ZINFI Technologies, Inc.

ZINFI Technologies, Inc.
  • 5.0 (3)
  • MARKETING
  • UPDATED BIMONTHLY

ZINFI helps technology providers and their channel partners achieve profitable growth rapidly and affordably by automating Partner Relationship Management (PRM) processes globally.

  1. MAR 10

    The Future of SaaS: Agents Replace Software?

    The Future of SaaS: Agents Replace Software? The Future of SaaS is fundamentally redefining the software industry by shifting from static tools toward dynamic, autonomous agents that execute complex business workflows. In this episode, Sugata Sanyal interviews Alina Vandenberghe, the Co-Founder & Co-CEO of Chili Piper, who provides a roadmap for the next decade of digital tools. Vandenberghe explains how organizations are moving beyond traditional software procurement by adopting “vibe coding” to build custom agents that replace fragmented off-the-shelf software. This shift addresses the inefficiencies of seat-based licensing and moves the market toward an outcome-oriented model. By focusing on the Future of SaaS and the orchestration of interconnected systems, businesses can achieve higher efficiency and greater operational joy. According to Vandenberghe, the success of modern organizations lies in the symbiosis between human creativity and AI execution, ensuring that technology serves as a neural network for growth. “We are going to create an interconnected network of systems and workflows, and agents that are contributing and collaborating with each other, rather than just using isolated software tools.” — Alina Vandenberghe. Related Guidebook Building Scalable Companies via Venture Studios How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Building Scalable Companies via Venture Studios Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: The Future of SaaS: Agents Replace Software? ✔ Chapter 1: Radical Transparency: From Communist Romania to Silicon Valley Alina Vandenberghe’s leadership style is deeply rooted in her childhood in communist Romania, an environment where the secret police regularly bugged houses and public discourse on “difficult topics” was a dangerous act that could lead to severe consequences. This background directly fuels her professional mission to be an “enabler of difficult topics” in the public sphere. As the Technical Co-Founder of Chili Piper, she consciously defies the traditional image of a tech leader—balancing her role with being a mother of four and maintaining a commitment to radical transparency. Within her company, she prioritizes an “adult culture” where sensitive information, including company financials, future projections, and actual bank balances, is accessible to all employees. She believes that treating everyone as an adult capable of handling the truth is essential to fostering a healthy, high-trust work environment. This commitment to human fulfillment was the primary driver for Chili Piper’s creation. Unlike many Silicon Valley startups, the company did not begin with a specific product or a grand vision for a tool; instead, the mission was to create a company where Alina and her husband/co-founder could truly be happy. Having climbed the corporate ladder from intern to Senior Vice President in just six years across complex, publicly traded companies, Alina found herself highly compensated but deeply unhappy. This realization led to a “human-first” approach to business development. To find their product, they embedded with revenue teams and engaged approximately 20 “thought leaders”—individuals admired and replicated by peers—using simple pen-and-paper mockups to identify real-world sales friction. Through this research, they identified a massive “leakage” in the top of the sales funnel, where companies were failing to instantly connect inbound prospects with sales representatives. This approach allowed them to build solutions that solved the actual needs of revenue teams, such as automated round-robin routing and smooth handoffs between sales and onboarding. Unlike competitors who relied on bottom-up freemium models, Chili Piper utilized a top-down acquisition model, seeking strong internal champions like VPs of Growth or Sales leaders who were committed to changing internal processes to improve conversion rates. By focusing on these fundamental human and business needs, Vandenberghe has created a brand that proves authentic human connection and radical honesty are the strongest currencies in a high-tech world. ✔ Chapter 2: The Rise of Vibe Coding and the 90% Roadblock How do global organizations automate business workflows as we head toward 2026? The industry is currently witnessing the rise of “vibe coding,” a phenomenon where AI enables non-technical teams to build custom solutions that directly replace dozens of traditional SaaS tools. Alina Vandenberghe shares a striking example of this shift: her own team at Chili Piper managed to replace 10 separate internal software tools in just a single quarter by building specialized AI agents to handle specific tasks. This trend suggests that the future of SaaS is becoming more fragmented; companies are realizing they no longer have to adapt to the rigid, “one-size-fits-all” platforms of the past but can instead build exactly what they need. However, the transition to custom agents is not without its challenges. While vibe coding allows users to get 90% of a solution working almost instantly, it often hits a plateau when it comes to the final 10%—the “unsexy” requirements of enterprise-grade scalability, security, and 99.9% uptime. For major organizations, even a half-hour of downtime can result in hundreds of millions of dollars in lost revenue, making robustness a non-negotiable requirement. This creates a critical gap: the agility of “vibe-coded” agents must eventually meet the high-performance infrastructure capable of supporting massive traffic and complex revenue volumes. Vandenberghe envisions a future where the software ecosystem stabilizes into an interconnected network of agents rather than a monolithic stack. In this model, individual “zones of genius” are captured by specialized agents that collaborate across workflows. The company of the future will operate as an orchestrated collection of these agents, designed to solve specific problems with precision. This shift empowers employees to move away from “soul-sucking,” repetitive administrative tasks and toward high-impact creative work, allowing them to contribute their unique skills and dreams to the organization. ✔ Chapter 3: Redefining Value: Outcome-Based Pricing and Human Symbiosis What are the best practices for the Future of SaaS in 2026? As the cost of LLM tokens drops and intelligence becomes abundant, the traditional seat-based pricing model is becoming obsolete. Alina Vandenberghe reflects on her early days at Chili Piper, admitting she initially underpriced her software at roughly $3,000 to $6,000 per year because she was being compared to tools like Calendly, which cost as little as $10 per user. However, she soon realized that the true value was not in the “seat” but in the outcome—her software was generating millions of dollars in pipeline for her clients. She argues that if a single RevOps person can have a 10x impact through AI automation, the value lies in that massive ROI, not the number of people using the software. This necessitates a shift in how software companies justify their costs, away from discounted cash flow models toward pricing that directly reflects the revenue and business growth they generate for clients. The human element remains the most significant variable in this new economy. While AI can efficiently manage and route meetings and move accounts toward a “closed-won” status, it cannot replicate the emotional resonance and body language that drive high-stakes business decisions. Vandenberghe describes a “beautiful symbiosis” between her and her husband as co-founders that serves as a blueprint for the future of human-AI interaction. In their partnership, one provides the structured, pragmatic logic—acting as the “mural”—while the other acts as the “neural network,” providing deep observation and emotional awareness. This model of collaboration—where one party (or AI) handles the logic and execution while the human provides the strategic and emotional nuance—is how businesses will create a better future in a world of abundant artificial intelligence. Frequently Asked Questions What is the main way that the software industry is changing today? The main way the software industry is changing today is through a fundamental shift from static, human-operated tools to autonomous, intelligent agents that execute entire workflows. For years, SaaS has functioned primarily as a “database with a UI,” requiring humans to manually input and move data between platforms. However, as AI becomes more abundant and inexpensive, we are entering an era of “vibe coding” in which companies can build custom, specialized agents to replace rigid, off-the-shelf software. These agents don’t just store information; they act on it—handling everything from top-of-funnel lead routing to complex business orchestrations—allowing human employees to move away from repetitive, soul-sucking tasks and focus on high-level strategy and creative problem-solving. What is “vibe coding” and how does it help modern business organizations? “Vibe coding” represents a paradigm shift where business users describe a desired workflow or outcome to an AI, which then generates the underlying code to build a custom solution. This allows organizations to move away from rigid, “one-size-fits-all” SaaS platforms and instead create specialized agents tailored to their specific internal processes. By enabling the replacement of dozens of fragmented tools with integrated, automated

