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Author Archives: Micaela Mcpadden

  1. From Boomers to Zoomers: Closing the Generational Gap in IT Leadership

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    The workplace is evolving, and IT leadership is no exception. With multiple generations working together, each bringing unique perspectives and priorities, how do we navigate these differences? According to Info-Tech’s IT Talent Trends 2025, generational divides, particularly around technology, are reshaping how IT teams operate.

    A recent survey highlights this gap, revealing that 75% of Gen Z employees have used generative AI tools, compared to only 39% of Baby Boomers. Gen Z, often described as digital natives, sees AI as a powerful tool for innovation. They’re optimistic about its potential to streamline workflows and transform how we work. Meanwhile, senior IT leaders, who have spent years ensuring operational stability, are more cautious. They know that innovation without trust in the system can lead to chaos.

    So, how do we bridge this gap? How can we create a workplace where these perspectives complement, rather than clash?

    Understanding Generational Perspectives

    Think about this: Gen Z employees grew up in a world of smartphones, instant answers, and rapid technological advances. Is it any wonder they’re excited about AI? To them, it’s not just a tool, it’s a way of thinking. They want technology that’s fast, flexible, and ready to take on repetitive tasks so they can focus on creative problem-solving.

    Now, consider senior IT leaders. Their experience has taught them the importance of reliability, security, and long-term planning when it comes to integrating new solutions. They’re not against AI, but they understand that rushing into adoption without proper guardrails can lead to unintended consequences.

    Both perspectives are valuable. But unless we find ways to connect the dots, there’s potential for friction.

    Building Bridges Across Generations What can CIOs do to foster collaboration across age groups? Here are a few ideas to spark thought and conversation:

    1. Encourage Cross-Generational Teams

    What happens when you put a Gen Z technologist on the same project as a seasoned IT veteran? Magic, if it’s done right. According to a Harvard Business Review report, companies that embrace multigenerational teams are more likely to outperform competitors. Diverse age groups bring complementary strengths—Gen Z’s digital fluency paired with veterans’ institutional knowledge fosters innovative, well-rounded solutions. Ensure success by providing clear communication channels and defining shared goals to harness the full potential of such teams.

    2. Embrace Reverse Mentoring

    Have you ever thought about flipping the script on mentorship? Younger employees who are fluent in AI and emerging technologies can teach senior colleagues, while learning from their leadership skills and institutional knowledge. HBR highlights that reverse mentoring programs improve knowledge sharing and foster empathy across age groups. This exchange helps reduce generational bias and builds a culture of mutual respect, making every employee feel valued.

    3. Invest in Lifelong Learning

    No one should feel left behind. HBR emphasized the importance of investing in learning and development tailored to diverse needs. Offer training that caters to all experience levels, from foundational IT principles to cutting-edge innovations like AI and cloud computing. Wouldn’t it be great if every team member felt confident navigating the latest tools? Learning opportunities that bridge generational gaps contribute to better collaboration and job satisfaction.

    4. Create Space for Open Dialogue

    Do employees feel heard? Regular forums where team members can share their views on technology can help break down barriers. The HBR report emphasizes the importance of psychological safety in multigenerational teams, noting that employees are more likely to contribute ideas when they trust their voices will be respected. Listening to concerns—whether about security or innovation—can pave the way for balanced decisions, fostering a workplace culture where everyone is empowered to thrive.

    What if we could turn generational differences into a competitive advantage? By embracing diverse perspectives, CIOs can build teams that are not only collaborative but also future-ready.

    The goal isn’t to choose between innovation and assurance in a system, it’s to find the right balance. When we bring generations together, we unlock a wealth of potential: fresh ideas, time-tested strategies, and a shared vision for success.

    How is your organization addressing generational divides? Are you creating opportunities for connection and collaboration? Let’s start a conversation about how CIOs and IT leadership can evolve to meet the needs of a multi-generational workforce.

    Together, we can bridge the gap, and build a stronger, more innovative future.

  2. AI-Ready Data: How Enterprises Must Prepare Proprietary Data to Power Agentic AI Systems

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    As enterprises integrate artificial intelligence into their business operations, the focus is shifting from automating routine tasks to enabling agentic AI—AI systems that can act autonomously, make decisions, and drive business outcomes. However, achieving this transformation depends on a critical but often overlooked resource: AI-ready data. Proprietary data isn’t just a supporting element in the AI ecosystem—it’s a strategic asset that determines how effectively agentic AI systems can operate.

