Tad Martin, Co-founder & CEO of Collective[i], an enterprise AI company designed to optimise revenue growth, on how new tech can help manufacturers overcome challenges & boost revenue.

The past four years have been a roller coaster ride for the manufacturing sector – from Covid and a worldwide disruption of the global supply chain to remote work, massive labour migration (aka “the Great Reshuffle”), inflation, rising interest rates and oil prices, two regional wars and heightened tensions between the United States, China, and Russia, a bank collapse, a recession, and so on. Few industries have ever had to navigate such a volatile environment with such massive impact on what they do.

So-called Black Swan events are now the New Normal. It’s no longer enough to have Six Sigma level efficiency. To be competitive requires an unprecedented organisational resilience. All of the challenges above present massive issues in every aspect of operations, including sales.

Since the pandemic, sales cycles are longer with declining ACVs (Average Contract Values). This phenomenon can be attributed to a host of factors including the rise in remote work (making it hard to build strong connections) with sellers managing larger buying teams often impacted by turnover and budget cuts due to economic uncertainty.

If your sales team is complaining that it’s harder to sell, simply put, it is.

How does AI help? AI provides the timely intelligence required for people to adapt. In a sales context, it can enable a manager to spot problems and opportunities sooner (such as accelerating or slipping deals, target revenue goals that are at risk) so they can be proactively addressed. Some examples of insights newer forms of AI provide include automated CRM data capture and clean-up, dynamic daily forecasting, opportunity monitoring, pipeline health assessment, and connection recommendations to provide referrals, warm introductions, and references.

Newer forms of AI, like that provided by OpenAI and Collective[i] leverage what are called foundation models trained by massive datasets to unearth patterns and generate intelligence that can automate tasks that eat away at productivity, augment human judgment, and improve outcomes. Teams that leverage the intelligence produced by foundational models have the ability to navigate uncertainty as it unfolds rather than having to wait for it to happen and then reacting. The impact of that transformation goes far beyond sales.

Operating with agility is hard in providers of physical goods dependent on a complex supply chain and inventory infrastructure. Fulfillment of orders often involves rigid processes and physical locations. Unlike a website which can be changed instantly, the operations of more complex manufacturing organisations require planning and coordination at scale.

Forecasting sales dynamically and reliably allows more time to procure goods and services. AI can also be used to predict when certain orders will materialise, enabling more efficient inventory management. All of the above can be integrated into how manufacturers price their products.

As soon as the AI spots an opportunity that is likely to close, procurement and fulfillment can weigh in on the optimal pricing instead of leaving pricing (and discounting) to sales who lack insights into the impact of their choices on margins. The results are staggering and implementing tools like AI helps by removing the productivity costs of manual forecasting for sales teams, enabling the supply teams to optimise margins, and providing buyers with a significantly better experience.

The concept is similar to what Amazon has done in the consumer space. Virtually everything that makes it easy to buy on Amazon (and gives them such massive advantages in retail) is informed by AI that has been trained on both Amazon’s own data and that of the third parties the company allows to sell on their platform.

Knowing what a consumer has bought in the past and combining that with emerging buying patterns (surfaced by AI) enables Amazon to predict what you are likely to buy next. That powerful insight informs the recommendation engine that presents personalised options to every user, driving almost a third of Amazon’s revenue.

The gains don’t stop at sales. Those predictions also enable Amazon to pre-ship goods to warehouses nearest to the customer in advance of the purchase being made. Through this, Amazon can maximise lifetime customer values, better manage their inventory costs, and provide buyers with amenities like same day shipping.

Now imagine if you had access to an AI engine that gave you Amazon level insights but designed for B2B buyers. Understanding their evolving patterns allows you to optimise your own operations. The AI would provide you with greater revenue and upselling capabilities. That same intelligence helps to improve supply chain management and fulfillment, resulting in a much better customer experience. It’s a virtuous cycle where everyone wins.

There are now applications that offer manufacturers AI-powered solutions to enhance various aspects of their sales operations, including marketing, pricing, forecasting, sales performance analysis, and CRM. By leveraging these platforms, manufacturers can gain valuable insights, improve decision-making, and ultimately drive sales growth.

However, achieving all of the above cannot be done with technology alone and having the AI in place is not enough. AI changes how companies operate. For starters, not all AI is the same. GenAI represents a new form of AI that mimics human intelligence much more closely than its heuristic predecessors. What matters most is the sources of data that have trained the models. If it’s solely provided by you (the older form of AI), you will not have access to the killer insights that lead to exponential gains.

As an example, to forecast revenue and provide opportunity specific odds, an AI provider must have access to training data that is dynamic, unbiased, and vast enough to surface buying and macroeconomic patterns that can be compared to your historical selling history. Without this data, the intelligence will be biased towards the past and off of only your experience. It can’t spot emerging buying patterns or forecast dynamically.

If the industrial age was all about assembly line execution (doing the same thing better and better), the next era of business will be about agile execution where companies adapt and respond to change more efficiently using AI as the engine to inform remarkably better operational execution. 

Strong leadership is required to align people and machines require knowledgeable and exceptional leadership. Not only do people need to know that they are supported and empowered to collaborate with AI, but also, need to be retrained on a new set of skills and rewarded for doing so.

AI represents a different paradigm and is perhaps the most disruptive technology of our time. AI is about presenting timely intelligence that empowers people to make better decisions. It requires reorganising how people work to respond to the signals it surfaces and adjust so that every aspect of output is optimised.

New technological advances will help the manufacturing industry better manage both demand and supply at an unprecedented scale. Goldman Sachs has predicted AI will contribute trillions of dollars to GDP in the next decade.

Many manufacturing companies feel as though they aren’t ready to dive in. AI sounds scary and many workers fear they will lose their jobs. The reality is that AI rewards the brave. Companies who successfully embrace it will have insurmountable advantages over slower competitors. 2024 is the year that the industrial sector will embark on this transformation. The benefits to doing so are enormous.

Tad Martin is the co-founder and CEO of Collective[i], a leading research organization at the forefront of artificial intelligence development. Collective[i]’s foundation model focuses on B2B commerce leveraging neural networks and deep learning to support the mission of using AI to help people and companies prosper. Collective[i]’s first application is designed to help sales organizations and professionals leverage advanced AI to forecast and grow revenue through insights and improved productivity. Prior to Collective[i], Tad was part of the management team serving as the head of Operations and Merchandising for Overstock.com helping to scale the company from a start-up to over $800M in annual sales. Prior to Overstock, Tad was part of the team who successfully secured Salt Lake City's bid for the 2002 Winter Olympics. Tad graduated from the University of Utah with a Bachelor of Arts degree and, in addition to his passion for cutting edge technology, is an avid skier and cyclist. 

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