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  • AI in Operations: Comparing 2024 Predictions to 2025 Reality & 2026 Outlook

    AI in Operations: Comparing 2024 Predictions to 2025 Reality & 2026 Outlook

    March 2024 Predictions vs. 2025 Reality

    Way back in March 2024 I wrote an article outlining 10 key AI use cases for COOs and predicted a shift from experimentation to implementation. With over a year (an eternity in AI time) in the rearview mirror I was curious how close I was to reality.

    Here’s how those 2024 predictions align with current trends and where reality diverged:

    2024 Prediction

    2025 Reality

    		  

    Predictive Maintenance

    Widely adopted, with AI now enabling autonomous maintenance decisions via agentic AI (e.g., reducing equipment downtime by 30–40%).

    McKinsey

    Demand Forecasting

    Enhanced by gen AI’s ability to analyze unstructured data (social sentiment, weather) for hyper-accurate predictions.

    McKinsey

    Supply Chain Optimization

    AI now orchestrates end-to-end supply chains in real time, breaking silos between procurement, logistics, and production.

    EdgeVerve

    Fraud Detection

    Expanded to cybersecurity: 55% of COOs now use gen AI for real-time threat detection and automated response.

    PYMNTS

    Bonus: Real-Time Market Shifts

    Partially realized via AI agents that monitor global data streams, though adoption remains early-stage.

    Operations Council

    Strategic Business Proposals

    Emerging via multiagent AI systems that simulate scenarios and draft strategies (e.g., $30M revenue gain from AI-managed sales accounts).

    McKinsey

     

    Where Reality Diverged:

    1. Gen AI Dominance: 2024 focus on “traditional” AI (predictive analytics) underestimated the rapid rise of generative AI and agentic AI (autonomous systems that act on insights).
    2. Cybersecurity Focus: Gen AI’s role in data security (55% adoption) became a critical COO priority, not just fraud detection.
    3. Centralized Governance: COOs now lead AI centers of excellence (COEs) to align siloed teams, a trend not anticipated in 2024.

    With a pretty good track record lets give it a go for the future and one year from now we can revisit and see how we did.

    New Predictions for 2026–2027

    Based on 2025 trends and gaps, here’s where AI in operations is headed:

    1. Autonomous AI Agents Will Begin to Manage End-to-End Processes
      • AI “employees” will handle complex workflows (e.g., procurement negotiations, shift turnover handoffs) with minimal human oversight.
      • Example: Gen AI agents will resolve supply chain disruptions in real time by rerouting shipments, renegotiating contracts, and adjusting production schedules.
    2. Ethical AI Governance Becomes a Core COO Responsibility
      • COOs will oversee AI ethics frameworks to mitigate risks like bias in hiring algorithms or sustainability gaps in supply chains. This maybe in addition to CAIEO’s (see CAIEO article) responsibilities or taking over their responsibility.
    3. AI-Driven Upskilling Replaces Traditional Training
      • Personalized AI coaches will train teams on new tools/processes in real time, reducing onboarding time by 50%.
    4. Cybersecurity AI Expands to “Zero Trust” Operations
      • Gen AI will autonomously enforce zero-trust policies, isolating threats before they breach systems.
    5. COO-CIO Collaboration Defines AI ROI
      • Success will hinge on tight integration between COOs (process owners) and CIOs (tech enablers), with shared KPIs for AI-driven efficiency gains. New roles such as CAIO will also evolve with AI Legal roles – protecting the data each business creates via its artificial intelligence work.  

    How did I do?

    My 2024 vision of AI streamlining operations has proven accurate, but the tools have evolved even faster than I predicted (See AI Time) .

    Gen AI and agentic systems are now rewriting playbooks, with COOs leading enterprise-wide reinvention / applications. The next phase will focus on autonomy (AI agents), ethics, and human-AI symbiosis.

    For COOs, I believe the mandate has become clear:

    • Partner with CIOs, CAIO, CHRO
    • Govern AI centrally – Ensure Data’s use and storage is crystal clear
    • Prepare teams for a future where AI isn’t just a tool-it’s a colleague
    • Prepare with legal team parameters and acceptable uses of AI within the corporate framework and put guardrails on non-authorized use within enterprise platform.

    What do you think? How will COO roles change in 2026 and beyond? What have I missed? Please comment below.

    #AIinOperations #COO #GenAI #FutureOfWork #DigitalTransformation

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  • The Future of CRM in Salesforce: Which Fields Will Disappear due to AI Features?

    The Future of CRM in Salesforce: Which Fields Will Disappear due to AI Features?

    Prediction: In just 12 months, many of the fields you see in Salesforce today will no longer exist. Sounds bold?

    Let’s break down why this radical shift is inevitable.

    The Problem: Too Many Fields, Not Enough Value

    Today, if you look at your Salesforce Opportunity object, you’ll likely find more than 100 fields. Yet most of these are rarely filled out by sales teams, and even when they are, the data often can’t be trusted.

    Reps enter inconsistent or inaccurate information-sometimes just to bypass validation rules. And every time a new field is added, it’s irrelevant for older records, further muddying your data.  This patchwork of unreliable fields leads to a CRM that’s cluttered, hard to use, and ultimately, not trusted by the very people it’s meant to help.

    Opportunity: Have reps use the exact same CRO approved sales process every time with mandatory fields. Gamify the process and have some fun with it. Better yet let Sentia AI enter the right fields for the rep with a single click new contact entry.