    Why Data Quality is the Foundation of Agentic AI

    Unlike traditional AI applications, which may operate on general datasets, agentic AI thrives on enterprise-specific, contextual data. According to the recent CIO article What to Expect from AI in the Enterprise in 2025, foundational models (FMs) trained on broad, public datasets are excellent for general-purpose tasks but often fall short when applied to enterprise-specific workflows. “To benefit from this wider range of [retrieval-augmented generation] services, organizations need to ensure their data is AI-ready,” the article notes.

    AI-ready data requires robust information management practices such as:

    • Data Cleaning & Validation: Ensuring that enterprise data is accurate, relevant, and free from duplicates.
    • Data Structuring & Enrichment: Organizing data into formats that AI systems can easily understand and enriching datasets with contextual metadata.
    • Data Ownership & Compliance: Clearly defining who owns the data and ensuring compliance with privacy and governance standards.

    Not sure where to start? Check out this comprehensive guideline for creating AI-friendly documents. 

    From Data Silos to Competitive Advantage

    Enterprises that treat their proprietary data as a competitive differentiator will be well-positioned in the coming years. As AI in the Enterprise in 2025 explains, “The sooner enterprises identify data assets from across the business, adopt a creative approach to how they might be used, and get them in an AI-ready state, the sooner they’ll be able to take advantage of new RAG services coming down the line in 2025.”

    This preparation enables businesses to generate deeper insights, unlock previously untapped opportunities, and create a sustainable competitive advantage through AI-powered innovations.

    Data Strategy: The CIO’s Next Business Imperative

    While CIOs have long recognized the value of data, many still struggle with aligning data strategies to tangible business outcomes. The CIO article 5 Tips for Better Business Value from Gen AI highlights how forward-thinking enterprises are linking data quality initiatives directly to revenue-generating outcomes:

    • Sales Enablement: AI-powered CRM tools that provide predictive insights by analyzing customer interaction data.
    • Marketing Personalization: Gen AI-driven marketing platforms that generate tailored content based on enriched customer data.
    • Service Optimization: AI-driven service teams that resolve customer issues faster by leveraging structured service records.

    “Improving data quality and integrating new data sources…are vital for applying AI in marketing and sales,” said Jacqueline Woods, CMO of Teradata, in the article. She emphasized how combining structured and unstructured data can unlock new opportunities for customer engagement and retention.

    Looking Ahead: AI-Ready Data as a Long-Term Asset

    Preparing proprietary data isn’t a one-time project—it’s an ongoing strategic investment. Forrester predicts that AI governance software spending will quadruple by 2030, reaching nearly $16 billion. This surge underscores the growing recognition that high-quality enterprise data is essential for building advanced AI capabilities.

    As businesses face intensifying competition and rising customer expectations, agentic AI fueled by proprietary data will be the defining factor between market leaders and laggards. The enterprises that invest today in making their data AI-ready will unlock capabilities that extend far beyond automation—enabling AI systems that drive innovation, enhance decision-making, and transform entire industries.

    Is your enterprise ready to harness the full potential of its proprietary data?

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  3. The Digital Vanguard: How CIOs Can Lead in 2025

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    Only 48% of digital initiatives hit their goals, yet organizations led by the “digital vanguard”—a select group of CIOs and CxOs—achieve a remarkable 71% success rate, according to The Gartner CIO Survey. These leaders don’t just adapt; they innovate, setting the standard for success in the digital age.

    This is the moment for CIOs across industries to lead. By embracing collaboration, skill development, and shared accountability, you can move from being a technology operator to an enterprise orchestrator, transforming your organization and ensuring its future success.

    Here’s how to rise above the rest and become a digital vanguard leader.

