AI Solutions for B2B Customer Experiences – with Shahar Chen of Aquant

Riya Pahuja

Riya covers B2B applications of machine learning for Emerj - across North America and the EU. She has previously worked with the Times of India Group, and as a journalist covering data analytics and AI. She resides in Toronto.

AI Solutions for B2b Customer Experiences – with Shahar Chen of Aquant@2x

This interview analysis is sponsored by Aquant was written, edited and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

AI is becoming a game changer for so many industries, but against many predictions from a decade ago – that manufacturing companies are currently foremost among many areas of adoption is somewhat surprising in hindsight. Traditional logic tells us that manufacturing is tech-adjacent in significant areas but slow to many forms of adoption.

Yet manufacturers are now seeing serious ROI with AI initiatives across numerous lines of business. While some business-to-business (B2B) enterprises are still figuring out the right path to successful adoption, many have already successfully leveraged the technology. 

In a study from the World Economic Forum, one automotive manufacturing company came forward to reveal that AI helped the company save up to 40 percent in energy used and reduce costs by 60 percent by preventing the use of excess welding materials. AI has further helped manufacturers largely solve some of their most persistent problems in areas like sales and inventory forecasts.

Emerj Senior Editor Matthew DeMello recently spoke with Shahar Chen, CEO and Co-founder of Aquant, on the ‘AI in Business’ podcast to discuss the role of AI in manufacturing and how copilots can make manufacturing companies more efficient. In the following analysis of their conversation, this article examines two key insights:

  • Implementing AI as a complementary tool to bridge knowledge gaps: Leveraging AI to bridge knowledge gaps, enhance problem-solving efficiency and improve post-sale support amid intense competition and workforce changes in manufacturing.
  • Increasing accuracy and efficiency with copilots: Using AI copilot platforms to navigate complex tasks and problem-solving in industries like manufacturing to streamline operations, expedite decision-making and optimize resource utilization. 
  • Different approaches to AI for B2B and business-to-customer (B2C) enterprises: Contrasting the stringent precision demanded by AI in critical B2B scenarios, where zero-tolerance for errors is imperative, against slightly forgiving margins in areas of B2C workflows. 

Listen to the full episode below:

Guest: Shahar Chen, CEO and Co-founder, Aquant

Expertise: Generative AI, Business Solutions, Computer Science, and Business Development

Brief Recognition: Shahar is the CEO and Co-Founder of Aquant. He previously spent 14 years at ClickSoftware as the Director of Solution Center, among various other senior roles. He graduated with a Bachelor’s in Computer Science at Bar-Ilan University.

Implementing AI as a Complementary Tool to Bridge Knowledge Gaps

Shahar opens the discussion by pointing out two significant challenges in modern manufacturing: intense competition and low profit margins. Regardless of the product type – whether in cars, 3D printers, or medical devices – the market is highly competitive, leading to minimal profit margins for manufacturers.

The conversation then turns to the post-sale phase of manufacturing relationship life cycles, where providing customer support becomes crucial. He identifies a substantial gap in the manufacturing industry in meeting customer expectations and addressing issues that arise after product shipment. A key factor contributing to the support challenge is the need for more experienced talent in the workforce. Shahar notes that, in the past, employees would accumulate extensive experience within a single company over several decades. 

However, the contemporary trend involves shorter job tenures, limiting the opportunity for individuals to gain in-depth knowledge and expertise. He suggests that AI technologies can provide support, analyze issues and bridge the knowledge and experience gaps caused by the changing dynamics of the workforce. In doing so, new emerging AI capabilities can contribute to meeting customer expectations and maintaining satisfaction in the manufacturing industry.

He further addresses the evolving perception of AI within the manufacturing industry, professing to observing a palpable shift in attitude towards these technologies over the past few years. Previously, the mention of AI on the manufacturing floor would evoke a sense of fear among workers, as there was a common perception that AI might be introduced to replace human jobs.

Today, Shahar emphasizes that the current understanding is different. He suggests that people now recognize that AI is not here to replace them, but to assist and enhance their capabilities. The perspective has shifted towards AI being a tool to help individuals perform at their best in their respective roles.

Shahar then shares a real-world example involving a technician who has been working with complex industrial printing equipment for 30 years. The technician, despite being highly knowledgeable about the mechanical aspects of the equipment, only needs help with network issues in the digital era. 

He points out that AI can address the technician’s challenges by troubleshooting and guiding end-users through problem-solving processes before a senior technician is dispatched to the field.

