All companies have budget dollars for AI and machine learning. They just might not call it that or allocate it that way – yet.
Know My Company
Tell us about your interaction with smart technologies such as AI and Cloud-based analytics platforms.
I’ve been involved in smart technologies such as AI and cloud-based analytics platforms for consumers, retail and consumer packaged goods (CPGs) for almost 20 years. Initially, I was involved with smart sensors, tracking customer interactions with products, and also traffic flow from a queuing perspective. Then, I moved into on-shelf availability and shelf analytics, where AI and Machine Learning is applied to computer vision, converting images of products on shelves into usable data. That data is then able to blend with inventory and POS data to give CPGs and retailers insight into how to optimize shelves and become more competitive.
Now, with Symphony RetailAI we are leveraging the capabilities of AI and Machine Learning to provide prescriptive recommendations, through data automation and analysis, to optimize revenue and margin opportunities. Symphony RetailAI is automating what has typically been done manually with spreadsheets or BI tools, now bringing speed and scale to the process.
How did you start in this space? What galvanized you to start at Symphony RetailAI?
I started in the software space right out of college at PwC, and then I went on to Oracle where I moved from a consultant into sales. At Oracle, I was taught the art of guerrilla marketing and sales, and I got the bug for selling software and technology. Following that, I’ve participated in two IPOs for startups, and then recently a startup to acquisition.
I was motivated to join Symphony RetailAI for a number of reasons. First, it’s an untapped market — that is, the utilization of AI and Machine Learning in revenue optimization. This is a new innovation and it’s an exciting opportunity to be a trailblazer. Secondly, working for a SymphonyAI company means the opportunity to work with and under the leadership of successful and brilliant minds such as Romesh Wadhwani and Pallab Chatterjee, who are well known and have proven success in the industry.
What is Symphony RetailAI and how does it leverage data science in its operations?
Symphony RetailAI is a global provider of a strategic revenue management solution suite for CPG manufacturers, powered by Artificial Intelligence.
Created from the ground up based on deep retail, grocery and CPG experience, our solution suite features the latest innovations in Machine Learning and Natural Language Processing to provide comprehensive and actionable intelligence across all channels in plain English. This enables CPGs to work smarter and faster to incrementally increase revenues and profits.
What is the state of AI for CPG technology in 2019? How much has it evolved since the time you first started here?
The evolution has not been tremendous, but it’s definitely evolved. I’m a big fan of Geoffrey Moore and the technology adoption lifecycle curve.
We’re in the relatively early stages of operationally deploying AI for CPGs and strategic revenue management, or the innovator and early adopter stages of that curve. I see the market evolving quickly to leverage this technology to allow CPGs to remain competitive. This year and into 2020, we’re going to see the evolution into the early majority market, followed by full mass market adoption in late 2020 and early 2021.
Tell us more about your vision into growing AI-driven revenue opportunities.
The vision for Symphony RetailAI is to create a suite of solutions that sit on top of our AI and Machine Learning platform and evaluate all areas of optimizing revenue, across a number of silos typical in a CPG company. We’ve initially started with solutions that optimize trade promotions and assortment. Now, we’re using AI to look at areas of leakage or settlement that are not optimized and leading to a reduction in margin and revenue. Leveraging AI with all of these solutions provides a better way to optimize revenue management from a micro market perspective as opposed to macro.
How could CPG businesses leverage Artificial Intelligence technology to strategically price their products? Which other technologies integrate with AI?
Our company gathers data from trade promotions systems, market data and syndicated data from companies such as Nielsen and IRI. We also pull in data from a company’s ERP systems, and its marketing data to gather sentiment. With this data in our system, we then harmonize it to approach revenue management from an “outside-in” versus “inside-out” perspective.
When looking “outside-in,” CPGs are putting the customer first, which is completely different from how companies are leveraging data today. The “inside-out” approach is very siloed, looking at individual quotas and trade promotions dollars for different retailers and their teams. An “outside-in” approach understands that customers have infinite choices of channels to buy their products. CPGs need to ensure that when consumers look at products, it’s their product and not the competition’s that they choose. This “outside-in” approach then allows CPGs to work under the arc of their company goals, along with a certain dollar and margin constraints.
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How do you see the raging trend of including involving AI and Machine Learning in a modern CIO or a CMO’s stack budget?
This varies from customer to customer. All companies have budget dollars for AI and Machine Learning. They just might not call it that or allocate it that way – yet. They might refer to it as consulting services, as AI will replace a lot of consulting dollars. Additionally, budget dollars today for trade promotion and trade promotion software can be leveraged for AI. Dollars for maintenance or enhancement of legacy systems can be better spent on the AI that supercharges existing trade promotion management or accounting systems.
