From data to dollars: generative AI in medtech

Technology waits for no one. And in the medtech industry, companies must find ways to make the best use of technological advances and the huge volumes of data they have access to. Addressing these challenges can improve patient experiences transform sales-force effectiveness and pricing, and lead to revenue growth.

Generative AI is democratizing access to AI technologies for all organizations, creating greater agility and innovation. It offers groundbreaking ways to transform sales and pricing outcomes, ultimately delivering commercial growth. In fact, research suggests that early adopters can increase lead identification by more than 100%, convert 50% more proposals, and boost incremental revenue by up to 10%.¹

A subset of AI technologies, generative AI uses vast deep-learning models to generate seemingly original content and insights. From creating realistic images and writing human-like text to generating novel outcomes from complex patterns and simulating possible future scenarios, gen AI goes far beyond traditional rule-based systems.

The power of generative AI in sales and price execution

To improve the effectiveness of a sales force and enhance pricing outcomes, generative AI tools can connect with systems such as configure, price, quote (CPQ) technologies. CPQ technology automates how companies configure products, set the best pricing terms, and generate price quotes.

Generative AI can enhance sales-quote velocity and consistency by guiding product configuration or procedural bundles and can enforce pricing and discount guidelines using predefined business rules. The impact? Improved sales efficiency, shorter sales cycles, more accurate quotes, and enhanced customer experiences.

Let's delve deeper into the possibilities for improving sales performance and pricing.

Sharper insights on customers

Medtech sales teams have vast volumes of internal, external, historical, and real-time data on their customers and their preferences and behaviors. But capitalizing on this information has always been complex and time-consuming. A typical field sales rep can easily invest up to 600 hours a year looking for customer and product-related data to support their customer conversations. As this is time not selling, it's difficult to measure how this is time well spent.

Enter generative AI. It's a game changer.

The technology can quickly synthesize and learn from all available customer data, including internal and external sources and their preferences and behaviors. It can identify hidden patterns and generate actionable insights that enable sales teams to plan their time efficiently and offer personalized solutions. Companies can use generative AI to review customer personas, specific purchase histories and expectations, and recommend custom contractual terms and conditions. Customer relationships benefit, as do sales results.

Case study: forecasting volumes in diagnostics

A sales team in a diagnostics company invests significant time trying to forecast the patient-test throughput for specific accounts. At the same time, it's creating commercial offers that include financing for high-value capital equipment and defining the quantities of disposables and reagents customers need to run their tests. The ability to forecast volumes correctly has a major impact on the optimal price and profitability of a deal and often becomes central to commercial negotiations.

Generative AI can identify and combine all relevant sources of data to quickly predict test volumes using public health data, registries, and sales data from similar accounts. This allows the sales team to focus on selling, armed with well-founded insights that help turn negotiations into signed contracts.

Automated quote generation

The power of generative AI also improves how companies automate sales quotes. It can create tailored, persuasive, and favorable approaches and offers for individual customers and customer groups, enhancing the efficiency and effectiveness of the sales process.

Embedding generative AI into a medtech company's CPQ technology stack maximizes the return on these strategic platform investments.

Upselling and cross-selling

The next logical step is to apply generative AI to processes that lead to incremental sales. By analyzing past purchases and customer-segment behaviors, generative AI can suggest additional products or services that match a customer's needs and the price targets and parameters most likely to lead to a contract signature.

Pricing optimization

Generative AI can also help decide on the best price. The technology can analyze historical data, current market trends, and customer willingness to pay to suggest the right price point for each product. This dynamic pricing model enables medtech companies to adapt quickly to market changes. Once deployed – and as companies collect more data over time – a fine-tuned custom large language model continues to learn and adjust strategies based on the new and changing trends it identifies.

Consider how generative AI becomes the new core to the CPQ process at each step by helping teams:

• Improve performance-oriented customer pricing and quoting with consistent discount logic and effective price-approval workflows
• Enhance the ability to rapidly align field-sales activities with regular pricing strategy updates
• Simplify product pricing and maintenance with price data insights from meaningful win-loss analytics
• Create a global standard for measuring price performance with common metrics at enterprise, key account, and customer levels
• Maximize sales-force effectiveness with a closed-loop opportunity-to-bid approach

Discount management

Contract-driven discounts and incentives are one of the largest ongoing operational expense items for the medtech industry. In some cases, teams use their instinct rather than quantified analysis to set on and off-invoice discounts. This can change with generative AI, putting negotiation control back in the hands of the medtech manufacturer rather than individual sales agents and trading partners.

Generative AI could also determine when it will be crucial to offer a discount to secure a sale based on past data, predicted customer behavior, and customer lifetime value. This strategic approach to discounting can transform pricing effectiveness.

Demand forecasting

Depending on the scope and usage of medical devices, customer demand may hold steady or fluctuate significantly. This complicates a manufacturer's ability to accurately forecast and implement timely and realistic price structures.

As generative AI models evolve, they will get better at forecasting customer demand based on historical and real-time data, taking into account nuanced customer, product, and geographic characteristics such as seasonality. Companies can use these insights to adjust pricing strategies, helping medtech companies prepare for changing demand.

Beyond existing data science approaches, generative AI can also improve margins and market share. Early adopters will be well placed to unlock value through transactional data analysis, price leakage analysis (including chargebacks and deductions leakage), and discount analysis. And with additional segmentation insights and deeper customer profiles, the technology can help develop better offer configurations and quote guidelines tailored to specific segments.

The opportunities from generative AI hold immense potential for medtech companies, particularly in enhancing sales-force effectiveness and optimizing pricing strategies. But there are challenges to overcome. Companies need a robust data infrastructure, a deep understanding of AI tools, and a commitment to ethical AI practices.

Here are four ways to set generative AI up for success by establishing:

1. A culture of responsible AI: build governance, guardrails, a prototype delivery system, and change management skills – and prioritize use cases
2. Auditing capabilities: auditing mechanisms will help develop and deploy policies to protect you from risks such as copyright infringement and data leakage
3. Centers of excellence: help employees become prompt engineers, compliance checkers, and customer protection officers. Use these hubs to design, integrate and scale generative AI solutions
4. The ability to meet dynamic data demands: funnel disparate data sources, talent, and tech stacks across an enterprise to build reusable and customized assets

Finally, companies must also set up scalable architectures to support gen AI and bring in and train high-quality talent with specific business, industry, and technical expertise. With this combination of technology and skills, companies can turn their gen-AI use cases into real-world examples of productivity and profitability.

As medtech companies face increasing competition and shifting market dynamics, generative AI offers a strategic advantage. By harnessing its power, companies can enhance their sales and pricing effectiveness, driving growth and success in the ever-evolving medtech landscape.

Authors:

• Rana Saha, Service Line Leader, Sales and Commercial, Consumer and Healthcare, and Fintech, Genpact
• Christopher Columbkille Biddle, Global Life Sciences Strategy Leader, Sales and Commercial, Genpact
• Richard Fergusson, Global Life Sciences Consulting Lead, Sales and Commercial, Genpact
• Kirthi Kumar Nallapati, Global Operations Leader, Life Sciences and Healthcare, Genpact
• Naresh Jallu, Global Life Sciences Solutions Lead, Sales and Commercial, Genpact