    38 min

  2. MAR 4

    Building Scalable Companies via Venture Studios

    Building Scalable Companies via Venture Studios A venture studio acts as a central engine that simultaneously builds and scales multiple startups. Unlike traditional accelerators, it offers long-term, hands-on involvement by integrating the roles of entrepreneur, operator, and investor. According to Matt Burris, a Subject Matter Expert (SME) on Venture Studios and a partner at the 9point8 Collective and a Senior Director at the Venture Studio Forum, notes in a podcast with Sugata Sanyal (Founder & CEO of ZINFI), this model provides essential day-one capital and operational support, allowing founders to prioritize product-market fit over administrative tasks. By centralizing resources, studios function similarly to ZINFI’s Unified Partner Management platform, orchestrating complex variables into a streamlined infrastructure. As we move through 2026, the studio model has emerged as a powerful alternative to standard venture capital. It is particularly effective for corporate innovators and seasoned entrepreneurs seeking to minimize risk while launching high-growth, scalable companies. “The Venture Studio is a co-founder. They have a vote on how things are going down, just like any other co-founder… preliminary numbers show that a Venture Studio provides about a hundred times more hands-on support than an accelerator does. — Matt Burris. Related Guidebook Building Scalable Companies via Venture Studios How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Building Scalable Companies via Venture Studios Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Building Scalable Companies via Venture Studios ✔ Chapter 1: Defining the Venture Studio Asset Class How to automate scalable partner ecosystems via the venture studio model? A venture studio is defined as a company that builds other scalable companies by playing three core roles in every venture it creates: the entrepreneur, the operator, and the investor. This triple-threat involvement distinguishes the studio model from traditional venture capital or incubators, which typically only provide one or two of these elements in a fragmented manner. By integrating these functions, studios can move significantly faster through the "zero-to-one" phase, providing all the legal, financial, and operational support that a solo founder would otherwise have to source independently. The flexibility of the venture studio model allows it to leverage diverse capital sources beyond traditional venture capital, including private equity exits, public financing, and state-level debt vehicles. This versatility is one of the model’s most untapped aspects, as it allows studios to build scalable companies tailored to specific financing models. Matt Burris notes that this adaptability changes the economics of the studio and dictates the types of companies built, whether deep tech, biotech, or "boring" but profitable businesses. For entrepreneurs, the studio serves as a high-conviction partner, providing thousands of hours of hands-on support, compared to the minimal hours offered by standard accelerator programs. This level of involvement ensures that the venture undergoes a "pressure cooker" of validation before significant time is invested. By acting as a co-founder, the studio ensures that the unit economics are modeled appropriately and the business case is bulletproof before the company ever attempts to raise follow-on capital from the broader market. ✔ Chapter 2: Driving Revenue Through a Strategic Co-Selling Framework What are the best practices for partner lifecycle management in corporate studios in 2026? Successful venture building within a large corporation — often termed intrapreneurship — typically requires either a high-level champion in leadership or a founder willing to navigate a long, brutal internal journey. The roadmap in a large company is often rigid, with budgets and teams already accounted for, making it difficult to insert new, disruptive ideas for scalable companies. Matt Burris explains that the venture studio model solves this by fully encapsulating the skills needed to take an idea to market without relying on external corporate resources. One of the primary blockers in corporate innovation is “blindness” to true costs, which prevents accurate modeling of unit economics and often leads to project rejection by upper management. Outside the corporate structure, venture studios have a much clearer picture of these costs because they have to manage them directly to survive. This transparency enables more realistic business cases and better alignment with customer needs, as independent studios are not constrained by internal corporate politics or sales-deal sensitivities that often prevent intrapreneurs from speaking directly to customers. The studio model compensates for founder gaps by tailoring support based on the entrepreneur’s background, whether they are a serial founder or a career professional with decades of corporate experience. While a VC might provide introductions to its network, a studio provides the operational muscle to execute on those introductions and build scalable companies. This distinction is critical for corporate entities looking to innovate without disrupting their core operations, as the studio acts as a standalone engine for growth and experimentation. ✔ Chapter 3: The Future of AI and Human Connection in Partnerships How does AI-powered PRM infrastructure drive partner-led growth ROI? The future of venture building is increasingly data-driven, with top-tier studios utilizing custom AI to map opportunities and validate ideas for scalable companies with unprecedented speed. For example, some advanced studios in Europe maintain massive databases of thousands of transcribed customer discovery calls, which are then loaded into proprietary AI models. When a new idea is proposed, the AI can immediately cross-reference it against existing customer profiles and interviewer notes to identify potential pitfalls or overlooked market angles. This sophisticated process is what makes or breaks the "zero-to-one" phase in a venture studio. Unlike individual startups that may struggle to find a single valid opportunity, a studio’s ability to run multiple ideas through a data-rich validation engine increases the success rate for scalable companies. This level of infrastructure is rarely seen even in large corporations, where innovation teams are often siloed or prohibited from direct customer interaction. By building these processes into the studio’s core, they create a repeatable factory for high-quality company formation. As the venture studio category continues to build momentum — now with several thousand studios globally — the formalization of these best practices is essential. Organizations like the Venture Studio Forum are working to document these "stunning" internal processes to help entrepreneurs and investors identify the right partners. In the next 24 months, the integration of AI into deal assessment and portfolio management will likely become the standard, further widening the gap between traditional investment models and the high-touch, data-powered venture studio focused on scalable companies. Frequently Asked Questions What is a venture studio, and how is it different from an accelerator, incubator, or VC fund? A venture studio is a company that builds other scalable companies by simultaneously acting as an entrepreneur, operator, and investor in each startup it creates. Unlike accelerators or incubators that offer limited-duration programs—or VCs that primarily provide capital—a studio is a true co-founder with a vote, delivering day-one capital plus deep legal, financial, and operational support. How does the studio model reduce “zero-to-one” friction and de-risk early company formation? Studios centralize critical resources—capital, infrastructure, and strategic validation—so founders can focus on product–market fit instead of administrative overhead. Startups are run through a “pressure cooker” of validation where unit economics are modeled, and business cases are stress-tested before pursuing outside capital, ensuring the fundamentals for scalable companies are sound from day one. What capital sources can venture studios use, and how does that shape the companies they build? Beyond traditional venture capital, studios can leverage private equity exits, public financing, and state-level debt vehicles. This flexibility changes the studio’s unit economics and influences which ventures they pursue—ranging from deep tech and biotech to “boring” but profitable scalable companies—because each financing style aligns with different growth profiles. When should corporations consider a venture studio instead of intrapreneurship? Large organizations often face rigid roadmaps, internal politics, and “blindness” to true costs, which stall new ventures. A venture studio operates independently of these constraints, bringing transparent cost management, direct customer access, and the operational muscle to execute on scalable companies without disrupting core operations. How are AI and data reshaping venture studios—and what does that imply for partner-led growth? Leading studios use proprietary AI and large datasets to rapidly validate ideas and map opportunities, improving zero-to-one success rates for scalable companies. This mirrors how AI-powered partner relationship management (PRM) systems and platforms like ZINFI’s Unified Partner M