    The Shift: From Manual Fields to AI-Powered Intelligence

    So why do we keep trying to force reps to fill out fields that don’t add value? The answer is: we won’t for much longer.

    The future of CRM in Salesforce is about keeping only the truly objective fields-focus will be on the essentials like:

    • Name
    • Amount
    • Close Date
    • Stage
    • Forecast Category
    • A handful of other critical fields for reporting

    Everything else that is subjective, context-rich, data-will move to AI.

    Instead of relying on reps to manually update competitor fields, next steps, or deal risks, AI will capture and analyze unstructured data: calls, emails, meeting notes, and more. You’ll simply query Salesforce in plain language, and AI will surface the insights you need.

    Example:
    Want to know which deals involve a specific competitor?

    Ask your future Salesforce system, and AI will scan all relevant communications and notes to give you a precise answer. Wondering which opportunities lack a clear next step? AI will tell you instantly-no more chasing down reps or sifting through incomplete fields.

    Why is Salesforce Headed in this Direction?

    AI in Salesforce is evolving rapidly. New agentic AI capabilities, like Salesforce’s Atlas Reasoning Engine and Einstein Copilot, are already automating data capture, driving predictive insights, and guiding sales teams with real-time recommendations. By 2027, AI will be so deeply embedded that most manual data entry will be obsolete.

    • AI-driven pipeline management: Real-time deal insights and risk detection
    • Automated lead scoring and follow-ups: AI prioritizes and nudges opportunities
    • Conversational AI assistants: Guide users and automate repetitive tasks
    • Hyper-personalization: AI tailors outreach and engagement at scale

    What This Means for Sales Teams

    • Far better data: AI captures context and nuance humans miss, making CRM data more accurate and actionable.
    • Less admin, more selling: Reps spend less time updating fields and more time building relationships.
    • New possibilities: Hundreds of use cases open up, from dynamic forecasting to real-time coaching and customer insights.

    In short, the CRM of the future will be lighter, smarter, and more powerful-and sales teams will finally be free from the tyranny of endless unused / useless fields.

    Salesforce Future

    A year from now-updating Salesforce with AI won’t be necessary because there will be almost nothing left to update. The future of CRM is objective data at the core, with AI doing the heavy lifting everywhere else.

    Are you ready for a Salesforce where fields are few, but insights are limitless? The countdown has already begun. www.sentia.website

    #Salesforce #CRM #AIinSales #FutureOfWork #SalesTech #AIAutomation

    Frequently Asked Questions: The Future of Salesforce Fields & AI

    Why are Salesforce fields predicted to disappear?

    Most Salesforce fields will disappear because AI now automates data capture. Traditionally, sales teams had to manually enter data into over 100 fields per Opportunity, leading to “dirty data” and administrative fatigue. With the rise of Salesforce Agentforce and autonomous AI, CRM data is captured directly from emails, calls, and meetings, making manual fields redundant.

    Which types of Salesforce fields will be removed first?

    The first fields to disappear are manual entry tracking fields, such as:

    • Next Steps: Automatically derived from meeting transcripts.

    • Discovery Notes: Captured via AI-powered sentiment and keyword analysis.

    • Competitor Tracking: Identified through natural language processing (NLP) of sales calls.

    • BANT Criteria: (Budget, Authority, Need, Timeline) automatically validated by AI interactions.

    What is Salesforce Agentforce’s role in CRM evolution?

    Agentforce represents a shift from passive CRM systems to active, decision-making agents. It acts as an autonomous data steward that enriches CRM records in real-time without human intervention, allowing sales reps to focus on customer relationships rather than data entry.

    How does AI improve CRM data quality?

    AI improves data quality by eliminating human error and “empty field syndrome.” Instead of relying on a busy sales rep to update a record, AI captures 100% of interaction data immediately. This provides RevOps teams with a “single source of truth” that is consistently updated and accurately reflects the state of the pipeline.

    Will Salesforce become a “field-less” CRM?

    While Salesforce won’t lose all fields, it is moving toward a “Headless CRM” or “Invisible CRM” experience. The user interface will shift from dense data tables to AI-generated summaries and actionable insights, where the underlying data exists but doesn’t require manual management.

    How should RevOps teams prepare for disappearing CRM fields?

    RevOps teams should begin by auditing their current Salesforce objects and identifying fields with low completion rates. Preparing for this shift involves moving away from rigid manual validation rules and instead investing in AI-driven data orchestration and clean data architectures.

  • 7-Step Checklist to Develop a New Sales Pipeline

    7-Step Checklist to Develop a New Sales Pipeline

  • 90-Day SEO Plan to Rank your Website in Top 10 Niche Rankings

    90-Day SEO Plan to Rank your Website in Top 10 Niche Rankings

  • If Your Sales Pipeline Data Isn’t Accurate, Your Forecasts, Strategies, and Growth Plans Are Built on Vapor

    If Your Sales Pipeline Data Isn’t Accurate, Your Forecasts, Strategies, and Growth Plans Are Built on Vapor

    We’ve all seen it: the pipeline review that reveals more fiction than fact.
    ❌ Opportunities that should have been closed months ago
    ❌ Close dates long passed
    ❌ Values wildly overstated or understated
    ❌ Stages that don’t match reality
    ❌ Fields that are blank or outdated

    The result? Leaders are building forecasts, strategies, and growth plans on unstable ground. And when the foundation is shaky, everything above it is at risk of collapse.