    From Operator to Orchestrator: The CIO’s Evolution

    The role of the CIO has evolved beyond managing IT operations and infrastructure. Today’s CIO must stand at the intersection of business and technology, driving enterprise-wide transformation. The digital vanguard offers a clear blueprint for success:

    • End-to-End Co-Ownership: Share accountability for digital solutions with business leaders. For instance, in sectors like manufacturing or service, this could mean co-developing tools that enhance supply chains or customer support.
    • Enhanced Collaboration: The most successful CIOs meet with their CxO peers four times more often than others. Regular, cross-functional dialogue builds alignment and accelerates innovation.
    • Embedded Technology Skills: Companies where 35% of business staff engage in tech initiatives outperform those where only 21% do. CIOs must empower operational leaders to become digital innovators through targeted training programs.

    Unlocking the Edge Across Industries

    CIOs bring unique expertise to their industries, positioning them to lead the charge in digital innovation.

    In Manufacturing:
    Manufacturing CIOs can leverage their knowledge of production systems and supply chains to drive significant improvements:

    • Leverage Digital Twins and IoT: Combine operational technology (OT) and IT for real-time insights that boost efficiency and accountability.
    • Champion Cross-Functional Training: Upskill teams to bridge IT and operations, enhancing collaboration.
    • Drive Co-Creation: Partner with business leaders to implement solutions like predictive maintenance, reducing downtime and optimizing processes.

    In Service:
    For service industries, agility and customer experience are paramount:

    • Create Scalable Platforms: Roll out user-friendly tools that empower employees to solve customer issues efficiently and independently.
    • Foster Collaborative Decision-Making: Use agile frameworks to align IT, marketing, and operations.
    • Empower Digital Champions: Train leaders to act as digital product managers, driving innovation directly from the frontlines.

    Skill Development: The Cornerstone of Success

    Skill-building is critical to creating a future-ready workforce that aligns IT with broader business goals. One strong example comes from CF Industries, a global manufacturer of fertilizers, where business leaders were trained as digital product managers. This alignment between IT and operations led to measurable success and can be replicated across industries.

    CIOs can drive similar initiatives by:

    • Building Growth Pathways: Create programs to deepen tech expertise among operational leaders.
    • Embedding Digital Literacy: Provide training platforms to ensure teams contribute effectively to digital goals.
    • Celebrating Wins: Highlight successful projects to inspire momentum and a culture of innovation

    The Future of CIO Leadership

    As we approach 2025, the stakes for CIOs have never been higher. By fostering collaboration, enabling skill-building, and co-owning outcomes, you can secure a place in the digital vanguard, driving transformation and innovation for your organization.

    The time to act is now. Will you define the future of your industry—or be left behind?

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  4. Aquant’s Service Co-Pilot Now Available in the Microsoft Azure Marketplace

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    Microsoft Azure customers worldwide now gain access to Aquant’s Service Co-Pilot to take advantage of the scalability, reliability, and agility of Azure to drive application development and shape business strategies.

    Aquant today announced the availability of its Service Co-Pilot in the Microsoft Azure Marketplace, an online store providing applications and services for use on Azure. Aquant customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management.

    Aquant offers an all-encompassing AI-powered solution designed to elevate service teams across sectors such as manufacturing, medical devices, industrial machinery, among others. Aquant’s platform empowers service leaders, field technicians, and contact center agents to bridge the service expertise gap through expert-level, scenario-specific recommendations. Leveraging deep analysis of a company’s service data and expert insights, Service Co-Pilot delivers precise guidance that factors in user skill level, problem complexity, and equipment status for optimal problem-solving.

    With Service Co-Pilot, service teams benefit from a continuously evolving AI model that learns from real-world data and feedback, improving troubleshooting accuracy and enabling proactive maintenance. Our solutions foster effective training and drive growth opportunities, positioning service as a strategic asset that contributes directly to business success.

    “AI is evolving fast — from copilots that assist to agents that act — but in manufacturing, the key to success is personalization,” said Assaf Melochna, Aquant’s president and co-founder. “Generic AI doesn’t cut it when you’re troubleshooting complex machinery. That’s why Aquant is on a mission to deliver personalized AI that understands the unique challenges of service teams. We build AI that thinks like your best experts, providing precise recommendations that help technicians solve problems faster, with less guesswork. We’re excited to extend Service Co-Pilot’s capabilities to Azure users and look forward to the impact it will have on their service operations.”

    “Microsoft Azure Marketplace welcomes Aquant, which joins a cloud marketplace landscape predicted to grow revenue 500% from 2022 to 2025,” said Jake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp. “Thanks to Azure Marketplace and partners like Aquant, customers can do more with less by increasing efficiency, buying confidently, and spending smarter.”

