As Artificial Intelligence (AI) waltzes through the corporate corridors, turning mundane tasks into narratives of efficiency and innovation, it brings a fresh rhythm to the age-old dance of quote-to-cash processes. The CLARITY Quote-to-Cash podcast, featuring insights from industry experts Stefan Weisenberger, the Global VP and Head of Industry Business Unit, Process Industries and Natural Resources at SAP, and Chelsea Ramage, Solution Manager for Mill Product and Mining at SAP, provides a comprehensive look into how AI is reshaping industries. Their discussion, hosted by Sergey Jermakov, Senior Partner and Monetization Architect at CLARITY, delves into efficiency, automation, sustainability, and the future of the industry.

Our guests are Stefan Weisenberger, the Global VP and Head of Industry Business Unit, Process Industries and Natural Resources at SAP, and Chelsea Ramage, Solution Manager for Mill Product and Mining at SAP and the host of the podcast – Sergey Jermakov, Senior Partner and Monetization Architect at CLARITY.

Content

Efficiency and Automation through AI in Manufacturing and Sales

The vast potential of AI to transform manufacturing and sales is made concrete through real-world impacts, bridging the gap between theoretical benefits and tangible outcomes. As AI automates the journey from quote generation to order completion, it not only streamlines operations but also opens new avenues for revenue growth. 

The power of AI-driven tools is captured through Stefan’s experiences, where he highlights AI’s role in eliminating up to 90% of manual tasks across sales and manufacturing. This efficiency is not merely about reducing manual labor but about redefining the very fabric of industry operations, from quote creation to order fulfillment. The AI innovation extends beyond automation, with classical machine learning taking center stage. Stefan shares a compelling case where a chemical company leveraged AI to boost upsell by an astounding 30%, simply by “advising smarter about what kind of product to choose.”

Furthermore, AI’s impact extends beyond the sales and operational domains. It promises to reduce turnover on the manufacturing floor by making information more accessible and understandable. AI can provide real-time customer summaries, enhancing preparation for customer interactions and ultimately leading to more effective and efficient service delivery.

Stefan and Chelsea discuss how the conversation with AI extends into customer interactions, challenging the traditional roles of customer portals and service tickets. Chelsea notes the differentiation between perceptions of traditional chatbots and more advanced language models like ChatGPT. The stark contrast in trust and utility between these technologies underscores a pivotal shift in how AI is perceived and utilized across industries.

Moreover, Stefan brings to light the nuanced complexity of AI applications in industry-specific scenarios. He reminisces about the early challenges of writing code, only to highlight how today, “with Chat GPT, we’re talking about generating extension apps on the business technology platform and you write 80% of the code with the help of AI and the developer just has to adjust that.” 

This not only showcases the advancements in AI but also illustrates the tangible productivity increases across the board, democratizing the ability to create applications and automate business processes without extensive coding knowledge.

As the discussion weaves through examples of AI’s application, from product recommendation engines to automated customer service, a clear theme emerges: AI is not just an instrument of efficiency but a conductor leading the charge towards a more innovative, automated, and efficient future. 

Transformation of Quote-to-Cash Processes with AI

The evolution of quote-to-cash processes through the lens of AI is akin to a digital renaissance, painting a future where each step of the business cycle is imbued with efficiency and strategic foresight. Sergey reflects on the transformative potential of AI, “It’s amazing how machine learning can make every part of the process better, leading to business transformation.” 

Streamlining Operations with Predictive Analytics

The operational backbone of the quote-to-cash process, from order management to fulfillment, is significantly strengthened by AI. Predictive analytics play a crucial role here, forecasting demand to optimize inventory and logistics. This foresight minimizes stockouts and excess inventory, ensuring just-in-time delivery and maximizing operational efficiency.

Moreover, AI automates routine tasks such as purchase order generation and customer requests, freeing up sales teams to focus on strategic activities. “And the typical feedback I’m getting, oh, we never knew this existed! We didn’t know that this is extremely powerful and that we can increase the productivity of the salespeople,” Sergey shares, revealing the transformative potential of AI in automating sales operations.

Real-world Impact and Customer Feedback

The tangible benefits of integrating AI into quote-to-cash processes are evidenced by improved sales department performance, reduced time-to-market, and enhanced decision-making. Businesses report unexpected gains, such as increased efficiency in product recommendations and order processing, which significantly reduce the learning curve for new sales personnel. Sergey’s reflection on the unexpected ROI from AI-driven quote-to-cash automation further illustrates the cascading benefits of technology.