    The High Cost of Bad Data

    Bad pipeline data isn’t just an internal headache-it’s a business-wide domino effect. When your sales forecast is built on bad data, every department feels the impact:

    • Finance may misjudge budgets or cash flow, risking over- or under-investment.
    • Operations might over hire or understaff, leading to inefficiencies.
    • Marketing could double down on campaigns that don’t actually drive high-value opportunities.

    Even small data inconsistencies can have massive consequences. If just 10% of your opportunities are missing amounts, and the average deal is $100,000, a pipeline of 1,000 deals could be undervalued by $10 million. That’s not a rounding error-it’s a gap that can swallow your quota whole.

    But perhaps worst of all, bad data erodes trust. When forecasts miss the mark quarter after quarter, stakeholders lose faith in sales leadership, making it harder to secure resources, align teams, or drive change.

    Why Data Integrity Is So Hard

    Let’s be honest: no salesperson dreams of updating CRM fields. Their passion-and their value-comes from being in front of customers, not wrestling with dropdown menus and mandatory fields. Expecting meticulous data entry as a natural behavior is unrealistic.

    Data integrity in sales is not a default setting; it’s a discipline. Without leadership and process, pipeline data will always decay.

    Lead the Way: Building a Culture of Data Integrity

    If you want reliable data, you have to lead it. Here’s how:

    1. Frequent, Structured Pipeline Reviews

    Don’t settle for monthly, surface-level check-ins. Hold structured, opportunity-by-opportunity reviews at least every fortnight. Ask tough questions:

    • Is the opportunity real?
    • Is the close date credible?
    • Is the value accurate?
    • Is it in the right stage?
    • What does the salesperson need to move it forward?
    1. Evidence-Based Accountability

    Push reps for evidence, not just opinions. “I think we’re moving forward” isn’t enough-ask for proof, like stakeholder feedback or documented next steps.

    1. Regular Data Cleanups

    Schedule routine audits to remove dead leads, update outdated info, and validate key fields. Automation can help, but human review is essential for context and accuracy.

    1. Embrace Data Governance

    Establish clear policies, assign data stewards, and provide ongoing training. Make data quality everyone’s responsibility, not just sales ops.

    The Payoff: Forecasts You Can Trust

    When you build a culture of data integrity, your forecasts become credible, your strategies become actionable, and your growth plans rest on a foundation as solid as bedrock-not sand.

    What you focus on grows. If you focus on data discipline, you’ll see not just better numbers, but better outcomes across your business.

    Are you ready to stop building on Vapor? Share your experiences and tips for pipeline data integrity in the comments below!

    #SalesLeadership #PipelineManagement #DataIntegrity #SalesForecasting #CRM #SalesOps

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  • JP Morgan Chase’s Open Letter: A Wake-Up Call for AI and SaaS Security

    JP Morgan Chase’s Open Letter: A Wake-Up Call for AI and SaaS Security

    In March 2025, JPMorgan Chase issued a stark warning to its third-party suppliers: the rapid adoption of Software as a Service (SaaS) and Artificial Intelligence (AI) is quietly enabling cyber attackers and creating vulnerabilities that threaten the global economic system. For companies not taking AI security seriously, this open letter is a clarion call-one that cannot be ignored.

    The SaaS Security Paradox: Efficiency vs. Vulnerability

    SaaS has become the default, and often the only, format for software delivery. This shift brings undeniable efficiency and innovation, but it also embeds concentration risk into global infrastructure. Organizations now rely heavily on a small set of leading providers, creating single points of failure. A breach or outage at one major SaaS or PaaS provider can instantly ripple through thousands of customers, amplifying the impact of any weakness and exposing the entire ecosystem to catastrophic consequences.

    Historically, software diversity and segmented security practices limited the scale of any single breach. Today, interconnectedness means an attack on one trusted integration partner can compromise entire supply chains.

    AI: Accelerating the Risk Landscape

    The integration of AI into SaaS platforms has supercharged these risks. AI-driven features-while transformative-introduce new attack surfaces:

    • Shadow AI: Unmonitored AI tools embedded in SaaS can operate outside the purview of security teams, exposing sensitive data without oversight.
    • Adversarial Attacks & Model Poisoning: Attackers can manipulate AI models or data inputs, leading to incorrect or harmful outputs, especially when AI is used for critical tasks like fraud detection or security monitoring.
    • Authentication Weaknesses: Modern integration patterns often collapse authentication and authorization into overly simplified interactions, creating explicit trust relationships between external SaaS and internal systems-effectively a single-factor trust model that attackers can exploit.
    • Data Sovereignty & Privacy: AI platforms, especially those operating across borders, can introduce regulatory and privacy risks, as seen with recent scrutiny of GenAI platforms like DeepSeek.

    Why Security Must Trump Speed

    JPMorgan Chase’s letter highlights a dangerous trend: the race for market share and rapid feature development often comes at the expense of robust security. Rushed releases without comprehensive, default-enabled security controls create repeated opportunities for attackers. This is unsustainable for the global economic system and exposes entire customer ecosystems to systemic risk.

    Modernizing Security Architecture: A Collective Responsibility

    Traditional security boundaries-such as network segmentation, protocol termination, and tiered access-are being eroded by modern SaaS and AI integration patterns. Today’s architectures often rely on identity protocols (like OAuth) and direct API integrations, which can collapse decades of security best practices into a single point of failure.