    46 min

  3. JAN 30

    Scaling Salesforce Growth Through Focused Strategic Partner Ecosystem Orchestration

    Scaling Salesforce Growth Through Focused Strategic Partner Ecosystem Orchestration Partner ecosystem orchestration is the strategic coordination of diverse partner entities within a technology environment to drive scalable revenue and customer success by aligning specific product solutions with vendor sales goals, ensuring every stakeholder achieves measurable growth and long-term market sustainability through shared resources and unified management processes. According to Sam Yarborough, an industry practitioner at Arcadia, effective orchestration requires moving from reactive management to a proactive co-selling framework. This approach ensures that technology partners provide specific value to account executives and solve clear customer problems. Sam Yarborough highlights that focusing on specific industry verticals, such as healthcare and financial services, is more effective than broad, horizontal strategies. She demonstrates how this focus drives significant partner-led growth and ROI metrics. By aligning with ZINFI Unified Partner Management principles, organizations can transform complex ecosystems into predictable revenue engines. “Any relationship that I had built the previous year, I could then go back and say, ‘What new accounts do you have?’ Staying close to people even if there is no immediate value is an under-utilized tactic.” — Sam Yarborough, SME. Related Guidebook Scaling Salesforce Growth Through Focused Strategic Partner Ecosystem Orchestration How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Scaling Salesforce Growth Through Focused Strategic Partner Ecosystem Orchestration Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Scaling Salesforce Growth Through Focused Strategic Partner Ecosystem Orchestration ✔ Chapter 1: How to Navigate Large Cloud Ecosystems Partner ecosystem orchestration requires starting with a very small and specific focus within large organizations like Salesforce. Sam Yarborough explains that many professionals feel overwhelmed by the technical complexities of massive hyperscaler environments. She suggests that partners should avoid trying to service every customer at once. Success comes from verticalizing your approach to a few key use cases. This method allows you to master a niche before you try to expand. Many companies fail because they spread their resources too thin across many industries. You must pick one area where your product solves a painful problem. This focused effort builds the foundation for long-term growth. Industry teams in healthcare and financial services often have specific needs that a partner can address directly. Sam Yarborough emphasizes that delivering value on a small scale helps build the necessary trust for larger opportunities. Once you deliver results for one person, they will naturally refer you to other teams within the organization. This creates a snowball effect that drives long-term autonomous partner engagement. You should find one account executive who is willing to experiment with your solution. Prove your value to them with a real customer win. This success becomes your internal marketing tool to reach more teams. Most partnerships fail because they lack these early and visible wins. Technology partners must understand the mechanics of the vendor program to be effective. Sam Yarborough mentions that her initial program was reactive and lacked a clear strategy before she prioritized data-driven decisions. By focusing on where leads were actually trickling in, she was able to rebuild a dying partnership into a core revenue driver. This demonstrates the importance of B2B ecosystem governance in managing high-growth channel relationships. You must study the compensation plans of the vendor sales team to align your goals. Knowledge of their internal processes makes you a more valuable partner. ZINFI Unified Partner Management helps you organize these data points for better decision making. Effective management turns a chaotic ecosystem into a predictable revenue stream. ✔ Chapter 2: Driving Revenue Through a Strategic Co-Selling Framework A co-selling framework is most effective when it removes all possible roadblocks for the vendor sales team. Sam Yarborough discusses her experience hosting lunch and learn events that quickly booked 20 customer meetings. This was more efficient than traditional BDR motions that might take a month to achieve the same results. High-impact co-selling requires making the vendor account executive the hero of the story. You must prepare all the marketing materials and customer data in advance. The account executive should only have to invite their customers to the meeting. Your job is to make their life easier while helping them hit their sales targets. This selfless approach builds deep loyalty among the vendor sales force. The SME notes that partner managers must act as a bridge between finance, product, and sales teams. You must design your offering to make the sale as easy as possible for the partner. If you make the process difficult, you will fail to gain any traction within the ecosystem. Sam Yarborough highlights that her focus allowed her company to source 65% of its revenue through these strategic motions. You need to talk to every department in your own company to ensure alignment. Pricing must be simple and transparent for the partner to explain to their clients. Product features should solve the exact gaps identified by the vendor. Successful co-selling is a team sport that involves your entire organization. Partner-led growth ROI metrics prove the value of this focused approach to executive leadership. Sam Yarborough explains that once you achieve a win, you must communicate that success across the entire organization. Telling other account executives about successful deals creates more demand for your partnership. This proactive communication is a core element of ZINFI Unified Partner Management. You should create case studies that highlight how the vendor salesperson benefited from the deal. Share these stories in internal newsletters and during team meetings. Visibility is the key to maintaining momentum in a large ecosystem. When everyone sees you as a winner, they will want to work with you. ✔ Chapter 3: The Future of AI and Human Connection in Partnerships Autonomous partner engagement is becoming a central theme as companies like Salesforce introduce tools like Agentforce. Sam Yarborough suggests that while AI is changing the landscape, human relationships remain the foundation of successful partnerships. Organizations are currently experimenting to find the right balance between automated processes and manual outreach. Staying open to these new technologies is essential to avoid being left behind. AI can handle the routine tasks of partner matching and data entry. This allows human partner managers to focus on complex strategy and personal networking. You must integrate these new tools into your existing workflows to remain competitive. ZINFI Unified Partner Management provides the platform to merge AI with human expertise. Executive teams now expect AI to be part of the product roadmap and the partner workflow. Sam Yarborough notes that early adopters of AI tools will likely receive more attention and resources from large vendors. However, the ROI of new autonomous tools is still being calculated by many industry practitioners. Partner leaders must balance the hype of AI with the practical needs of their ecosystem. You should start small by using AI to automate your reporting and lead tracking. Test how these tools impact your daily productivity before rolling them out to the whole team. Continuous learning is necessary as the technology evolves every month. Understanding the limits of AI is just as important as knowing its capabilities. The humanity of partnerships is a unique value that technology cannot currently replace. Sam Yarborough emphasizes that personal connections and tenacity are what allow new partners to break into established territories. AI can help with partner matching and data analysis, but it cannot replace a face-to-face relationship. Maintaining this harmony between tech and touch is a primary goal for modern B2B ecosystem governance. You should still prioritize taking partners to lunch and attending industry events. These personal interactions build the trust that is required for large enterprise deals. Technology should support these relationships rather than replace them. A balanced approach ensures that your partnership remains resilient in a digital world. Frequently Asked Questions What is partner ecosystem orchestration in the Salesforce environment? Partner ecosystem orchestration within the Salesforce environment involves strategically managing various relationships, including Independent Software Vendors (ISVs), agencies, and technology partners, to drive mutual value. Rather than being reactive, effective orchestration requires a proactive strategy that focuses on verticalizing into key use cases, such as healthcare or financial services, to deliver specific impact. By aligning partner solutions with the needs of Salesforce Account Executives (AEs) and their customers, organizations can ensure higher platform adoption and more efficient business outcomes for the entire ecosystem. How can an ISV successfully engage with Salesforce Account Executives? To successfully engage with Salesforce Account Executives (AEs), ISVs must prioritize a value-driven, highly specific messaging approach. Instead of generic outreach, ISVs should demonstrate exactly how their solution s