    The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. The Azure Marketplace helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use.

    Learn more about Aquant at its page in the Azure Marketplace.

  5. Why We Built the F.A.S.T AI Decision-Making Template—and How to Use It

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    When it comes to AI, there’s no shortage of hype. Everyone says AI will transform businesses, change how we work, and redefine industries. But anyone in the trenches knows AI isn’t some magic wand. It’s hard work. There are dozens of decisions to make, from choosing the right projects to getting buy-in from teams and ensuring the results are valuable.

    The problem isn’t a lack of options; there are too many. You can’t pursue every interesting AI idea that comes up—resources are limited, and teams can only handle so much. You need a way to cut through the noise and focus on the initiatives that will move the needle.

    That’s why we created the F.A.S.T AI Decision-Making Template. It’s a simple tool to help leaders zero in on what matters. The template organizes decisions around four key areas—Focus, Alignment, Scalability, and Tangible Results—each designed to filter out unnecessary complexity and steer you toward high-impact choices. Here’s a breakdown of each component, followed by the template itself.

    The Components of the F.A.S.T AI Template

    Focus: Prioritizing the Right Projects

    The first step is Focus. AI opens up new possibilities, but just because something is possible doesn’t mean it’s worth doing. Focus is about ensuring every project directly connects to your business’s core goals. If it’s not a high-impact priority, it’s a distraction.

    Alignment: Getting Everyone Moving in the Same Direction

    Then comes Alignment. AI projects rarely work in isolation—they require input from different departments and buy-in across teams. Without alignment, projects get bogged down in miscommunication and competing priorities. Alignment is about ensuring everyone understands the “why” behind a project and that each team is rowing in the same direction.

    Scalability: Building for Long-Term Success

    AI projects need to scale to deliver real value. Many projects start strong in pilot stages but fail to expand without a lot of rework. Scalability is a reminder to build AI solutions that can grow with your business’s needs, so you don’t end up with something you outgrow in a year.

    Tangible Results & Feedback Loop: Staying Grounded in Measurable Outcomes

    Finally, there’s Tangible Results. It’s to get lost in theory with AI—focusing on what could happen instead of what will happen. Tangible Results forces you to get specific about outcomes. It’s about setting clear metrics that prove the project’s impact, so you’re not left guessing whether it’s worth the effort.

    The F.A.S.T AI Decision-Making Template

    (See link below for access to template)

    The template organizes these four components into actionable questions and criteria. Use it as a checklist to evaluate each AI initiative, so you know exactly where to focus, who to align, how to scale, and what results to expect.

    Why This Template Works

    The F.A.S.T AI Decision-Making Template isn’t some magic bullet. It won’t make AI projects easy, but will make them easier to manage. The reason is simple: it forces you to get clear about the purpose of each project. Instead of jumping into AI initiatives because they sound interesting, you have a framework that asks, “Is this worth doing?”

    Many decision-making frameworks get bogged down in theory. F.A.S.T is meant to be the opposite—practical and to the point. It’s a way to cut through the endless possibilities of AI and get back to basics: which projects are worth your time, who needs to be involved, can it scale, and will it deliver results?

    This template is like a compass in a world where AI will only get more complex. It won’t tell you every step to take, but it’ll keep you pointed in the right direction.

    Final Thoughts

    The F.A.S.T AI Framework and this template are about making AI decisions that count. With Focus, Alignment, Scalability, and Tangible Results as your guide, you’ll avoid getting lost in the weeds. And in the world of AI, where complexity can easily overshadow impact, that’s exactly what leaders need.

    The F.A.S.T AI decision-making template can be found here.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  6. Aquant Named to Fast Company’s Fourth Annual List of the Next Big Things in Tech

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    List Recognizes Groundbreaking Advancements Redefining the Way We Work and Live

    Aquant today announced that it has been named to Fast Company’s fourth annual Next Big Things in Tech list, honoring emerging technology that has a profound impact for industries—from education and sustainability to robotics and artificial intelligence.

    This year, 138 technologies developed by established companies, startups, or research teams are featured for their potential to revolutionize the lives of consumers, businesses, and society overall. While not all technologies are available in the market yet, each is reaching key milestones to have a proven impact in the next five years.

