Cost-reduction With AI/ML Is Easier to Track Than Revenue Gain — Tredence Co-founder and CRO

(US & Canada) Shashank Dubey, Co-founder and Chief Revenue Officer at Tredence, speaks with Robert Lutton, VP at Sandhill Consultants and Editorial Board Vice Chair at CDO Magazine, in a video interview about metrics demonstrating the value of initiatives, improving sustainability, the future role of AI in the enterprise, and advice to CDOs tackling last-mile problems.

Tredence is a data science company that provides data analytics and AI services to hyperscale enterprises.

Speaking of indicators and metrics that demonstrate the value of initiatives, Dubey elaborates on the different dynamics of top-line value and bottom-line value. For instance, he says that utilizing data science, AI, and ML to reduce costs is more attributable and easily measurable than using AI and ML to increase revenue.

Adding on, Dubey says, when building an algorithm to help a business acquire more customers, there are numerous expenses like marketing and sales. In this scenario, it becomes challenging to ascertain which customer acquisition model attributes to the larger scheme of things.

Therefore, while building solutions or getting sponsors, one goes to the CTO, CIO, and heads of supply chain, pricing, and merchandising, says Dubey. But, to establish a true value measurement of the initiative, the CFO is the go-to person.

To build on this, Dubey shares an example of working with a global manufacturer and demonstrating $60 million worth of cost savings in six months last year. This happened because the team built an AI-ML solution where the inventory was shifted based on perishability and demand forecasts to the distribution centers.

This course of action was certified by the CFO, and in addition to that, they reduced the inventory cost, improved on-time delivery, and reduced the cost of shipping and transportation.

In a nutshell, Dubey affirms that partnering with the Chief Financial Officer is critical to gaining the unbiased sponsorship and support needed to move the needle. Also, he advises organizations to be cautious while measuring the top-line revenue impact versus the bottom-line cost impact.

When asked how organizations can improve the sustainability quotient, Dubey notes that it has been a pertinent topic for customers focused on industrial manufacturing. As a first step towards sustainability, he mentions collaborating with CIO, CDO, and CDTO functions for the largest global manufacturers to build data-driven visibility.

Dubey maintains that while sustainability is a hot topic, the critical metric for a billion-dollar organization is to be able to measure sustainability initiatives in a well-governed and predictable manner.

The focus should be on designing the APIs in such a way that they increase investment in sustainability, generate better benefits, and reduce fines, says Dubey. He adds that many progressive companies have started to attract model customers through sustainability.

Additionally, Dubey asserts that educating clients on the fact that sustainability is not just a CSR initiative. If done right, it can become a competitive advantage to acquire more customers.

Commenting on the future role of AI in enterprises, Dubey sheds light on the current siloed organizational scenario. He recalls an incident of blockage in the Suez Canal where a ship got stuck, resulting in huge supply chain disruptions.

Describing its aftereffects, Dubey remarks that the organization did not respond to it as a unified whole. Rather, all the different organizational sectors started doing their own thing in silos.

This paints the picture of a disconnected organization that had no common response to an external disruption. The solution to this is to build a resilient business model that can deal with such geopolitical and sustainability risks.

Now, while building these shock-proof models, it is critical to underline the data architectures of AI and ML and create decision-making applications that are interconnected, says Dubey. For now and in the future, this could build the enterprise brain, which can work as a wholesome unit to drive an optimal response to external disruptions.

As advice to CDOs tackling last-mile problems, Dubey suggests considering data assets as revenue centers and not cost centers. Then he highlights how some seasoned CDOs are creating internal data marketplaces.

In order to reach there, Dubey believes it is critical to shift the paradigm from just building clean data to taking a step further and starting to build domain data sets. Furthermore, he urges CDOs to comprehend the wants of business users and create a specific data set that caters to a certain business problem.

In conclusion, Dubey says that the true value lies in the ability to build curated datasets. When CDOs succeed in building those, they can create an internal data marketplace and change the conversation from cost to internal revenue realization. This, in turn, changes the nature of data from being a cost center to a revenue center, making it a true business enabler.

CDO Magazine appreciates Shashank Dubey for sharing his insights with our global community.