    38 min

  4. JAN 29

    Scaling Nonprofit Fundraising Through Strategic Partner Ecosystem Orchestration

    Scaling Nonprofit Fundraising Through Strategic Partner Ecosystem Orchestration Partner Ecosystem Orchestration is the strategic alignment of diverse third-party entities to deliver integrated value to a specific market segment. In the nonprofit sector, this involves connecting donors, charitable organizations, and technology providers to ensure efficient mission fulfillment. According to Jamie Mueller, an industry leader at FundraiseUp, scaling these ecosystems requires a shift from transactional referrals to deeply integrated business partnerships. The nonprofit market represents approximately $1 trillion in annual global revenue. Managing this scale requires a sophisticated tech stack and a robust partner strategy. By leveraging ZINFI Unified Partner Management principles, organizations can automate the partner journey from recruitment to revenue influence. This approach ensures all stakeholders win while maximizing social impact through modern donation technologies. “When we had a partner involved in a deal, whether they sourced it or were assisting or influencing it, we saw double-digit improvements in closed win rates.” — Jamie Mueller, SME. Related Guidebook Scaling Nonprofit Fundraising Through Strategic Partner Ecosystem Orchestration How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Scaling Nonprofit Fundraising Through Strategic Partner Ecosystem Orchestration Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Scaling Nonprofit Fundraising Through Strategic Partner Ecosystem Orchestration ✔ Chapter 1: Understanding the Global Nonprofit Landscape The global nonprofit market operates with approximately $1 trillion in annual revenue influenced by diverse organizations. This ecosystem includes major players like United Way, UNICEF, and Greenpeace along with thousands of local community groups. Industry practitioner Jamie Mueller notes that the nonprofit tech sector often lags behind for-profit industries by five to ten years. This gap creates a significant opportunity for innovation through specialized SaaS solutions like Fundraise Up. Fundraising organizations range from medical research institutions to local food banks and social safety nets. Each entity requires secure ways to manage donor data and process financial contributions effectively. Technology partners must bridge the gap between legacy systems and modern e-commerce standards. Successful orchestration requires understanding these unique tax codes and regulatory environments across different global regions. Modern nonprofits increasingly rely on an ecosystem of consultants, marketing agencies, and software vendors. These partners help organizations move away from traditional direct mail toward digital-first fundraising strategies. The complexity of these interactions necessitates a unified approach to partner relationship management. Orchestrating these players ensures that funds are generated and stewarded with high ethical standards. ✔ Chapter 2: The Quadruple Win Partnership Model Strategic partnerships in the fundraising space must facilitate a “quadruple win” to remain sustainable and profitable. First, the individual donor must feel a personal connection and see the measurable impact of their gift. Second, the nonprofit organization must maximize its revenue while minimizing the friction associated with collecting donations. Third, the consulting or technology partners must find value in recommending specific solutions to their clients. Fundraise Up only succeeds when these three other stakeholders achieve their goals simultaneously. This transactional model creates a symbiotic cycle where program impact drives more donor engagement. Industry practitioner Jamie Mueller emphasizes that no company can thrive in the modern market without active collaboration. This model aligns business goals with social impact to create a scalable growth engine for all parties. The Quadruple Win requires moving beyond simple referral fees to true business alignment. Partners provide the localized expertise and implementation services that software vendors cannot offer alone. By integrating Fundraise Up into a larger tech stack including CRMs like Salesforce, partners deliver a complete solution. This collaborative approach builds long-term trust and ensures the nonprofit mission remains the central focus. ✔ Chapter 3: Restructuring Teams for Revenue Influence Scaling a partner program requires a transition from purely transactional activities to tracking total revenue influence. In 2024, Fundraise Up focused on analyzing partner performance and establishing clear performance standards. This analytical phase identified that partner involvement leads to a 10% or higher increase in closed-won rates. Consequently, the team shifted its focus from just sourcing leads to influencing the entire customer journey. The team structure now reflects a sophisticated partner journey model with specialized roles for success and hunting. A dedicated Partner Success Manager handles a small group of high-value partners that generate half of the channel revenue. This role provides white-glove service, including QBRs and direct access to the product roadmap. Meanwhile, Partner Managers act as hunters to recruit net-new partners in specific verticals like higher education. Successful orchestration involves rotating partners between tiers based on longevity and business behavior. Industry practitioner Jamie Mueller utilizes tools like Crossbeam for account mapping to align with the direct sales team. This alignment ensures that partners are focused on the highest-priority enterprise logos. By prioritizing influence over simple referrals, the organization maximizes the strategic value of the entire ecosystem.