    Aquant offers an all-encompassing AI-powered solution designed to elevate service teams across sectors such as manufacturing, medical devices, industrial machinery, among others. Aquant’s Service Co-Pilot empowers service leaders, field technicians, and contact center agents to bridge the Service Expertise Gap™ through expert-level, scenario-specific recommendations. Leveraging deep analysis of a company’s service data and expert insights, Service Co-Pilot delivers precise guidance that factors in user skill level, problem complexity, and equipment status for optimal problem-solving.

    With Service Co-Pilot, service teams benefit from a continuously evolving AI model that learns from real-world data and feedback, improving troubleshooting accuracy and enabling proactive maintenance. Aquant’s solutions foster effective training and drive growth opportunities, positioning service as a strategic asset that contributes directly to business success.

    “This recognition by Fast Company is a testament to our team’s commitment to staying ahead in a crowded AI market,” said Assaf Melochna, President and Co-founder of Aquant. “While companies like Salesforce and Microsoft offer one-size-fits-all solutions, our technology learns directly from your top experts, going beyond standard documentation to capture and analyze unique insights from their notes and real-world experiences. This enables Service Copilot to deliver personalized recommendations tailored to the specific challenges of a company’s machinery, reducing guesswork and enhancing service operations.”

     “The Next Big Things in Tech provides a fascinating glimpse at near- and long-term technological breakthroughs across a variety of sectors,” says Brendan Vaughan, editor-in-chief of Fast Company. “Spanning everything from semiconductors to agricultural gene editing, the companies featured in this year’s list are tackling some of the world’s most pressing and vexing problems.”

    Click here to see the final list.

  7. The Next-Gen CIO: Embracing AI to Enhance Competitive Edge

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    Companies investing heavily in AI are seeing significant financial benefits. According to new research from Accenture, 74% of organizations have reported that their investments in generative AI and automation have met or exceeded expectations, and 63% plan to further strengthen these capabilities by 2026. The new report, Reinventing Enterprise Operations with Gen AI, also reveals that the number of companies with fully modernized, AI-led processes has nearly doubled from 9% in 2023 to 16% in 2024. These “reinvention-ready” companies achieve 2.5 times higher revenue growth, 2.4 times greater productivity, and 3.3 times greater success at scaling generative AI use cases compared to their peers.

    In this new AI-powered era, the role of the CIO is evolving from managing IT infrastructure to strategically leading AI initiatives that can drive growth, distinction, and innovation. Today’s CIOs are better described as Chief Competitive Advantage Officers, focusing on how AI can not only solve existing problems but also unlock new business models, streamline operations, and personalize customer experiences at scale.

    But with this evolved role comes a critical challenge: how can CIOs implement AI initiatives that deliver fast, scalable wins?

    AI: A Strategic Imperative for Competitive Advantage

    While we’re seeing more companies than ever experimenting and implementing AI programs, a decent amount are struggling to attain real value at scale. Only 26% have developed the necessary capabilities to move beyond proofs of concept and generate tangible value, according to new research from Boston Consulting Group (BCG). Even more telling, just 4% of companies have achieved cutting-edge AI capabilities across functions and consistently generate significant value.

    Accenture’s 2024 report, Reinventing Enterprise Operations with Gen AI, further reinforces this gap in AI maturity. It found that nearly two-thirds (64%) of organizations are still struggling to change the way they operate. A major barrier is their data infrastructure—61% report that their data assets are not yet ready for generative AI, and 70% are finding it difficult to scale projects using proprietary data. The inability to build a strong data foundation and effectively scale AI initiatives prevents these companies from realizing the full potential of their investments.

    I’ve highlighted below the characteristics that set leading CIOs – who make AI a key priority – apart:

    1. Embrace a Talent-First Reinvention Strategy

    One of the most crucial steps leading CIOs take is focusing on people and processes over purely technological capabilities. Both studies emphasize the importance of this approach. Accenture’s research highlights that 82% of companies in the early stages of AI maturity have not yet applied a talent reinvention strategy, putting them at a disadvantage. To address this, leading CIOs invest heavily in training and developing AI-specific skills within their workforce to keep up with rapid advancements in generative AI. Similarly, the BCG study reveals that successful companies allocate 70% of their AI resources to people and processes, ensuring that employees are equipped and workflows are optimized to integrate AI initiatives effectively.

