Presented by Provectus

Companies now no longer have a choice but to prioritize AI transformation. In a time where the Amazons of the world have set the customer service bar incredibly high, consumers have developed high expectations for personalized user experiences and smart features. Simply put, AI is the key to staying competitive.

“But AI is not a silver bullet — it’s just an instrument, like the cloud or like the web,” says Stepan Pushkarev, co-founder and CTO/CEO at Provectus, Inc. “And it will become just another expensive technology unless you start with a mission, map it to a real business need, and then break down your overarching strategy into specific use cases, with specific ROI.”

But while company leaders are eager to leverage AI for its well-documented benefits, the leadership team can often struggle with where to start. This can stem from the misleading hype around how the technology works, the result of leaping ahead based on a gut feeling instead of data, or out-of-date perceptions around AI as a sci-fi technology.

Leaders can end up prioritizing the wrong projects or with skewed use case objectives. For instance, a social media campaign that surfaces viral content can improve engagement metrics, but in the long term, harm the customer experience. Or a call center customer-support strategy focused only on cost reduction might improve the bottom line in the short run, but over time, customer experience will take a hit.

An AI strategy is most successful, and most transformative, when it is backed by strong, top-down vision and objectives for AI transformation, real business knowledge, and guided by a company’s mission.

Where to start

Depending on the type of enterprise, AI use cases come from three main sources in the organization: 

Top management, usually a result of strategy sessions and executive offsites, and driven by the most broad understanding of the company opportunities and market drivers. For instance, a set of customer-360 use cases that support the company’s mission to become a customer-centric company.

Business units, usually driven by specific business processes and KPIs. For example, introducing AI-powered automation in the call center in order to improve metrics like time-to-resolution and customer satisfaction scores.

Bottom-up ideas, which come from innovative leaders at all levels. This is the most powerful and creative source of the use cases.

However, ideas should be encouraged from all levels of the company — every engineer and analyst should have the tools and the access to generate ideas and push them to management to be prioritized for execution.

“The goal is to commoditize AI within the enterprise and make it available and accessible for every single department, every single team member to digitize, optimize, and improve their business processes,” Pushkarev explains. “But in the early stages of AI transformation, an organization needs to define a strong top-down vision and objectives for AI, and the process needs to be kickstarted with a top-down push, from the C-suite or the executive leadership across functional leadership teams.”

The AI use cases to prioritize first

Where should the C-suite’s vision start? Here are some of a company’s most urgent calls to action.

Customer-centric use cases.  Over and over, the pandemic has highlighted the need for companies to go from product- or brand-centric to customer-centric, and many enterprises are trending that way, selling directly to consumers and realizing they need to find ways to manage the customer journey end-to-end. How a company treats its customers has become a competitive advantage — and AI can give companies a leg up in that arena.

“Customer-centric companies win,” Pushkarev says simply. “AI use cases should be evaluated on their own, based on individual business circumstances, but we’ve seen that it’s helpful to prioritize customer-focused use cases. And if you start framing your problems from the customer’s perspective, you’ll start to align a cascade of use cases.”

Customer-360 or customer-centric AI embraces all possible use cases and point solutions for providing better and personalized customer experience with the brand directly or indirectly, across all channels and touchpoints.

That includes areas like personalized product recommendations in ecommerce operations and optimizing supply chain and last-mile delivery logistics. Pharmaceutical companies accelerate the drug discovery process with deep learning models and, at the of the day, deliver new and personalized drugs to patients faster and more reliably by going directly to consumers. Airlines reimagine customer experience by personalizing the user journey from booking to post-booking, customer service, airport, and on-the-plane services. The same goes for banking, retail, CPG, automotive, and even manufacturing industries. 

Employee or workforce-centric use cases. Post-pandemic, the number of jobs is skyrocketing but there’s a shortage of workers. This gap is exacerbating supply chain disruptions, and hampering economic growth, with leading industries struggling to regain momentum. AI is a key to solving those challenges. Automation helps employees work smarter, and take on more interesting tasks while algorithms eliminate the repetitive, boring aspects of their work. In the science world, computer vision and machine learning are helping doctors and researchers scale their diagnostic work to improve results. And in the manufacturing industry, AI is key to dramatically improve employee safety.

Operational excellence use cases. While ambitious ideas for new business, customer- and workforce-centric use cases may have a high return on investment, they can also require a long-term vision and transformation of the entire company. However, operational use cases have the highest probability for success and the most clear path to ROI. 

There are endless areas in which AI can optimize operations, including customer support automation, which ensures that customers get fast, reliable service from plugged-in support employees. Or inventory optimization, which can give you real-time global visibility across all your inventory, which, in turn, improves service levels and maximizes your on-time in-full performance. Intelligent document processing lets you capture, extract, and process data from a variety of document formats, helping automate manual processes and slash costs, unlock key insights, and more.

How to begin ideating

When you sit down and talk about real, specific use cases, it’s important to have the right team in place: the executive sponsors, product managers, AI solution architect and the subject matter experts.

From the start, you need to take into consideration a number of things. Is the data available, and if it’s accessible, is it structured or unstructured? You need to assess if it’s a commoditized AI use case, and what kind of tolerances your use case has or not — in other words, how accurate does your model need to be in order to make an impact?

It’s also crucial to assess the length of the timeline of adoption for different use cases. With AI and machine learning there is an adoption curve as data comes in, and each iteration of a machine learning model is trained, and the timeline and corresponding business expectations need to be set in these early prioritization sessions.

How leaders can launch the AI transformation journey

To launch an AI strategy, above all, you need to be hands-on. But that doesn’t mean going and getting a PhD in computer science.

“First of all, develop an intuition about how AI works, how exactly it could be applicable to your business, and be able to execute and operate,” Pushkarev explains. “You need to know exactly how each piece of the business works, how it’s going to be improved by AI, and be hands-on in prioritizing use cases with the help of your subject-matter experts.”

You also have to be hands-on in managing expectations of any potential use cases — not just your team’s, but your own. And you need to work to ensure that the project never loses its primary objective: to support real business users.

And finally, you need to be hands-on in building a cross-functional team and ensuring executive sponsorship. Domain experts, AI solutions architects, and hands-on business people will drive your projects from the top down, and ensure that your key objectives are achieved, as well as uncover new possibilities along the way — kicking off a full-scale AI transformation journey.

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