the world, as they look to bridge the growth capital  funding gap.  

We recently invested in Operative Intelligence, a leading AI-enabled enterprise platform for  contact/call center management, which aims to help organizations unlock the potential of every  human interaction by radically improving customer experiences, faster, and at scale. 

Its co-founder, Peter Iansek, is a hugely impressive founder with great insights into how to sell to  enterprise customers – the holy grail for venture-backed SaaS. I sat down with him to get the  lowdown on his journey and successes to date… 

‍Good to see you Peter. Why does AI represent a breakthrough for contact centers and the  world of customer experience?

Over the last decade, we’ve seen an explosion of new technology in this space, largely driven by  the migration to cloud platforms, while companies have also paid more attention to customer  experience as a differentiator to boost revenues, leading to significant investments in the space. 

Yet despite this investment, contact center volumes have continued to increase, meaning more  human agents are needed. Simultaneously, customer experience levels have actually dropped  to their lowest in a decade. The fundamental problem is that organizations have struggled to  work out why customers are reaching out in the first place. Contact centers are flooded with  data, but it’s difficult to pinpoint the drivers behind customer behavior, what customers truly  need, which agents are performing well, and which channels are delivering.  

This is where AI, and specifically our solution at Operative Intelligence, comes in. We use a  methodology for analyzing unstructured customer data and apply large language models  (LLMs) to automate that analysis, helping businesses uncover exactly what customers are  looking for in real-time. 

So Operative Intelligence can analyze all the data and give contact centers a better  understanding of customer needs? 

Exactly. Our AI helps answer the most foundational question: “What do customers really want?”  

We train models to analyze the unstructured data and provide insights in the customers’ own  words. Contact centers can ask questions of all their data and receive insights that reveal  specific, actionable issues. For example, some common problems that are surfaced using our  technology are navigation problems with the client’s app, or areas in the code that need  adjusting. Fixing these types of issues not only improves the customer experience but also  helps the wider business identify areas for improvement. 

Which industries or verticals are particularly suited to this technology? 

It’s not about specific verticals—it’s more about the size of the contact center. When a company  reaches a certain scale, the volume of data becomes unmanageable without automation.  

So we work with industries that have large customer bases, like banks, airlines, insurance  companies, retailers, software companies, and manufacturing. Many of these organizations also  have hybrid models, combining large in-house teams with outsourced partners. Our technology  is built to help these companies make sense of all the data coming from their customer  interactions, regardless of the channel they take place on. 

You’ve been selling AI to enterprises since 2020. What are the secrets of your success?

When we started, terms like LLMs and machine learning (ML) weren’t widely understood, so we  couldn’t rely on technology alone to make the case. Instead, we focused on the specific  problem we were solving—one that we had firsthand experience with from our time in the  industry.  

For enterprises, the main challenges with AI are data security and the sheer volume of AI  products entering the market. Many organizations are skeptical about AI because new solutions  pop up every week. That’s why we prioritized building up case studies and proof points quickly.  We addressed data security concerns by offering self-hosted solutions and custom training our  models for each client. 

Ultimately, it’s the specificity of the problem your solution solves that resonates with  enterprises. And in our case, we had the advantage of being operators ourselves—we’ve lived  through these problems. Our entire methodology was born from real-world experience, and  when we show prospective clients our decks, the images are from actual contact centers we’ve  worked with. The methodology is the real breakthrough; the tech is simply our means of  automating it. 

Are there any other common barriers you encounter when speaking to large organizations? 

One of the biggest challenges is skepticism. Many companies are wary of deploying AI without  a clear understanding of the problem they’re trying to solve. We’ve all seen examples of AI  chatbots going rogue because they weren’t set up with the right context. That’s why our  models are custom-trained for each client, ensuring that they’re aligned with the specific  customer base and use case. 

How have your enterprise relationships helped to shape your product roadmap? 

While our main focus is contact centers, we’ve seen that the insights we generate are valuable  across the entire business. For instance, product teams can use our data to improve self-service  options, while marketing teams can learn what’s driving customer inquiries. We’ve also seen our  technology used to hold project teams accountable—ensuring they’re addressing the  business’s most critical customer-related challenges.  

As we continue to build out our product functionality, addressing the full range of potential  business benefits is very much top of mind. 

How do advancements in large language models affect your business model? 

Most solutions in the market today use general models. These are easy to deploy—you can  send them your data, and they’ll get to work—but they lack the context needed to drive better  customer outcomes. Our custom models approach solve this issue and allows us to understand  the nuances of each customer’s data, which is where the real value lies.

The future of our platform is about leveraging these custom models to drive even more  automation. For example, today’s chatbots are great for simple, transactional responses, but  anything more complex and the customer is kicked over to a human agent. Because we deeply  understand the customer data, we’ll be able to power more advanced chatbots that can handle  increasingly complex queries without human intervention. 

What’s your view on where the AI market is headed, especially looking toward 2025? 

We’ve run large benchmarking surveys that show 39% of what drives customer contact could  be automated over the next five years. That’s the low-hanging fruit for enterprises, and it  represents a massive opportunity. At the executive level, there’s a clear mandate to invest in AI  solutions. Companies are becoming more comfortable with these investments, and they’re  realizing that platforms like Operative Intelligence can also surface insights into other areas ripe  for automation. 

One of the biggest hurdles we foresee is around infrastructure—getting different systems to  communicate with each other effectively, so they can enable more advanced AI use cases. The  availability of GPUs is also likely to be a major challenge in the year ahead. But as these issues  are resolved, the potential for AI to transform customer experience will only grow. 

That’s fascinating, Peter. Thank you for sharing your insights. 

My pleasure, Ivan. Thanks for having me. Peter 

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Ivan Nikkhoo is the Founder & Managing Partner at Navigate Ventures, an early growth fund  focused on B2B Enterprise SaaS companies outside Silicon Valley between series A and Growth  rounds, offering a risk mitigated strategy with short holding period and an accelerated path to  DPI.  

Ivan Nikkhoo, Managing Partner 

Navigate Ventures

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