    2. Focus on Core Business Processes for AI Integration

    According to BCG, leading companies derive 62% of their AI value from core business functions. This strategic focus allows them to leverage AI where it can drive the most impactful outcomes. CIOs who prioritize AI-driven transformations in these critical areas create substantial competitive advantages and outperform their peers. This primary focus on core functions, with a secondary focus on support functions, is a hallmark of successful AI integration. Of course, every business is different, so it’s important to understand what the core functions and supporting functions of your business are.

    3. Adopt a Robust Data Foundation and Scalable AI Strategy

    Both reports point to the importance of establishing a solid data infrastructure and focusing on scalable AI initiatives. Accenture’s research found that 61% of companies struggle to build the necessary data foundation for generative AI, which hinders their ability to scale these technologies. Leading CIOs implement centralized data governance and adopt a domain-centric approach to data modernization, ensuring their data assets are ready to be leveraged by AI tools across the business.

    By prioritizing talent development, focusing AI efforts on core business processes, and establishing a robust data infrastructure, leading CIOs can ensure they maintain their competitive edge and unlock the full value of their AI investments and drive sustainable business growth.

    Setting the Stage for AI Success

    Despite its immense potential, implementing AI comes with challenges. In Part 2, we’ll explore the key hurdles CIOs face—like ensuring cross-functional collaboration, handling ethical considerations, and establishing AI governance. We’ll also introduce the FAST AI Framework, a structured roadmap for achieving scalable AI wins.

    Stay tuned for Part 2, where we’ll dive deeper into these challenges and discuss actionable strategies to drive AI success. In the meantime, check out Aquant’s ebook 6 Key Considerations When Adopting AI for Service, the executive guide to kickstart your AI strategy for a more innovative, faster, future-proof service operation.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  8. AI Has Transformed Data Access: From Hours to Seconds—What’s Next?

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    By Assaf Melochna, President of Aquant, and Mark Hessinger, Global Customer Services Business Leader in Industrial Manufacturing

    For decades, the ability to access and leverage data effectively has been a major hurdle for companies, often stifling innovation and informed decision-making. A recent Forbes survey revealed that a staggering 70% of businesses cite this as a critical challenge. Despite amassing vast amounts of data, organizations struggled to utilize it meaningfully, creating operational bottlenecks that stalled progress. But now, with AI, the way businesses interact with data is changing—leading companies are already scaling these solutions, turning data into a dynamic tool for real-time insights and smarter decisions.

    The Old Data Dilemma

    Not long ago, accessing data was an arduous, time-consuming task requiring specialized skills. Massive datasets across multiple databases were often locked away, accessible only to data scientists who could navigate complex datasets and extract insights. In industries like manufacturing, service teams had to sift through mountains of maintenance logs, repair histories, and performance data just to identify patterns—a process that took not only hours but was fraught with potential human error, leading to missed opportunities and inefficiencies, and often resulting in stepping away from the task as the process was too cumbersome.

    AI’s Game-Changing Evolution: Data at Your Fingertips

    Fast forward to today, and AI has radically simplified this process. Imagine a service manager needing to understand recurring machinery failures. Instead of drowning in data, he or she can simply pose a question to an AI-powered virtual assistant or copilot, which pulls all relevant information—from maintenance records to sensor data and operator notes—into a cohesive response within seconds. This transformation is not just about speed; it’s about delivering precise, actionable insights instantly.

    For example, in customer service, AI-powered assistants can retrieve the latest customer interactions, system issue and maintenance histories, and even sentiment analysis at the click of a button. This enables representatives to provide highly personalized support in record time, boosting customer satisfaction and significantly reducing response times.

    A Glimpse into the Future: Instant, Intuitive, and Predictive Data Access

    The future of data access is set to become even more immediate and intuitive, transforming how businesses and technicians interact with information. Picture a factory floor where augmented reality (AR) glasses overlay real-time equipment data as technicians work, or scenarios where predictive analytics and AI-driven insights are delivered directly to a smartwatch, alerting employees to potential issues before they even know to ask.