    47 min

  5. 12/03/2025

    Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity

    Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity In this crucial discussion, Sugata Sanyal, Founder & CEO of ZINFI, sits down with Barry Mainz, CEO of Forescout Technologies, to dissect the Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity. Barry Mainz highlights how the threat landscape has dramatically shifted, noting that the exposure of Critical Infrastructure Protection is growing exponentially due to legacy vulnerabilities in OT devices. The conversation introduces how Forescout is adapting its Forescout Security platform and evolving its Channel Partner Strategy to meet the new demands in sectors such as manufacturing and oil & gas. Mainz also offers deep insights into shifting C-level priorities, where Cybersecurity Metrics like ARR and GDR now dominate. The discussion concludes with insights on the ROI of AI and the next major threats: Quantum Computing and Agentic AI. This is a must-listen for understanding the intersection of digital transformation and physical world security. Related Guidebook Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Next Frontier of OT/IoT Ecosystem: AI & Cybersecurity ✔ Chapter 1: Cultural Blueprint and Critical Shift to OT/IoT Security Barry Mainz outlines the Forescout Security culture, defining it not as an amorphous concept but as the company’s blueprint for problem-solving and establishing core routines. Forescout’s ethos is straightforward: one must constantly improve, as “there’s no staying the same” in the dynamic world of cybersecurity. A foundational routine involves executive engagement at the point of sale, or “where the money changes hands,” to gain the customer’s perspective. This unique focus on customer friction and ease of doing business drives cultural evolution and helps the organization refine its culture over time. This cultural commitment is crucial given the company’s 25-year history in a complex, global, and nation-state-involved space. The discussion shifts to the OT/IoT Ecosystem, highlighting the massive change driven by connected devices that extends beyond traditional IT. Mainz, leveraging his experience with embedded operating systems, notes that non-traditional devices, such as industrial controls (IOT/OT and medical OT), now have vulnerability issues (CDEs) exceeding those of standard IT operating systems. These critical infrastructure devices—from power grids to industrial robots—were not built for patching, creating significant, hard-to-remediate risk. Forescout recognized this shift early, transitioning from a core NAC company to a broad Forescout Security platform focused on network operations security for the world’s largest public and private companies. This evolution into Critical Infrastructure Protection is accelerating due to the increasing frequency of severe breaches and regulatory pressure. The US Disclosure Act, for instance, has made CFOs and CEOs personally liable for non-disclosure of breaches affecting OT/IoT devices. This regulatory push is forcing mature organizations, which often deal with outdated, decades-old systems, to rethink their approach to security. Conversely, emerging markets (like META and India) frequently exhibit less ego and legacy lock-in, making them more open to modern, flexible solutions, which has led to them becoming Forescout’s fastest-growing regions. The complexity of the OT/IoT Ecosystem demands this cultural fluidity. ✔ Chapter 2: Channel Partner Strategy and Evolving Cybersecurity Metrics Forescout operates on a 100% partner-based go-to-market model. The ecosystem comprises distributors (essential for hardware logistics and export compliance), resellers, and strategic alliance partners, such as Siemens or Yokogawa. The channel is segmented by a combination of vertical alignment (e.g., dedicated reps for healthcare, federal government) and horizontal motion for down-market strategics. This network extends to strategic alliances for deep, technical integrations, often resulting in ODM or OEM relationships. The Channel Partner Strategy includes sell-with (integration) and sell-through motions, covering 700 alliance partners and 25 OEM/ODM relationships worldwide. Distribution partners have moved far beyond their traditional roles. Today, they are critical value-add partners, providing specialized professional services, Tier 0/1 support in local regions, and acting as thought partners to guide the proper go-to-market motions, especially in emerging territories. The shift to a subscription-first business model (90% software) has fundamentally changed the financial metrics tracked by the board. Key metrics now include Annual Recurring Revenue (ARR) and Gross Dollar Retention (GDR), along with contract length, which have superseded TCV and non-recurring revenue. While traditional hardware metrics, such as RMAs, are still tracked, they are less central to running the business. Other critical metrics include CSAT/NPS scores, pipeline coverage, and sales productivity indicators. Forescout’s product is ambidextrous, offering both cloud and on-prem deployment options, a flexibility that is proving critical as large enterprise customers begin to experience cloud repatriation—moving workloads back to cost-effective co-location due to CapEx/OpEx trade-offs on hyperscale platforms. ✔ Chapter 3: AI Investment, Talent, and the Next Big Security Bets Measuring the ROI of AI investment is a challenge. Forescout’s investment strategy is two-fold: Internal Productivity (e.g., advanced translation, co-pilot functions) and Product Feature Enhancement. In the product, AI is utilized as a tool to generate audit reports and prioritize events for the Security Operations Center (SOC). However, due to concerns over hallucinations and reliability, Forescout still doesn’t permit AI agents to execute direct network control (like blocking). A new element in the sales cycle is a customer checklist to ensure vendors are utilizing AI, indicating a shift in customer procurement requirements. The board-level dialogue has matured from hype to pragmatism, asking for “real facts” on AI’s impact. The shortage of AI/ML talent is a significant struggle, reminiscent of past industry transitions. The challenge lies in the lack of maturity in the AI space—specifically, the need to change language models, system choices, and the understanding of correct application—making it challenging to hire and train the proper personnel. This talent gap must be addressed to leverage AI within the OT/IoT ecosystem successfully. Finally, Mainz reveals his subsequent big bets for the cybersecurity industry. The first is Quantum Computing, which is seen as a near-term existential threat due to its potential to allow “bad actors” to unencrypt vast amounts of data in seconds—a post-quantum encryption problem that demands industry attention. The second is Agentic AI. He also dispels the myth that “IOT and OT don’t matter” on campus.