    To address these challenges, organizations must:

    • Prioritize Security Over Features: Security must be built-in and enabled by default, not bolted on as an afterthought.
    • Implement Robust IAM and Zero Trust: Strong identity and access management, multi-factor authentication, and zero-trust principles are essential.
    • Continuously Monitor and Test: Automated security testing, continuous monitoring, and anomaly detection are critical for both SaaS and AI environments.
    • Govern AI Models: Regular auditing, validation, and explainability are necessary to ensure AI systems are trustworthy and compliant.
    • Encrypt Data and Enforce Privacy: Data must be encrypted in transit and at rest, with privacy controls embedded throughout the application lifecycle.
    • Reject Weak Integration Models: Organizations should not accept integration patterns that undermine security principles. Where possible, demand solutions like confidential computing, customer self-hosting, or “bring your own cloud” to maintain control over sensitive data.

    The Call to Action: Secure by Design, Not by Slogan

    JPMorgan Chase’s Chief Information Security Officer, Patrick Opet, urges providers and customers alike to move beyond compliance checklists and marketing slogans. Security must be demonstrable, continuous, and transparent. The ecosystem must adopt new security principles and controls that enable swift adoption of cloud and AI services-without exposing customers to provider vulnerabilities.

    “The most effective way to begin change is to reject these integration models without better solutions. I hope you’ll join me in recognizing this challenge and responding decisively, collaboratively, and immediately.”

    • Patrick Opet, Chief Information Security Officer, JPMorganChase1

    Final Thoughts

    The message is clear: AI and SaaS have redefined the technology landscape, but without a fundamental shift in security priorities, they risk becoming vectors for systemic failure. Every organization-whether a provider or a customer-must act now to modernize security, demand transparency, and build resilience into every layer of their technology stack.

    The time to take AI security seriously is now. The stakes are nothing less than the stability of the global economic system.

    For more details, see JPMorgan Chase’s full open letter and recent industry analysis on SaaS and AI security risks and strategies.

    Citations:

    1. https://www.jpmorgan.com/technology/technology-blog/open-letter-to-our-suppliers
    2. https://softwareanalyst.substack.com/p/securing-aillms-in-2025-a-practical
    3. https://www.sentinelone.com/cybersecurity-101/cloud-security/saas-security-risks/
    4. https://perception-point.io/guides/ai-security/ai-security-risks-frameworks-and-best-practices/
    5. https://www.linkedin.com/pulse/saas-ai-security-challenges-strategies-in-depth-roy–uhqqf
    6. https://wing.security/blog/saas-security/five-ai-security-threats-in-saas/
    7. https://www.valencesecurity.com/resources/blogs/mitigating-genai-risks-in-saas-applications
    8. https://www.exabeam.com/explainers/information-security/software-supply-chain-attacks-attack-vectors-examples-and-6-defensive-measures/
    9. https://www.cybersecuritydive.com/news/jpmorgan-chase-ciso–software-supply-chain-security/746476/
    10. https://lumenalta.com/insights/ai-security-checklist-updated-2025
    11. https://www.zscaler.com/zpedia/what-is-saas-security
    12. https://www.databricks.com/blog/introducing-databricks-ai-security-framework-dasf
    13. https://www.scworld.com/feature/data-breaches-impact-of-ai-and-insider-risk-top-the-list-of-saas-security-concerns
    14. https://www.polymerhq.io/blog/breach/top-saas-breaches-of-2022/
    15. https://www.linkedin.com/posts/jorgebestard_jpmorganchase-just-released-an-open-letter-activity-7322607647596199937-abRx
    16. https://www.forcepoint.com/blog/insights/8-saas-security-risks
    17. https://securitybrief.co.uk/story/experts-warn-of-ai-driven-threats-to-saas-applications
    18. https://www.linkedin.com/posts/harishperi_an-open-letter-to-third-party-suppliers-activity-7322405313226117120-yCXu
    19. https://www.infisign.ai/blog/ai-in-saas-security
    20. https://www.valencesecurity.com/resources/blogs/2024-saas-security-breaches-lessons-learned
    21. https://www.jpmorganchase.com/content/dam/jpmc/jpmorgan-chase-and-co/documents/Supplier%20Incident%20Response%20Procedure%20-%20Best%20Practice%20Recommendations.pdf
    22. https://www.linkedin.com/posts/katheryn-rosen-2482b2122_an-open-letter-to-third-party-suppliers-activity-7322596505909932032-85ih
    23. https://www.jpmorganchase.com
    24. https://thehackernews.com/2025/04/ai-adoption-in-enterprise-breaking.html
    25. https://www.jpmorganchase.com/ir/annual-report/2024/ar-ceo-letters
    26. https://www.cybersecuritydive.com
    27. https://www.bankinfosecurity.com/jpmorgan-chase-develops-early-warning-system-a-12855
    28. https://www.bloomberg.com/news/articles/2025-04-14/jpmorgan-bny-limit-information-sharing-with-occ-after-hack
    29. https://news.ycombinator.com/item?id=43815673
    30. https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-efficiency-fraud-reduction
    31. https://blackcloak.io/j-p-morgan-chase-fbcs-experience-data-breaches/
    32. https://www.reco.ai/blog/jpmorgan-chase-ciso-names-saas-security-as-top-priority-heres-why
    33. https://www.sisainfosec.com/wp-content/uploads/2025/04/blog-10-cybersecurity-best-practices-in-the-age-of-ai-2025.webp?sa=X&ved=2ahUKEwjjyobRi_yMAxUnMNAFHd9_HAkQ_B16BAgIEAI
    34. https://www.getguru.com/id/reference/ai-security
    35. https://cloudsecurityguy.substack.com/p/a-step-by-step-plan-to-master-ai
    36. https://www.securityjourney.com/post/navigating-the-ai-threat-landscape-in-2025
    37. https://exchangesavvy.com/the-role-of-ai-in-saas-security-benefits-risks/
    38. https://blogs.cisco.com/security/cisco-introduces-the-state-of-ai-security-report-for-2025
    39. https://secureframe.com/blog/ai-frameworks
    40. https://wing.security/blog/saas-security/five-ai-security-threats-in-saas/
    41. https://www.privasee.io/fr/post/ai-security-best-practices
    42. https://www.valencesecurity.com/saas-security-terms/what-are-saas-attacks
    43. https://blog.qualys.com/product-tech/2025/02/07/must-have-ai-security-policies-for-enterprises-a-detailed-guide
    44. https://www.valencesecurity.com/resources/blogs/mitigating-genai-risks-in-saas-applications
    45. https://thehackernews.com/2025/03/ai-powered-saas-security-keeping-pace.html
    46. https://outshift.cisco.com/blog/top-10-supply-chain-attacks
    47. https://www.businesswire.com/news/home/20250128519268/en/Cloud-Security-Alliance-Issues-SaaS-AI-Risk-for-Mid-Market-Organizations-Survey-Report
    48. https://virtualizationreview.com/Articles/2024/11/21/Case-Studies-of-Real-World-SaaS-Ransomware-Attacks.aspx
    49. https://www.csoonline.com/article/3846304/ai-development-pipeline-attacks-expand-cisos-software-supply-chain-risk.html
    50. https://cloudsecurityalliance.org/blog/2024/03/26/5-security-questions-to-ask-about-ai-powered-saas-applications
    51. https://www.metomic.io/saas-breach-database
    52. https://www.zscaler.com/cxorevolutionaries/insights/ai-software-supply-chain-risks-prompt-new-corporate-diligence
    53. https://perception-point.io/guides/ai-security/ai-in-cybersecurity-examples-use-cases/
    54. https://www.ibm.com/think/insights/cyber-criminals-compromising-ai-software-supply-chains
    55. https://www.arcserve.com/blog/7-most-infamous-cloud-security-breaches
    56. https://www.jpmorgan.com/technology/technology-blog
    57. https://www.linkedin.com/posts/bartvandekerckhove_ai-governance-and-ai-security-are-high-on-activity-7322657453257469952-YVrf
    58. https://www.strongdm.com/what-is/chase-bank-data-breach
    59. https://home.treasury.gov/system/files/136/Managing-Artificial-Intelligence-Specific-Cybersecurity-Risks-In-The-Financial-Services-Sector.pdf