How do you differentiate between technologies for Customer Success and Customer Support? Who are you competing with in this landscape?
Our AI solutions are designed specifically to maximize revenue opportunities.
In terms of competition, this is a new landscape with a number of different players. There are a handful of other vendors who are focused on delivering AI and machine learning solutions without a legacy of other systems. Second, there are traditional companies such as Accenture and CAS Systems, which are trade promotion management systems, and they’re trying to extend those solutions with AI components.
Third, are the trade promotion optimization companies. These companies are focused market by market, product by product, and still require a significant amount of human intervention to make the solution work. Symphony RetailAI takes a horizontal view across all products, channels, and opportunities, automating the entire process. Fourth, are the companies providing market data, like IRI or Nielsen, but they’re seen as coopetition. They’re strategic partners of ours, but also have competing solutions within their businesses, although they are not necessarily leveraging AI yet.
How should young technology professionals train themselves to work better with automation and AI-based tools?
Data science is a great opportunity for young professionals looking to get involved with automation and AI-based tools. Not only is data science required from the perspective of algorithm creation, but in order to understand and translate the application of AI algorithms to real-world solutions.
How potent is the Human-Machine Intelligence for businesses and society? Who owns Machine Learning results?
It depends. Symphony RetailAI has a proprietary metadata layer where we harmonize data from a number of different sources, and that metadata is used for Machine Learning and AI purposes. The outcome or results are actions for specific customers, as it provides them a competitive advantage.
Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?
Beyond 2020, I see the level of human involvement in smart technologies set to change. Right now, AI looks for areas of revenue optimization through trade promotions and provides a number of results that specifically say, “Here’s what you need to do to optimize.” Humans then manually review the results, making tweaks and modifications before sending changes out in the field for execution. In the future, as self-learning machines get smarter, I see confidence and trust in those machines increasing, so humans will only have to step in for exceptions, rather than the norm of processing.
The Good, Bad and Ugly about AI that you have heard or predict —
People say AI is going to replace people. Yes, it will. It will change people roles in companies. It’s not too dissimilar from the industrial revolution when machines took over what people were doing manually. The labor pool changed from manual labor to smarter, engineer roles, where people optimize, maintain and design production line systems. We’ll see a migration from people with roles in traditional BI analytics or spreadsheets to becoming business decision makers — using AI to automate a lot of the heavy lifting associated with data analysis so that they can focus on the guide-rails to accelerate revenue for their company.
The Crystal Gaze
What technologies within AI/NLP and Cloud Analytics are you interested in?
Being in the CPG and retail space, I’m interested in any and all AI, Machine Learning and cloud analytics that provides a benefit to customers. If there isn’t one, there’s no reason why one should deploy such technology. For us, trade promotion is all about having the right items in the right channel at the right time for the right people.
What’s your smartest work-related shortcut or productivity hack?
I have two. This might seem simple, but — I’m a fan of micro victories. Whenever I have large tasks or goals that the company needs to accomplish, I look for and set micro victories. In doing so, it expedites the path to that final goal and motivates and engages teams along the way.
Second, I don’t keep a task list. I put my tasks on my calendar and block out the time it takes to complete them. This forces me through execution of tasks instead of getting distracted.
Thank you, Steven! That was fun and hope to see you back on AiThority soon.
Steven Hornyak is the President of Symphony RetailAI, CPG Solutions. Steven has spent the past 30 years involved in the software, SAAS and retail technology business sectors, and has experience in managing fast growing and start-up companies.
Symphony RetailAI is the leading global provider of role-specific, AI-enabled revenue growth management solutions and customer-centric insights for retailers and CPG manufacturers across the entire value chain. Our proven, industry-leading, AI-enabled software, coupled with the industry’s only conversational natural-language AI interface, CINDE, provides key users with proven prescriptive and preemptive recommendations that make it easy to identify end-to-end growth opportunities, activate plans, and realize measurable profit and revenue growth. Our solutions are specific to key decision-maker roles focused on profitable growth across the retail value chain from source to consumer.With our strong global partner ecosystem, we serve more than 1,200 organizations worldwide – including 15 of the top 25 global grocery retailers, 25 of the top 25 global CPG manufacturers, thousands of retail brands, and hundreds of national and regional chains – all through the Microsoft Azure Cloud. Symphony RetailAI is a SymphonyAI company. More at www.symphonyretailai.com