    43 min

  6. 12/02/2025

    Future of B2B Marketing: AI & Trust Redefining the Journey

    Future of B2B Marketing: AI & Trust Redefining the Journey The world of B2B Marketing is undergoing a seismic shift, driven by rapid advancements in technology and a fundamental change in how buyers engage. In this insightful discussion, Sugata Sanyal, Founder & CEO of ZINFI, sits down with industry veteran Rick Wootten to dissect the forces shaping the future. They explore the journey of demand generation from its roots in Web 1.0 to the complexities of today’s multi-touch, multi-channel environment. Key topics include the disruptive impact of AI Marketing on content strategy, the critical challenge of building and maintaining trust with increasingly skeptical buyers, and the strategies marketers must adopt to navigate this new, decentralized B2B Buyer Journey. Tune in to learn how a multi-touch playbook can secure your success in the Future of Marketing and pipeline generation. Related Guidebook Hybrid Cloud and Edge AI Computing Impacting the Future of PRM How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Hybrid Cloud and Edge AI Computing Impacting the Future of PRM Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Future of B2B Marketing: AI & Trust Redefining the Journey ✔ Chapter 1: The Historical Arc of Demand Generation: From Web 1.0 to Web 2.0 The genesis of digital B2B Marketing was a far cry from the complex, data-driven systems of today. The early Web 1.0 era saw marketing websites primarily serving as little more than online brochures—a static catalog that users could browse, but not truly interact with. Demand generation at the time was predominantly manual and personal, relying heavily on in-person events and cold-calling. The shift began with pioneers who introduced the novel idea of a web form, allowing companies to capture customer interest and respond almost in real-time, effectively getting over the buyer’s challenge of having to call and listen to a sales pitch. This consumerization of IT, as it was called, marked a pivotal moment in which decision-making began to move online, providing a massive new opportunity for companies to capitalize on digital channels. This foundational change in how the buyer received information set the stage for the next phase of digital evolution. The transition to Web 2.0 fundamentally reshaped B2B Marketing strategies by shifting the focus from simple online presence to building dynamic e-commerce businesses and, critically, customer relationships. This era saw the advent of marketing automation tools, such as Eloqua, which provided the first glimpses of intelligence—the ability to send personalized communications and track email opens or purchases. This increased sophistication enabled marketers to move beyond simple database emails and leverage new insights into buyer behavior, allowing them to tailor content and target individuals based on the problems they were trying to solve. The intelligence, though primitive by today’s standards, offered marketers a distinct advantage in improving conversion rates and revenue generation, even leading to aggressive promotional tactics that created channel conflicts that were common in 2005-2006. The key lesson learned was the critical need to adapt quickly and develop effective techniques for scaling campaigns. The evolution from a static online brochure to an interactive online experience introduced the concept of the B2B Buyer Journey, a notion previously reserved for consumer-centric marketing. This period was characterized by a rapid, shared innovation where marketers constantly reviewed and copied the source code of interesting websites to build upon each other’s techniques, drastically accelerating the sophistication of platforms. By the late 2000s, this collective knowledge had laid the groundwork for advanced capabilities, such as lead scoring, which became a centerpiece of inbound methodologies promoted by companies like HubSpot. This trajectory confirmed that the B2B Marketing playbook was no longer a matter of a single interaction, but an increasingly intelligent sequence of engagements, moving the industry decisively away from purely manual demand generation methods. ✔ Chapter 2: Navigating the Multi-Touch, Multi-Channel B2B Buyer Journey With the arrival of the iPhone era around 2007, the marketing landscape splintered, demanding a fundamentally different approach to the B2B Buyer Journey. The security-centric trend of Bring Your Own Device (BYOD) meant that employees were now interacting with B2B content across laptops and personal phones, presenting a profound challenge for marketers who could not easily track one individual across multiple devices. This cross-device gap was partially filled by leveraging ideas and brand-building concepts from B2C, which had already invested heavily in digital channels. While initial ROI on tactics like in-app and mobile advertising was often underwhelming due to poor data and targeting, the core shift was clear: the buyer was now reachable on exponentially more platforms, from SMS and various social networks to targeted ads seen while shopping on Amazon. The central challenge in contemporary B2B Marketing is that the playbook is no longer a simple single-touch conversion, such as a web form lead, but a complex, multi-touch engagement model. Marketers must accept that a successful pipeline is often the result of a coordinated sequence of interactions across multiple channels. A company’s ideal playbook might involve seeing a person at an event, following up with content syndication, and then guiding them to a private executive dinner. Crucially, the effectiveness of any given channel is constantly in flux, making strategic re-evaluation essential. Channels previously considered obsolete, such as direct mail and radio, are now experiencing a resurgence in effectiveness precisely because they are not saturated, demonstrating that marketers must continually refine their tactics to maximize reach. Despite the explosion of channels and tactics, the tooling for B2B Marketing has also advanced dramatically to manage this complexity, particularly with orchestration platforms. Modern tools from companies like Adobe, Sixth Sense, and Demandbase enable marketers to view all these touchpoints and gather signals they previously couldn’t. For instance, these platforms can indicate that a target buyer is in a purchase cycle by revealing they downloaded a case study from a third-party site. This capability means that while the buyer’s journey is much more complicated, the technological ability to manage, track, and optimize campaigns across a multi-touch B2B Buyer Journey has also evolved, moving far beyond the “stone tools” of early marketing automation. ✔ Chapter 3: AI, the Trust Deficit, and the Future of B2B Marketing Skills The rise of generative AI introduces polarizing elements and a significant trust deficit into the already complex world of B2B Marketing. With AI capable of writing content and creating videos, the challenge lies in the current lack of trust that buyers, particularly Gen Z, have for media and advertising. This distrust is leading to a profound shift in information validation, signaling a potential return to the most fundamental source of influence: peer-to-peer networks and personal relationships. It is projected that as this new generation of budget owners advances in their careers, their network of knowledgeable peers will become the primary source for information, referrals, and validation, making the “human element” of marketing more critical than ever. The long-term outlook is optimistic, as transparency mechanisms, such as tagging AI-generated content, will eventually help to rebuild that foundational trust. Beyond content creation, AI is enabling practical B2B Marketing applications that fundamentally change the planning process and go-to-market engineering. AI’s real power lies in its ability to pull in and cross-reference massive, disparate datasets—such as census information, competitor office locations, and industry data—to generate actionable insights on which markets to enter quickly. This capacity for mass data analysis and orchestration is built into virtually every modern marketing tool, from Marketo to Sixth Sense, meaning that all future marketers must have a concept-level understanding of AI literacy. This analytical capability facilitates the ongoing convergence of marketing stacks and tactics between mid-market and enterprise organizations, where the complexity of the problems being solved remains the same. In building out a modern B2B Marketing team, a CMO’s focus must shift from pure technical skills to foundational soft skills, which are the only constants in an ever-changing landscape. The three critical non-negotiables for a successful modern marketer are Aptitude (raw ability), Passion (loving the job you do), and Self-Awareness (commitment to lifelong learning and constant self-improvement). Combined with AI literacy and an unrelenting commitment to consuming industry content, these traits will determine who succeeds in the future. The volatility of channels, the power of AI, and the buyer’s demand for trust ensure that continuous learning and core human qualities will drive the success of the Future of Marketing.