    JP Morgan Chase’s Open Letter: A Wake-Up Call for AI and SaaS Security

  • Stop Losing Leads: Fixing GTM Inefficiencies in Lead Management

    Stop Losing Leads: Fixing GTM Inefficiencies in Lead Management

  • Courageous Marketing: The B2B Marketer’s Playbook for Career Success

    Courageous Marketing: The B2B Marketer’s Playbook for Career Success

    Book Review & Synopsis

    Udi Ledergor’s Courageous Marketing is a transformative guide for B2B marketers seeking to break free from conventional, uninspired strategies. Drawing on over two decades of experience, including his role as CMO at Gong, Ledergor advocates for boldness and creativity as essential tools for driving business success. The book challenges the prevailing “play-it-safe” mentality in B2B marketing, arguing that taking calculated risks and embracing originality are critical for standing out in a crowded marketplace. Released on April 10, 2025, it serves as both a manifesto and a practical playbook for marketers aiming to make a lasting impact.

    Through vivid examples—such as Gong’s innovative Super Bowl-adjacent commercial stunt—Ledergor demonstrates how unconventional approaches can deliver outsized results even on limited budgets. The book is structured around actionable strategies designed to empower marketers to craft compelling content, leverage guerrilla marketing tactics, create memorable events, and establish thought leadership.

    Key Points from the Book

    Core Themes

    • Boldness as a Strategy: Playing it safe is portrayed as the riskiest approach in today’s competitive marketing landscape.

    • Creativity Over Conformity: The book emphasizes the importance of originality and human creativity, especially in an era dominated by AI-generated content.

    • Marketing with Impact: Campaigns should not only be creative but also drive tangible business results.

    Actionable Strategies

    1. Craft Compelling Content: Develop material so engaging that audiences would pay for it.

    2. Leverage Guerrilla Marketing: Use unconventional tactics to amplify brand presence without large budgets.

    3. Establish Product Categories: Position offerings strategically to dominate new market segments.

    4. Design Memorable Events: Create experiences that resonate deeply with attendees and foster brand loyalty.

    5. Champion Industry Issues: Take bold stands on pivotal topics to establish thought leadership.

    6. Advance Careers and Teams: Cultivate personal growth and build high-performing teams through intentional strategies.

    Why It Matters Now

    • With AI flooding the market with generic content, human creativity has become a key differentiator.

    • The book provides a roadmap for marketers to navigate this landscape by embracing audacity and innovation.

    Endorsements

    • Industry leaders like Neil Patel, Robert Cialdini, and Daniel H. Pink praise the book for its practical advice and bold vision.

    • Michelle Taite (CMO of Intuit Mailchimp) highlights its transformative potential for marketers tired of conventional approaches.