Increasing Accuracy and Efficiency with Copilots

Shahar further outlines three key areas where he believes AI will have a significant impact in manufacturing workflows, all of which are tied back to improving operational uptime throughout the industry: 

  • Predictive Maintenance: Using AI analytics tools to predict when equipment will likely fail and perform maintenance proactively, preventing costly unplanned downtime. Shahar emphasizes that the ROI for predictive maintenance derives from the fact that being proactive about fixing equipment can be up to 100 times cheaper than reacting to a failure after it occurs. Traditionally, achieving predictive maintenance has been a long-term goal for many organizations, and now with advancements in AI, it has become achievable with consistently reliable ROI. 
  • Intelligent Diagnostics or Troubleshooting:  Once a machine has already broken down, the focus shifts to getting it back up and running as quickly as possible. AI can assist in guiding users through a series of questions to identify the root cause of the problem, allowing for more efficient and targeted problem resolution.
  • Concept of Copilots: A copilot is an AI system that works alongside a human operator, providing guidance and assistance. He appreciates that a copilot doesn’t take over the role completely but helps navigate and fill knowledge gaps, ensuring that the human operator remains in control. This collaborative approach is essential when specific expertise is needed, such as troubleshooting network issues in the example provided earlier.

“When you’re talking about copilots for specific industries like manufacturing or service or any of these, it becomes even more critical that the answer will be accurate. And it will take time for you to get to the right answer without calling the guy that might be on a conference call for four hours, or maybe the guy retired yesterday. He’s no longer available, but you’ll still know. So it’s the knowledge in the test used to be in the head of people right in their mind as an experience that they gain. Now that knowledge is the IP of the company, the IT of the manufacturing company, then it stays in the organization forever.”

– Shahar Chen, Co-founder and CEO of Aquant

Different Approaches to AI for B2C and B2B Enterprises

Shahar distinguishes between B2B and B2C scenarios in this part of the discussion, specifically in AI implementations and data quality.

  • Stringent Requirements in B2B: He emphasizes that in B2B scenarios, particularly in critical industries like healthcare, where medical devices are involved, the level of accuracy and reliability in AI-generated responses is paramount. Unlike B2C situations where a margin of error might be acceptable (such as fixing a household appliance), there’s a zero-tolerance for errors in B2B, especially with medical devices or construction equipment. The consequences of a failure can be severe, affecting patient health or causing significant financial losses.
  • Importance of Bulletproof Answers: He stresses that responses in B2B scenarios must be “bulletproof.” There’s no room for ambiguity or guesswork. The data used to generate responses must come from the organization’s most reliable and authoritative sources. This requirement reflects the critical nature of the problems being addressed and the need for precision in solutions.
  • Challenges of Data Quality: One of the significant challenges in B2B implementations — data quality is that many organizations start with data that may be perceived as “garbage” or noisy. The challenge lies in quickly and effectively filtering out the noise from the data to ensure accuracy. Shahar emphasizes the importance of leveraging the experience and expertise of individuals within the organization to expedite this process.
  • Speed and Accuracy in B2B: Shahar underscores the importance of speed and accuracy in B2B scenarios, where time is money and delays can have serious consequences in sectors like healthcare, potentially affecting lives. The ability to rapidly and accurately solve problems, especially for newer employees who have yet to accrue decades of experience, is a significant advantage facilitated by AI.
  • Impact of AI on Skill Development: Shahar provides a powerful example of a young employee, having joined the company only six months ago, being able to solve a complex problem at a hospital involving an MRI machine in just 10 minutes. He contrasts this with traditional workflows, where it might take individuals 30 years to develop such high levels of expertise. It highlights the transformative impact of AI on skill development and problem-solving capabilities within a relatively short time frame.

In concluding the conversation, Shahar highlights the changing dynamics in the industry, particularly the evolution of customer expectations over the past few years. He emphasizes the critical importance of time in addressing issues, noting that previously, companies would send someone promptly – even if they knew that person might not be capable of solving the problem – to stop the clock and avoid penalties. However, Shahar points out that this approach is no longer acceptable in today’s landscape:

“Ten years ago, all we cared about was that the technician that was assigned to solve our cable issues at home would arrive on time. Now arriving on time is given. Now I expect that you’re going to arrive and you’re going to solve my problem. I don’t want three guys to come to my house. I don’t want three guys to come to the hospital and fix that MRI. I want the first guy that comes in to know everything there is to know about that machine and solve my problem.”

– Shahar Chen, Co-founder and CEO of Aquant

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