    49 min

  7. 12/01/2025

    RevOps is Dead: Why GTM Ops is the Future

    RevOps is Dead: Why GTM Ops is the Future In this insightful episode, Sugata Sanyal, Founder & CEO of ZINFI, sits down with Andy Mowat, Founder of Whispered and former RevOps leader at Upwork, Box, Culture Amp, and Carta. They dive into the evolution of Revenue Operations (RevOps), which Andy argues is an overused term for what should be called Go-to-Market Ops. The discussion highlights the six core functions of a modern GTM Ops team and the move towards a Modern Data Stack. Andy shares his view that we are in the "dark ages" of systems like Salesforce and must prioritize AI Fluency and the right mindset over just skill sets when hiring. Listen in to understand the future of the operations function and what leadership skills matter most in the age of AI. Related Guidebook Hybrid Cloud and Edge AI Computing Impacting the Future of PRM How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Hybrid Cloud and Edge AI Computing Impacting the Future of PRM Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: RevOps is Dead: Why GTM Ops is the Future ✔ Chapter 1: The Evolution from RevOps to Go-to-Market Ops The term Revenue Operations (RevOps) is often misapplied, with many teams simply performing Sales Operations under a trendier title. Andy Mowat and his peers prefer the designation “Go-to-Market Ops” because it properly encompasses the crucial functions of Marketing Operations (MOPs) and Customer Success Operations (CS Ops). This unified approach ensures coordination and prevents system conflicts, particularly as data flows from marketing systems into sales systems. A comprehensive GTM Ops function is defined by six core areas: Sales Operations (SOPs), GTM Systems, Sales Strategy, Post-Sales/CS Ops, MOPs, and Enablement. Each area plays a distinct but interconnected role, from territory design and commissions (SOPs) to using product data for efficiency (CS Ops) and managing marketing automation systems (MOPs). An effective GTM Ops leader must think strategically about both the systems and the processes by which the company sells. ✔ Chapter 2: Modern Data Stack and the Dark Ages of CRM Andy reflects on the technological journey of Revenue Operations across various unicorn companies, including Upwork, Box, and Culture Amp, noting that the discipline is constantly evolving. While early roles were focused on core systems, the need for a Modern Data Stack became clear to handle sophisticated concepts like pipeline coverage, which existing CRMs couldn’t manage without a dedicated data layer. He highlights tools like Fivetran, DBT, and Census as essential components for a modern GTM data environment, emphasizing that today, a rev ops professional needs fluency and understanding of how data works, often including SQL knowledge. Despite the proliferation of tools, Andy believes the industry is in the dark ages of systems, arguing that the user experience of dominant CRMs like Salesforce is “terrible” and not built for the modern world of unstructured and product data. This frustration with legacy systems has led to the emergence of next-generation solutions, with a prediction that the next CRM will likely be a data warehouse. ✔ Chapter 3: The Impact of AI on GTM Ops Talent and Mindset When hiring for Revenue Operations, particularly in the age of AI, mindset is significantly more critical than just skill set. Andy Mowat stresses that key traits include intensity, the ability to articulate, work cross-functionally, and a willingness to “get your hands dirty.” For junior roles, he often grows talent from high-performing CS or support teams, looking for that spark of logical thinking and structured thinking. At the director level and above, hiring managers require individuals who possess the skills to hire, manage, and develop other director-level staff, with a focus on managing up, making trade-offs, and articulating a clear strategy. The most significant shift today is the absolute necessity for AI Fluency. Failing to embrace and utilize AI is detrimental, leading to a demand for new, yet-to-be-fully-defined roles, such as the GTM Engineer. This new functional role is emerging because specialized tools, like Clay, can be complex, following a pattern where new tools create new jobs, which then prompts the development of more tools to make those jobs easier.