    Real-World Examples

    • Gong’s viral marketing stunts, such as cost-effective yet impactful Super Bowl-related campaigns, illustrate how bold ideas can succeed without excessive spending.

    Availability

    Courageous Marketing is available through major retailers like Amazon and Barnes & Noble. Udi Ledergor will also be speaking at upcoming events, including Canva Create (Los Angeles), Pavilion CMO Summit (Atlanta), and The Brand Tech Summit (Tel Aviv).

    This book is not just a guide—it’s a call to action for marketers seeking to challenge the status quo and redefine what’s possible in B2B marketing.

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  • Transforming In-Store Execution with AI: Introducing the Sentia Smart Layer

    Transforming In-Store Execution with AI: Introducing the Sentia Smart Layer

    Transforming In-Store Execution with AI: Introducing the Sentia Smart Layer

    Sentia’s AI seers here to give you the latest in the CPG wars where margins are tighter than a.. well you know and the slightest edge is fought for like the beaches of Normandy. Fear not faithful CPG warrior – we have the scoop on what’s shaking in, in store merchandising and what you need to know you’re competitors are already doing and why your numbers are dropping faster than tariff threats.

    Let’s get back to basics. In today’s hyper charged consumer goods industry, staying ahead in retail execution is not a option it’s win or bust.

    Enter Sentia Smart Layer, a groundbreaking AI-powered platform designed to revolutionize how field teams and visual merchandisers operate. With cutting-edge AI enhanced technologies, Sentia transforms images and voice inputs into actionable insights in real-time, empowering teams to deliver unparalleled in-store performance.

    Your Question?

    “There is a bit of AI marketing “fluffier” around AI App capabilities. What makes Sentia AI different from all these AI VC backed startups that claim to uncover every target, upsell, cross sell, website pixel generated lead that was never possible before Alexander Grahm Bell and the telephone.” – Frustrated not willing to go on record CEO (4X in past 5 days).  But as Sentia says, I digress.

    Why are these CEO, COO, CFOs feeling so much frustration when their CRMs can be enhanced with AI?

    What real world problems can it solve – that I will be willing to pay for?   

    Why Sentia Smart Layer is a Game-Changer

    Here’s how Sentia is redefining retail execution:

    1. Effortless Field Reporting
      Say goodbye to tedious typing! Field teams can now create detailed reports simply by speaking. Sentia’s AI extracts insights from photos of shelves, fixtures, and displays, enabling real-time data capture and reducing administrative burdens.
    2. In-Store Visual Analysis
      Sentia analyzes store images to ensure planogram compliance, measure stock levels, assess POS material quality, and validate shelf allocations—all while suggesting layout improvements to optimize shopper flow and visibility.
    3. Competitive Intelligence
      Beyond monitoring your own products, Sentia tracks competitor share of shelf, promotional displays, and stock levels—providing visual benchmarking to keep you ahead of the competition.
    4. Real-Time Prioritization
      Incoming reports are scored for urgency and relevance, surfacing critical issues like compliance gaps or stock shortages as actionable Smart Cards. Teams can act fast to fix problems before they escalate.
    5. Continuous Monitoring
      Every submitted image is vetted for out-of-stocks, non-compliant POS materials, competitor intrusions, and execution quality—ensuring proactive alerts for rapid resolution.
    6. On-Demand Summaries
      Get concise visual summaries by store, region, or retailer with execution scores, competitor trends, and actionable recommendations—all at your fingertips.
    7. Natural Language Chat Interface
      Just ask questions like “What stores have execution issues this week?” or “Summarize merchandising problems in Region Y,” and Sentia delivers instant answers through conversational AI.

    Why It Matters

    The Sentia Smart Layer empowers brands with unparalleled control and visibility across thousands of retail locations—without relying on outdated manual reporting or delayed audits. By enabling faster decision-making and smarter execution strategies:

    • Teams act swiftly on opportunities to improve share at the shelf.
    • Brands gain deeper insights into competitor activities and trends.
    • Execution quality reaches new heights with real-time monitoring and actionable insights.

    Key Features That Set Sentia Apart

    • Trend Detection: Spot long-term patterns in competitor movements or display quality.
    • Predictive Alerts: Receive warnings about potential stock-outs or compliance gaps before they happen.
    • Integration-Ready: Sync seamlessly with retail apps, CRMs, and BI dashboards for streamlined workflows.
    • Gamification: Motivate field teams with scorecards and leaderboards to drive performance excellence.

    Revolutionizing Retail Execution Starts Here

    The Sentia Smart Layer isn’t just an innovation—it’s a transformation of how consumer goods brands approach in-store execution. From empowering field teams to delivering competitive intelligence at scale, this platform is the ultimate tool for winning at retail.

    Are you ready to elevate your brand’s in-store presence? Let Sentia lead the way into the future of smarter retail execution!

    Keywords for Visibility:

    #AI #RetailExecution #ConsumerGoods #VisualMerchandising #PlanogramCompliance #CompetitiveIntelligence #SmartTechnology #FieldTeams #RetailInnovation #SentiaSmartLayer

     

    Add Your Heading Text Here

  • How do Field Sales Reps Gain an Edge with AI? 10 AI Features to Know

    How do Field Sales Reps Gain an Edge with AI? 10 AI Features to Know

    AI in Field Sales: Current Impact and Future Potential

    Field sales reps have both the coolest (on the road!) and most stressful jobs out there. Sales in general can be an extremely monetarily rewarding field yet also has equally high job churn rates. Salespeople by nature are competive, always looking for an edge and they were some of the first to truly embrace AI and quickly leverage it.