    For example, instead of thumbing through manuals or waiting on hold for support, a field technician could use an AI-driven earpiece that provides step-by-step guidance based on the latest approved service protocols, past repair data, and real-time diagnostics. This immediate access doesn’t just reduce downtime; it also boosts accuracy and safety on the job.

    As AI increasingly integrates with IoT devices, we can expect an even more proactive approach to data access. AI systems will anticipate necessary information based on user actions or environmental cues. Imagine an AI solution detecting an unusual vibration in a machine and automatically sending a pre-emptive analysis and recommended action to the service engineer’s and supervisor’s devices—all without a single manual query.

    And this future is fast approaching – with a few major developments emerging within the last week. Microsoft’s recent launch of semi-autonomous AI agents hints at what’s to come, while SAP’s Joule agents demonstrate how integrated AI can break down silos across business functions like finance and supply chain, enabling complex, connected workflows. Similarly, Anthropic’s new AI agent, though a bit clunky now and still evolving, is pushing the boundaries of what AI can autonomously accomplish within enterprise environments. Together, these advancements signal a move toward more sophisticated, integrated AI solutions in business settings.

    Choosing the Right AI Tools: A Strategic Approach

    It’s easy to fall into the trap of adopting AI just because it’s a buzzword or because there’s extra room in the budget. But a strategic approach begins with identifying the real problems you want to solve and focusing on AI tools that directly address those challenges, delivering tangible value.

    A great example of this approach stems from a conversation we had at an industry conference a couple years back. Mark had been facing a problem that many service leaders in industrial manufacturing encounter: how to equip technical support teams with the knowledge they need, at their fingertips, to solve equipment issues more efficiently. It wasn’t just about implementing AI—it was about finding a solution that addressed a critical business need. That’s when our discussion turned to Aquant’s Triage solution, which was built on this very foundation.

    At the time, Assaf and his co-founder Shahar recognized a growing challenge in the industry, one that continues to affect many organizations today—the service expertise gap. This gap refers to the disparity in skill levels, knowledge, and problem-solving abilities between the most experienced service technicians and newer, less experienced team members. As products – machinery and equipment – become more complex and experienced workers retire, the gap widens, leading to inefficiencies like misdiagnosed issues, unnecessary parts replacements (often referred to as parts “shotgunning”), and avoidable service calls.

    Our mission is to bridge this gap by using AI-driven insights to capture, replicate, and scale the knowledge of top experts across the workforce. With AI analyzing historical data and service records, teams—regardless of experience—can access personalized recommendations, predictive insights, and decision support. This enables technicians at all levels to perform at the highest standard, improving efficiency, accuracy, and cost-effectiveness.

    This experience highlights that choosing the right AI tools isn’t just about the technology—it’s about solving real-world problems and enhancing performance. When approached strategically, AI transforms data into your organization’s most valuable asset—accessible, insightful, and actionable—empowering your teams to make better, faster decisions than ever before.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  9. Are Outcome-Based Pricing Models The Future of SAAS?

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    Should AI companies only be charging customers when the tech works? The Information recently reported that Zendesk, an AI-powered customer support software, changed its pricing model, charging businesses only when the AI chatbot successfully resolves customer issues without human intervention.

    This shift reflects a broader trend among software providers like Intercom and Forethought, who are also moving away from user-based subscription models and instead adopting outcome-based pricing, charging customers only when AI features perform effectively. This model aims to appeal to businesses that are cautious with their IT spending and skeptical of high-priced AI features. Although it’s still early days and outcome-based pricing remains a small part of their business, these companies believe the industry will eventually move in this direction as AI continues to automate tasks like customer support, sales, and recruiting.

    While this model is relatively new for software and AI companies, outcome-based pricing has existed for many years and has been introduced in other industries like healthcare and manufacturing. A well-known example is Siemens, which uses Energy Performance Contracts (EPCs) in building automation and energy management. In this model, Siemens installs energy-efficient systems and guarantees a specific level of energy savings for their customers. Rather than charging for the installation or the system itself, Siemens charges based on the energy savings achieved.

    For instance, Siemens may guarantee a 20% reduction in energy costs for a commercial building. If that target is met, the customer pays Siemens according to the savings realized. This outcome-based model ensures that the client’s payments are directly tied to the results delivered, aligning Siemens’ revenue with their customer’s energy efficiency goals.