    43 min

  8. 12/01/2025

    Future of Managed Service Providers: Automation, Security, and AI

    Future of Managed Service Providers: Automation, Security, and AI In this insightful episode, Sugata Sanyal, Founder & CEO of ZINFI, welcomes Michelle Accardi, CEO of Liongard, for a deep dive into the evolving world of channel partnerships and cybersecurity. Michelle shares her journey through significant scale-ups, offering critical insights on how managed service providers (MSPs) can maximize their business valuation. The discussion highlights the shift from one-time sales to recurring revenue, emphasizing the need for efficiency, automation, and a clearly monetized tech stack. They explore the impact of AI automation on service delivery and talent, concluding with a focus on human skills, curiosity, and the critical importance of a strong network in the channel ecosystem. Listen now to understand the future path for profitable MSP growth. Related Guidebook Hybrid Cloud and Edge AI Computing Impacting the Future of PRM How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management Download your COMPLIMENTARY COPY of Hybrid Cloud and Edge AI Computing Impacting the Future of PRM Best Practices Guidebook. How AI, Hybrid Cloud, and Edge Computing Are Transforming Partner Relationship Management. Download for FREE Video Podcast: Future of Managed Service Providers: Automation, Security, and AI ✔ Chapter 1: Evolution of Managed Service Providers (MSPs) and Business Valuation The role of managed service providers (MSPs) has undergone a fundamental transformation over the last two decades. Starting as simple value-added resellers (VARs) focused on moving hardware, they evolved by adding services and, eventually, a software layer, creating new categories of service offerings. The key differentiator for success became the ability to capture recurring revenue, rather than relying on one-time sales. This transformation is critical because the central metric for valuing an MSP business today is its EBITDA (profit margin). Companies looking to be acquired for a reasonable multiple must establish strong fundamentals, focusing on growth and robust profit margins. Michelle Accardi, with her experience running roll-up MSP Logically, stresses that a healthy business is defined by its ability to generate revenue and maintain profitability. A crucial financial benchmark discussed for these service businesses is the “Rule of 40,” a metric commonly used in the SaaS world. This rule suggests that the sum of a company’s growth rate and its profit margin (EBITDA percentage) should roughly equal forty percent. Many MSPs, however, struggle to meet both growth and profitability goals simultaneously. To achieve this level of performance, MSPs must focus on driving profit through either growth (by adding new services) or improving efficiency (through cost-cutting/automation). Ultimately, businesses aiming for the highest returns should target an EBITDA margin of 15% to 20% combined with a growth engine of 15% to 20%. This strategic focus on financial health is crucial for achieving long-term success and a favorable business valuation. Driving efficiency is the cornerstone of a successful modern managed service provider (MSP) business. Outside of the Rule of 40 metric, core success attributes for an MSP include automation, a strong toolset, and the ability to leverage resources nearshore or offshore. However, the foundational element is having the right people in place who align with the core value proposition. Beyond operational efficiency, the most critical factor is the ability to monetize the technology stack. MSPs often experiment with new technology but fail to develop offerings around it to sell to their customer base. Every dollar spent on the tech stack must be viewed as an investment intended to generate more revenue and provide additional value to the end customer. This approach ensures that the business is not just technically capable, but fundamentally profitable and scalable for the future. ✔ Chapter 2: Cybersecurity, Asset Management, and AI Automation For growing managed service providers (MSPs), especially those with several hundred customers and a team of ten to twenty people, identifying areas for growth can be challenging in a brownfield environment where they must displace a competitor. Michelle Accardi suggests that the most critical first step, which sounds rudimentary, is for the MSP to understand what their customers truly have. This means taking a comprehensive inventory of all assets. From this inventory, MSPs can discover a wealth of information that informs them about what new, high-value services, particularly in security and IT automation, they should be selling. By understanding how a customer’s environment changes on a daily, monthly, or yearly basis, an MSP can help them rationalize their existing IT and security spending. This focus on a source of truth for assets, though unsexy, is where the real money is found in the services business. Liongard’s core offering aligns perfectly with this need for a source of truth, establishing itself as a cybersecurity SaaS platform. Their platform automates asset discovery, inventory, and monitoring of configuration changes to identify vulnerabilities and risks preemptively. The company targets MSPs and MSSPs with more than twenty customers, as complexity in asset inventory and risk management increases with customer count. A key feature is the use of AI to generate asset summaries for account managers, enabling them to discuss customer environments intelligently without requiring a technical background. By integrating with over 90 different IT systems, Liongard becomes a reliable, central source of truth for MSPs, enabling them to build their own automation on top, whether through RPA or agent-based AI. The discussion extends into the emerging concern of Shadow AI—users bringing their own unmanaged AI tools into the workplace, similar to the “bring your own device” trend. Managing these tools is complicated as they don’t fit into traditional hardware or human resource management systems. Michelle Accardi argues that the core focus for security shouldn’t be the AI tools themselves, but rather identities. Security must focus on identifying which identities have access to critical systems and underlying data and ensuring that access is properly tracked and controlled. Liongard is also integrating generative AI directly into its platform with the upcoming Answer IQ feature. This will enable partners to utilize natural language search to query the massive data lake for immediate insights, such as identifying which customers lack MFA-enabled accounts or determining which ports on a firewall pose a risk, thereby democratizing technical data for non-technical account managers. ✔ Chapter 3: AI in Service Delivery and the Future of Talent The immediate focus for managed service providers (MSPs) in adopting AI is two-fold: first, helping customers leverage available tools, such as Microsoft Copilot. Second, and more importantly for sophisticated MSPs, is utilizing AI internally to enhance their own service delivery and achieve efficiency. This internal automation, often achieved by mining data from a source of truth to identify new service offerings, must precede external services. Horizontal use cases, such as Copilot, are the current primary offerings, although some niche players are developing vertical-specific applications for industries like legal and hospitality. Ultimately, the goal is for MSPs to leverage AI to increase their efficiency before creating new bundled offers for their customers. A significant area of transformation is the use of AI agents to handle basic, high-volume customer requests. Instead of logging a traditional ticket, customers can use a self-service interface, such as a ChatGPT-like bot, to resolve simple problems and escalate to a human technician only when necessary. This shift is already evident in margin-constrained businesses, such as the hospitality and retail industries. For MSPs, this means the first line of defense—solving common, simple issues like Wi-Fi connectivity problems—can be automated. While this automation helps drive necessary profit margins, it also presents a risk to entry-level engineers. The changing landscape of service delivery has a direct impact on the talent pool. Historically, MSPs recruited frontline support from community colleges and trade schools. While new automation in the PSA (Professional Services Automation) industry created new categories and jobs in the past, the rise of AI agents means the path forward is complex. Michelle Accardi suggests a bifurcated path: some systems will utilize agentic AI to replace lower-skilled talent. In contrast, others will create new paradigms where talent focuses on roles such as training AI models. The consensus is that lower-skilled workers are most at risk. However, top-tier talent with critical thinking skills will remain indispensable for solving edge cases and complex problems that an AI model cannot efficiently address. The core skill set for future success encompasses not only technical knowledge but also curiosity, building a strong network, and understanding the economics of business.

    45 min
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About

ZINFI helps technology providers and their channel partners achieve profitable growth rapidly and affordably by automating Partner Relationship Management (PRM) processes globally.

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  • Creator
    ZINFI Technologies, Inc.
  • Years Active
    2015 - 2026
  • Episodes
    239
  • Rating
    Clean
  • Copyright
    © ZINFI Technologies, Inc.
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    ZINFI Technologies, Inc.