    Let’s start with the statistics. Today’s field sales representatives spend approximately 65% of their time on non-selling activities—a ratio that AI technologies are now flipping by handling administrative burdens and enabling reps to focus on relationship building and closing deals1. Organizations implementing AI-powered sales tools are experiencing remarkable results: leads increasing by over 50%, call times reducing by 60-70%, and operational costs decreasing by 40-60%3. Lots of opportunities to apply AI – But how, who and which tools are delivering?

    Today’s Top 10 AI Features for Field Sales Representatives

    1. AI Meeting Note-Taking and Transcription

    Use Case: Field sales representatives can focus entirely on customer conversations while AI automatically captures, transcribes, and summarizes meetings.
    Example: Fireflies.ai integrates with video conferencing platforms to record, transcribe, and summarize customer interactions, then syncs these notes directly to your CRM2.

    1. AI-Driven Conversation Intelligence

    Use Case: Reps receive real-time feedback and coaching on their sales conversations based on patterns from top performers.
    Example: Gong.io analyzes sales calls to provide actionable insights and suggestions for improvement, such as optimal question patterns for discovery calls2.

    3. Prospect Personality Assessment

    Use Case: Before making contact, sales representatives gain insights into a prospect’s communication style and preferences.
    Example: Crystal uses compliant customer data to predict a prospect’s personality using the DISC methodology, then provides communication recommendations tailored to that individual2.

    4. AI-Powered Sales Forecasting

    Use Case: Field sales teams leverage predictive analytics for more accurate territory planning and goal setting.
    Example: InsightSquared offers 350 pre-built reports and identifies data gaps affecting forecast accuracy, helping reps better predict monthly bookings and opportunity values2.

    5. Automated Meeting Preparation

    Use Case: Reps receive comprehensive pre-meeting intelligence without manual research.
    Example: Cirrus Insight’s Meeting AI automatically compiles company and contact information ahead of scheduled meetings, eliminating hours of preparation time2.

    6. Territory Management and Route Optimization

    Use Case: AI optimizes travel routes between customer locations, maximizing productive selling time.
    Example: Field sales organizations using AI for territory management report 20-30% increases in productivity by optimizing customer visit sequencing1.

    7. Lead Prioritization and Scoring

    Use Case: Representatives focus on prospects most likely to convert based on AI analysis of customer behavior patterns.
    Example: AI systems analyze website visitor behavior and can send automatic “trigger reports” when high-quality leads are identified3.

    8. AI-Enhanced CRM Assistance

    Use Case: Sales reps receive guided selling recommendations throughout the sales process.
    Example: Salesforce Sales Cloud’s Einstein Copilot leverages customer data to guide sellers, generate personalized emails, and suggest actions most likely to move deals forward2.

    9. Automated Follow-up Management

    Use Case: AI ensures consistent post-meeting follow-up by automatically generating and scheduling communications.
    Example: AI SDR agents like Artisan AI’s Ava handle email outreach by drafting responses and automating follow-ups to streamline sales activities3.

    10. Real-time Competitive and Product Information

    Use Case: Field reps instantly access relevant product specifications and competitive information during customer conversations.
    Example: In real estate sales, AI agents process MLS listings, tax records, school ratings, and neighborhood information in real-time during client interactions1.

    Top 5 AI Features Coming in the Next Six Months

    1. Advanced AI Sales Development Representatives

    Expected Impact: Complete automation of early-stage sales processes, with AI agents handling lead generation, qualification, and initial outreach across multiple channels (email, voice, social) with increasingly human-like interaction capabilities3.

    2. Real-time Conversation Coaching

    Expected Impact: AI will provide live, in-ear guidance during customer conversations, suggesting responses to objections, identifying upsell opportunities, and recommending optimal negotiation strategies based on real-time conversation analysis2.

    3. Predictive Opportunity Management

    Expected Impact: AI systems will forecast not just overall sales but individual opportunity outcomes with greater precision, recommending specific actions to improve close probability for each deal in a rep’s pipeline2.

    4. Cross-platform Customer Intelligence

    Expected Impact: AI will aggregate and analyze customer signals across disparate platforms (social media, website visits, email engagement, purchase history) to create comprehensive engagement profiles accessible to field reps in real-time3.

    5. Industry-Specific AI Sales Assistants

    Expected Impact: Rather than generic AI tools, field sales representatives will have access to specialized AI assistants trained on industry-specific knowledge, regulations, and selling patterns, particularly in complex sectors like healthcare, financial services, and manufacturing1.

    Caution: When AI Over-Promises and Under-Delivers

    Field sales leaders should be cautious about several common pitfalls when adopting AI technologies:

    Integration Challenges

    Many AI tools promise seamless integration with existing systems but require significant customization and technical resources to deliver value. Before implementing new solutions, evaluate whether they truly connect with your existing tech stack without creating data silos1.

    Training Requirements

    Some AI systems require extensive training with your specific sales data before providing accurate recommendations. Be realistic about the volume of quality data required and the time needed before seeing meaningful results3.

    Human Oversight Needs

    While automation is valuable, tools that promise to eliminate human involvement entirely often create new forms of work in verification and exception handling. The most effective AI implementations augment rather than replace human expertise1.

    Cost vs. Value Assessment

    Sophisticated AI tools often come with premium pricing. Carefully evaluate whether the promised efficiency gains justify the investment, particularly for smaller sales teams or those with limited technology budgets2.

    Security and Compliance Concerns

    AI tools that access customer data or record conversations may create privacy vulnerabilities or compliance risks in regulated industries. Thoroughly vet vendors’ security practices and compliance certifications3.

    Field Sales Tech Stack Assessment Checklist

    Capability Current Tech Stack AI-Enhanced Potential
    Meeting Documentation Manual note-taking, inconsistent CRM updates Automated transcription, AI-generated summaries, CRM integration without manual entry
    Lead Prioritization Intuition-based or simple scoring models Behavioral analysis, predictive conversion modeling, real-time engagement tracking
    Territory Management Static assignments, manual routing Dynamic territory optimization, predictive customer value mapping, automated route planning
    Customer Intelligence Limited to CRM data, manually researched Real-time aggregation across platforms, personality insights, competitive intelligence
    Conversation Effectiveness Limited coaching, periodic reviews Real-time guidance, pattern recognition from top performers, automated improvement suggestions
    Follow-up Management Manual tracking, template-based Automated, personalized follow-ups with optimal timing recommendations
    Sales Forecasting Experience-based, often inaccurate AI-driven predictions with specific deal-level insights and improvement recommendations
    Competitive Intelligence Static battlecards, limited updates Real-time market intelligence, personalized competitive positioning suggestions
    Product Knowledge Manual research, limited accessibility Instant access to relevant specifications, AI-suggested positioning based on customer needs
    Performance Analytics Lagging indicators, manual analysis Real-time performance tracking, AI-recommended improvement areas, predictive coaching

    What’s Coming Next?

    Despite the hype we’re on the fringes of AI in business. As is typical in most tech cycles the marketing and sales drumbeat is louder than the actual successful implementations to date. I’m predicting that 2025 will be the year that AI starts taking hold and becomes accountable for real ROI just like any tech tool. That will force the pretenders out and the rest focus on real world solutions that have a clear and demonstratable impact.

    With that as a base there is no doubt that AI can fundamentally transform field sales by addressing core inefficiencies in the traditional selling model and field store visit teams. The most successful implementations to date pair AI capabilities with human expertise (“Functional AI) rather than attempting to replace it. As these technologies continue to evolve over the next six to nine months, field sales organizations that strategically evaluate and integrate AI tools while maintaining focus on relationship building will create sustainable competitive advantages.

    The key to successful AI adoption is to treat like any other new technology. Do this by starting small with focused use cases, to known easy to see problems that can deliver immediate value, then gradually expanding to more complex processes as teams build confidence in their digital teammates. If your analysis shows only incremental improvements (less than 20% ROI) look for bigger impact opportunities.

    Citations:

    1. https://relevanceai.com/agent-templates-roles/field-sales-representative-ai-agents
    2. https://www.cognism.com/blog/ai-sales-tools
    3. https://research.aimultiple.com/sales-ai/
    4. https://goconsensus.com/blog/best-ai-sales-tools/
    5. https://relevanceai.com/agent-templates-roles/sales-forecasting-analyst-ai-agents
    6. https://www.leadbeam.ai/blog/how-automation-ai-agents-ai-sdrs-impact-field-sales
    7. https://makesocialmediasell.com/ai-in-sales-examples/
    8. https://www.mailmodo.com/guides/ai-tools-for-sales/
    9. https://www.copy.ai/blog/field-sales-representatives
    10. https://www.close.com/blog/ai-sales-tools
    11. https://www.stackmoxie.com/blog/ai-readiness-checklist-for-marketing-ops-revops/
    12. https://www.avoma.com/blog/sales-forecasting-tools
    13. https://www.mdm.com/article/featured/featured-blog/ai-in-the-field-the-secret-to-success-in-outside-sales/
    14. https://www.glideapps.com/use-cases/field-sales
    15. https://www.mentimeter.com/blog/business/ai-sales-tools
    16. https://huble.com/blog/ai-use-cases
    17. https://www.getassistive.com/blog/field-sales-crm-solution-checklist-of-essential-features
    18. https://www.clappia.com/blog/how-ai-can-help-with-field-sales-tracking
    19. https://zapier.com/blog/ai-sales-assistant/
    20. https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
    21. https://skaled.com/insights/generative-ai-sales-checklist/
    22. https://veloxy.io/sales-ai/field-sales-software/
    23. https://spotio.com/blog/ai-sales-tools/
    24. https://www.fullestop.com/blog/ai-in-sales-use-cases-benefits-and-challenges
    25. https://www.quantified.ai/blog/sales-readiness-checklist/
    26. https://www.techpowerup.com/forums/threads/report-ai-software-sales-to-experience-massive-growth-with-40-6-cagr-over-the-next-five-years.325039/
    27. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/an-unconstrained-future-how-generative-ai-could-reshape-b2b-sales
    28. https://www.gartner.com/peer-community/post/ai-improve-sales-operations
    29. https://www.salesmate.io/blog/ai-sales-assistant/
    30. https://learnprompting.org/blog/ai-tools-for-sales-teams
    31. https://www.mindtheproduct.com/order-promising-the-secret-sauce-of-multi-channel-fulfillment/
    32. https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html
    33. https://www.vivun.com/blog/5-ai-trends-shaping-the-future-of-sales
    34. https://www.linkedin.com/pulse/ai-agents-overpromising-underdelivering-closer-look-richard-hn2pc
    35. https://veloxy.io/sales-ai/software-comparison/