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May 07.2025
3 Minutes Read

How Quicklizard's Acquisition by Riverwood Capital Is Shaping AI-Driven Pricing Strategy

Futuristic depiction of AI-driven pricing optimization.

Quicklizard's Acquisition: A Game Changer in Dynamic Pricing

In a significant move reshaping the landscape of business operations, Quicklizard has officially closed its acquisition by Riverwood Capital. This partnership, announced on May 7, 2025, positions Quicklizard not only as a leader in AI-driven pricing optimization but also as a crucial ally for companies navigating the complexities of today's economic turmoil.

The Adaptive Edge of AI in Pricing Strategies

Today’s businesses face a myriad of challenges from fluctuating tariffs to disruptions in the supply chain and changing consumer behaviors. Traditional pricing models, often static and slow to respond, are becoming obsolete. Quicklizard shines in this context with its advanced AI-powered platform that transforms pricing from a mere reactive task into a proactive strategy aimed at growth.

As Quicklizard’s CEO Pini Mandel states, "Today’s environment demands precision, adaptability, and speed." The platform is built to understand market dynamics in real-time, enabling businesses to not only react to changes but also anticipate and shape them.

Success Stories: How Quicklizard Drives Results

Trusted by global brands such as Sephora and Samsung, Quicklizard’s technology has demonstrated impressive outcomes. On average, clients report an 8% increase in revenue and a 3%-5% uplift in profits—tangible results that many companies are eager to achieve. By identifying competitors, avoiding price wars, and forecasting demand accurately, Quicklizard provides a competitive edge that is invaluable in the current economic landscape.

Riverwood Capital’s Vision for the Future

With Riverwood’s investment, Quicklizard is set to embark on an exciting next phase of growth. The focus will be sharpening its Generative AI capabilities and expanding its global footprint, particularly in the U.S. market. Ramesh Venugopal from Riverwood highlights the importance of Quicklizard’s platform as a vital tool for retailers to maintain margins even in challenging times. This signifies not just an acquisition, but a commitment to redefine retail strategies using advanced technology.

The Broader Impact: Supporting Israeli Innovation

This acquisition also underscores Riverwood Capital’s commitment to the thriving Israeli technology ecosystem. Jeff Parks, co-founder of Riverwood, emphasizes that Quicklizard's growth story is a testament to the strength of Israeli innovation in the tech sector. As Quicklizard brings its dynamic pricing solutions to more brands worldwide, it continues to enhance customer experiences and profitability across the board.

Future Trends in AI-Powered Retail Solutions

The evolving landscape of AI in retail isn’t limited to pricing. It reflects a broader trend encompassing chatbots, virtual assistants, and robotics. These innovations play a critical role in how brands streamline operations and enhance customer engagement. As Quicklizard develops its offering, we can expect significant advancements in machine learning and natural language processing (NLP) that will further empower retailers in making data-driven decisions.

What This Means for Businesses Going Forward

As businesses look to adapt to rapid market changes, the integration of AI solutions like Quicklizard's has never been more vital. The implications are profound, suggesting that the competitive landscape will require businesses to not only adopt new technologies but to innovate continuously. Quicklizard’s strategic direction, fueled by Riverwood's investment, exemplifies how technology can power growth and resilience in uncertain markets.

Conclusion: A Call to Innovate

The era of traditional pricing strategies is quickly fading as companies navigate a world defined by volatility and rapid change. Quicklizard stands at the forefront of this transformation, urging businesses to embrace innovative approaches to pricing and strategy. As we monitor the impact of this acquisition, companies must consider how they can harness emerging technologies to stay ahead in this dynamic environment.

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12.08.2025

As AI in Production Surges, Will Data Quality Keep Pace?

Update AI Deployment on the Rise Despite Data Quality Challenges Across the globe, enterprises are increasingly adopting artificial intelligence (AI), with a recent survey finding that 64.5% of organizations now have AI in production. However, the same survey by Apryse reveals a glaring issue: only 38.1% of these enterprises rate their document data as 'excellent' for AI use. This presents a critical gap in AI readiness that is alarming for business leaders keen to harness the disruptive potential of AI technologies. The Paradox of Progress: AI Adoption vs. Data Quality The findings suggest that while AI has cemented its position in the operational landscape of businesses, the infrastructure supporting it—particularly in terms of document data quality—has not kept pace. Traditional data trapped within documents is often messy and inconsistent, posing a significant barrier to effective AI implementation. As Andrew Varley, CPO of Apryse, points out, “AI is no longer experimental, it’s operational,” but many organizations find the document data governance inadequate for leveraging this operational AI effectively. The Financial Impact of Poor Data Quality Many enterprises are now facing significant challenges with data quality, as revealed by a complementary survey from Qlik. It shows that 81% of AI professionals report persistent data quality issues, which could jeopardize the ROI of their AI investments. Poor quality data can lead to biased models and unreliable insights, ultimately compromising the stability of businesses. Financially, organizations need to place increased focus on data quality or risk significant waste and liability. The Role of Document Automation in AI Success The Apryse survey highlights a vital trend: 82.8% of organizations plan to invest in document automation in the next twelve months. Document automation not only streamlines processes but also enhances data quality for AI readiness. Tools that enable the extraction of structured data from unstructured documents—such as improved table recognition and metadata tagging—will be crucial in bridging the gap. By investing in these tools, organizations can unlock the full potential of their AI deployments. Asia-Pacific: A Different Story in AI Maturity While North America remains at the forefront of AI deployment, organizations in Australia and New Zealand are showcasing a surprising leadership in AI infrastructure maturity. These regions are early adopters of data residency rules and have shown a commitment to robust document processing, making them models for other markets. The unique regulatory environment in Oceania—especially in healthcare and financial services—has pushed for solid document-to-data workflows, which may serve as a case study for global enterprises. Key Strategies for Improving Data Quality To overcome the data quality challenges, enterprises must develop comprehensive data management strategies. This includes investing in data governance frameworks, ensuring that all stakeholders understand their roles in maintaining data quality, and implementing continuous data validation processes. Furthermore, utilizing smart automation technologies can help to clean and improve data quality, ensuring reliable foundations for AI models. By adopting these practices, organizations can mitigate risks associated with flawed data and enhance the performance of their AI initiatives. Why Action on Data Quality Matters Now Companies increasingly recognize the urgency of addressing data quality concerns. A fifth of data professionals believe that without prioritizing data quality, businesses face a crisis that could severely outweigh current investments in AI technology. Organizations must act now not just to safeguard investments but to ensure that AI delivers on its promise of operational efficiency and enhanced customer experiences. As industry leaders explore innovative solutions through AI, a commitment to high-quality data is essential for sustainable success. In summary, while the surge in AI adoption is promising, it is accompanied by serious challenges related to data quality and governance. Firms that proactively invest in document automation and enhance data quality practices will stand to gain significant competitive advantages in the evolving AI landscape.

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Maximizing Value in Hybrid Cloud: The Role of VMware Cloud Foundation

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