“When we automate parts of the quote-to-cash process, such as making product recommendations or choosing products for a purchase order, it leads to significant savings in training time and costs. Instead of spending two years getting new employees up to speed on complex products and variants, quote-to-cash automation allows someone who’s only been with the company for a month to handle these tasks. They use AI to get the necessary information, verify it, and then complete the process by sending out the quote. This drastically shortens the time it takes to get products to market, boosts the sales department’s efficiency, and helps alleviate staffing shortages by making employees more productive sooner. We’ve seen some surprisingly positive feedback from customers on how these changes improve their operations.”

Overcoming Business Challenges with AI

Business operations, be it in strategy, sales, marketing, or day-to-day activities, face a multitude of challenges. AI emerges as a versatile tool, capable of addressing issues across these varied domains – some with a single click and others through unveiling previously unseen solutions. This capability can significantly ease the workload, especially for those who prefer a more direct approach to accessing information.

Sergey, Chelsea, and Stefan have shared insights into the multifaceted ways AI-driven tools can enhance business operations across various domains, drawing on their experiences and specific case studies to illustrate the point.

Marketing and Sales: Tailoring the Message

Businesses often fall short in customizing their marketing and sales pitches for their customers, either because they’re not collecting and analyzing industry-specific information or because their sales teams prefer to stick with a standard presentation rather than invest time in personalization. Stefan explores AI’s impact on marketing, sharing how AI can generate specific content that resonates with different industry segments, thus significantly improving engagement and sales outcomes. 

Sergey echoes this, mentioning how a basic review of the common issues within a customer’s industry can significantly improve pitch delivery. This research can be efficiently conducted using AI tools.

Pricing and Dynamic Markets

Businesses today are operating in highly volatile markets where energy prices fluctuate unpredictably, demanding real-time, dynamic pricing strategies. AI stands at the forefront of this challenge, offering solutions that can track and analyze data to adjust prices instantly. 

As Sergey notes, “Especially these days with all of the changes that are happening with the pricing, ideally we should have real-time pricing that would depend on all the different commodity prices.” This capability of AI to monitor and provide pricing recommendations in real time would allow businesses to maintain profitability even amidst market instability.

Sustainability and Value Proposition

The intersection of pricing and sustainability is becoming increasingly crucial. Companies are eager to understand how much more they can charge for greener products. Chelsea brings up how companies like Albemarle, an American specialty chemicals manufacturing company, are willing to bear higher costs for sustainable business practices, for example, sustainable supply chains, indicating a shift in corporate values towards sustainability. 

Chelsea, addressing the pricing issue, highlights the importance of being able to quickly adapt by shifting between options. She notes that if a move towards sustainability begins to impact financial stability, utilizing AI to adjust pricing strategies or parameters can be a swift solution. AI’s analytics offer deeper insights into ongoing trends, enabling more strategic adjustments.

Data Quality Challenge

Keeping data accurate is a big challenge for many businesses, especially in industries like steel and cement where they still use old-school, paper-based methods to collect information. These outdated ways are slow and often lead to mistakes in the data, which can mess up analyses and lead to wrong decisions, hurting the business’s bottom line.

Improving data quality is crucial because good data helps businesses make smart decisions and understand what’s really happening. Stefan points out how tricky it can be to tell if a piece of data is an outlier or if it’s supposed to be that way. Using AI, companies can automate the process of checking their data. This helps catch and fix errors much faster, making the data more reliable. Better data means better decisions, helping businesses stay ahead of the competition.

The interplay between AI, sustainability, and industry growth is reshaping the competitive landscape, presenting both opportunities and challenges. As companies look to AI for insights, they’re also grappling with the implications for sustainability and the growth trajectories of their businesses.

Chelsea reflects on the use of AI for competitive insights and its potential for sustainability reporting, suggesting a seamless integration of AI into areas traditionally burdened with manual processes. Stefan echoes this, speaking to the vast quantities of data processed manually, stressing the importance of automation in gathering and analyzing both internal and external data which can impact pricing strategies and product value, especially in sectors like metals and packaging.

The conversation shifts to the willingness of companies to invest more in sustainability. Chelsea shares an insight from Albemarle, revealing their choice to incur higher transportation costs for sustainable routes. This reflects a broader trend where companies prioritize green practices, even at a premium.

Stefan draws from the mining industry, describing choices like shipping iron ore with low-emission vessels as decisions that AI can assist with, ensuring that companies can opt for greener options without sacrificing efficiency. 

Stefan brings up a conversation with a chemical company, which, due to AI, has seen their once-staid business of manufacturing membranes for electrolysis surge due to increasing demand for hydrogen production – a clear indicator of how AI can signal market shifts and opportunities for growth.

Yet, as much as AI can predict and streamline, it’s not without its limitations. Chelsea and Stefan discuss the challenges of planning for unpredictable events, with Stefan, referring to a “parking lot of broken dreams” where not all AI ambitions come to fruition, especially in the face of unforeseen events, also known as “Black Swans”. 

However, there’s optimism for AI’s role in preparing for rare occurrences. Sergey shares an intriguing use case of leveraging generative AI to create training data for such rare events, combining it with traditional machine learning to prepare for scenarios without historical precedent.

Overall, the future trends in AI, sustainability, and industry growth are complex and multifaceted. While AI offers significant potential to drive these areas forward, companies must navigate the challenges of data quality, unpredictable events, and the integration of sustainable business practices. 

Overcoming Implementation Challenges

However, any transformation journey is never without its challenges. Stefan reflects on the varying pace of AI adoption among companies, “Some customers are basically front runners, and there are other companies that would love to use AI, but they simply do not have the know-how.” This observation brings to light the digital divide, where the disparity in technological maturity and resources shapes the adoption curve of AI innovations.

Chelsea, contributing to the conversation, illustrates how even companies with no traditional IT infrastructure, like Liontown, leverage AI to drive growth, “Liontown aims to be an ESG leader in the resources sector and a globally significant provider of battery minerals for the rapidly growing clean energy market. They don’t have any IT and they don’t want any IT and they still can implement AI and machine learning.” Her example serves as a beacon for startups and midsize companies, demonstrating that the core offerings embedded within platforms like SAP can democratize access to AI, enabling businesses to scale and innovate irrespective of their size or IT capabilities.

Addressing the practicality of AI in driving quote-to-cash efficiency, Sergey shares his insights. “When talking with our customers, we say that they don’t have to automate everything; they can start with one document or one process and then scale it further if it proves effective,” Sergey says. This pragmatic approach to AI integration, focusing on incremental improvements and tangible results, resonates with the audience, offering a blueprint for navigating the complexities of digital transformation.

Practical Strategies for AI Implementation

For businesses eager to leverage AI, the experts share valuable strategies for successful implementation:

  • Start Small: Begin with manageable projects using pre-built models and existing use cases to quickly generate value and build trust within the organization.
  • Focus on Low-risk Scenarios: Opt for applications in areas with lower risk to avoid complex challenges initially, such as document extraction and quote assessment.
  • Leverage Existing Successes: Apply proven AI applications across the organization to maximize impact without reinventing the wheel.
  • Emphasize Trust and Ease: Choose applications that are easy to trust and implement, such as conversational interfaces for analytics, to lower entry barriers and encourage adoption.

The conversation also touches on the difficulty of transitioning machine learning prototypes into productive applications. This gap underscores the need for solution providers to deliver out-of-the-box AI functionalities that are easy to implement and use, addressing the industry-wide challenge of resource scarcity, particularly in attracting talent with AI expertise.

The Bottom Line

The integration of AI into the quote-to-cash process marks a significant business transformation towards operations that are not only more efficient but also sustainable and profitable. However, a notable barrier to this technological adoption is often simply a lack of awareness. Many businesses remain unaware of the automated solutions available and the considerable value they can add by streamlining operations.

As Stefan Weisenberger, Chelsea Ramage, and Sergey Jermakov have demonstrated through their insights, staying informed and open to new technologies is crucial. AI’s capacity to solve complex challenges and enhance business operations is just the beginning. It paves the way for enduring success and the birth of innovative ideas, steering companies towards a future where sustainability and efficiency are at the forefront of their operational strategies.

The CLARITY Quote-to-Cash podcast talks about how AI can really change the game for businesses, making processes smoother and helping companies be more sustainable. Getting AI into the mix is all about staying curious, learning new things, and asking experts for advice to make the most of what AI can offer.

Listen the full episode