    The Shift Toward Value-Driven Models

    For more than two decades, software-as-a-service (SaaS) pricing has relied on the “pay-per-seat” model, where companies pay based on the number of users who access the software. While effective in many cases, this model is starting to show its limitations in a world where AI enables automation at scale. As companies adopt AI-driven solutions, fewer employees are needed to perform many tasks, which leads to a paradox: businesses are being charged more for software that increasingly reduces their need for human users.

    Outcome-based pricing flips the script. Instead of charging for access, companies like Zendesk, Intercom, and Forethought are pioneering models where customers only pay when the AI system successfully completes a task—whether resolving a customer support ticket or closing a sales lead. This represents a fundamental shift from selling usage to selling value, where the true impact of software is tied to the results it delivers.

    This alignment of cost with benefit makes pricing more transparent and fair. Customers pay for the value they derive, which in theory, could foster deeper trust between software vendors and clients, particularly in uncertain economic environments.

    Case Study: Gym Pricing

    In one well-known case, a fitness center adopted a creative pricing model where members were charged only if they didn’t show up for their scheduled workout sessions. This reverse model, rooted in behavioral economics, used the principle of loss aversion to encourage regular attendance. By charging members for missed sessions rather than for attending, the gym created a financial incentive for people to stick to their fitness routines. This example highlights how outcome-based models can effectively align business revenue with customer behavior and desired outcomes.

    Although this may seem like a contrast to outcome-based pricing, the underlying principle is similar: the business aligns its revenue model with the customer’s behavior and outcomes. In this case, the desired outcome is the member’s attendance, which is vital for achieving fitness goals. The model still connects payment to performance and outcomes—in this case, the act of showing up—just as AI-driven outcome-based models charge customers when the software performs a specific task.

    Case Study: Rolls-Royce’s Power by the Hour

    Another classic example of outcome-based pricing comes from Rolls-Royce, which introduced the Power by the Hour model in the aviation industry. Instead of selling aircraft engines outright and charging separately for maintenance, Rolls-Royce charges airlines based on the number of hours the engine is operational. This shifts the maintenance responsibility from the airline to Rolls-Royce, aligning their revenue with the engine’s performance and uptime.

    Airlines only pay for the hours the engine is running effectively, meaning Rolls-Royce is incentivized to keep engines in top condition to maximize their operational hours. This model has since been adopted by other aerospace companies, including GE Aviation, and has become a standard in the industry.

    The Challenges: Navigating Complexity and Uncertainty

    Despite its potential, outcome-based pricing also comes with a set of unique risks. Perhaps the greatest concern for vendors is revenue volatility. Unlike traditional subscription models, which provide predictable recurring income, outcome-based pricing ties revenue directly to performance. If the AI underperforms or encounters technical issues, vendors could see sharp declines in revenue. For smaller companies, this unpredictability might pose significant financial risks.

    Success in outcome-based pricing depends on the ability to manage performance risk. Companies need to ensure that their AI systems can deliver consistently across varied environments. Otherwise, they expose themselves to revenue instability.

    Another challenge lies in defining outcomes. While automating customer support might be relatively straightforward, what about more complex tasks like strategic decision-making or creative problem-solving? How do you measure and price success in scenarios that are inherently ambiguous? The value of resolving a simple customer query is vastly different from resolving a high-impact technical issue, and businesses may struggle to find consensus on the “right” price for different outcomes.

    Looking Forward: A Hybrid Approach?

    The future of enterprise software pricing may lie in hybrid models that combine the predictability of traditional subscription pricing with the flexibility of outcome-based fees. In this model, customers pay a base fee for access to AI solutions but also agree to performance-based premiums when the software delivers specific, measurable outcomes. This structure could provide both vendors and buyers with the best of both worlds: predictable revenue streams for vendors and clear ROI for customers.

    Ultimately, the shift to outcome-based pricing reflects a broader transformation in how we think about technology and the value it brings. AI is no longer just a tool—it’s a partner in the workplace. And like any good partner, its worth is measured by its contributions, not just its presence.

    As AI continues to revolutionize business processes, companies that can successfully align their pricing models with the value they provide will be well-positioned to lead in this new era. Outcome-based pricing may not be a universal solution, but for many, it represents the next frontier in how software is sold and consumed.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant.