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Tag: ChatGPT

  • Salesforce AI Agents: Are they the next big AI breakthrough?

    Salesforce AI Agents: Are they the next big AI breakthrough?

    The Evolution of Salesforce AI Agents: Transforming Business Operations

    Unless you have been hiding under a proverbial rock and missed it, AI Agents are the big buzz in tech news right now. If you’re curious what the heck these AI Agent things are, where they came from and why marketing is pushing them so hard – this is the article for you!

    Let’s go way back in tech history to the “pre-AI days”, one long decade ago. Salesforce’s foray into AI began in 2014 when CEO Marc Benioff declared the company would become “AI-first.” This strategic shift aimed to revolutionize Salesforce from a system of recordings to a system of intelligence, making AI accessible to every company and employee.

    The Genesis of Salesforce AI

    The acquisition of RelateIQ in 2014 marked the beginning of Salesforce’s AI evolution, introducing machine learning capabilities to capture workplace communications data and provide valuable insights. Over the years, Salesforce has consistently invested in AI technologies, developing predictive AI tools and eventually venturing into generative AI.

    Agentforce: The Next Frontier

    In 2024, Salesforce introduced Agentforce, a suite of autonomous AI agents designed to enhance business operations across various domains. These agents, unlike traditional chatbots, are intelligent and dynamic, capable of understanding context and nuance in customer interactions.

    Current Applications of Salesforce AI Agents

    1. Customer Service: Agentforce Service Agent handles a wide range of service issues without preprogrammed scenarios, improving efficiency.
    2. Sales Development: The SDR Agent engages with inbound leads 24/7, answering questions and booking meetings for human sellers.
    3. Sales Coaching: AI agents provide personalized role-play sessions for sales teams, helping them practice pitches and handle objections.
    4. E-commerce: Merchandiser and Personal Shopper agents assist with site setup, personalized promotions, and enhance the buying experience.
    5. Marketing: Campaign Optimizer automates the full campaign lifecycle, from analysis to optimization.

    Future AI in ‘25

    Salesforce AI agents are expected to become more integrated into daily business operations with a big Q1 push. They will likely:

    – Enhance decision-making processes by providing real-time, data-driven insights.

    – Automate more complex tasks across various departments.

    – Offer more personalized customer experiences through advanced data analysis.

    – Collaborate more seamlessly with human workers, augmenting their capabilities.

    5 Compelling Salesforce AI Agent Use Cases for Consideration

    1. Autonomous Customer Service: The Service Agent can handle customer inquiries across multiple channels 24/7, resolving issues quickly and escalating complex cases to human agents when necessary.
    2. AI-Powered Sales Development: The SDR Agent qualifies leads, answers prospect questions, and schedules meetings, allowing human sales representatives to focus on high-value interactions.
    3. Personalized E-commerce Assistance: The Personal Shopper agent acts as a digital concierge, offering tailored product recommendations and assisting with search queries on e-commerce platforms.
    4. Automated Marketing Campaigns: The Campaign Optimizer agent analyzes, generates, personalizes, and optimizes marketing campaigns based on business goals and customer data.
    5. Continuous Employee Support: Agentforce assists employees with various tasks, from data retrieval to action plan creation and execution, boosting workplace efficiency.

    Real-World Examples of Successful Agentforce Implementation

    Looking for something done vs. talked about? Several companies have successfully implemented Salesforce AI agents. Here are five Examples with links:

    1. OpenTable: The restaurant reservation platform uses Agentforce for Service to handle common issues like account reactivations, reservation management, and loyalty point inquiries. This has boosted productivity for service representatives [3].
    2. Wiley: The educational publisher implemented Agentforce Service Agent to manage seasonal surges in service requests. This resulted in a 40-50% increase in case resolution, outperforming their previous chatbot. Additionally, Wiley reduced new agent onboarding time by 50% and achieved a 213% return on investment [2][3].
    3. 3. Precina Health: This healthcare innovator leverages Agentforce to improve diabetes care. In a pilot study, Agentforce and Salesforce technologies helped reduce the average blood sugar levels of 50 patients in Rural Louisiana from 9.6 to 6.4 (measured via an A1C test) in just twelve weeks [1].
    4. AAA Washington: The American Automobile Association used Salesforce Agentforce to create an autonomous agent capable of answering customer support requests, keeping clients informed about roadside updates, and offering personalized suggestions to customers [1].
    5. Saks Fifth Avenue: The luxury retailer utilizes AI service agents from Salesforce Agentforce to enhance their shopping experience. By integrating Customer 360, Commerce Cloud, and Slack, Saks has streamlined information flow for both staff and customers [3].

    These real-world examples demonstrate how Salesforce AI agents are being used to improve customer service, increase operational efficiency, and drive business growth across various industries.

    For businesses evaluating Salesforce AI agents, it’s crucial to understand that these tools offer more than just automation. They provide intelligent, context-aware assistance that can significantly enhance customer experiences, streamline operations, and drive growth. With the ability to customize agents, businesses can tailor AI solutions to their specific needs and industry requirements.

    As AI technology continues to evolve, Salesforce’s commitment to innovation ensures that its AI agents will remain at the forefront of business transformation, offering increasingly sophisticated and valuable solutions to enterprises of all sizes.

    Pick a strong AI enabled Salesforce consulting partner and see how you can successfully apply agents in ’25.

    Research Citations

    1. https://www.cxtoday.com/crm/agentforce-case-studies/
    2. https://www.salesforce.com/news/stories/data-ai-customer-stories/
    3. https://www.salesforce.com/customer-stories/?bc=OTH
    4. https://www.salesforce.com/news/stories/building-business-ai-agents/?bc=OTH
    5. https://digitaldefynd.com/IQ/salesforce-using-ai-case-study/
    6. https://www.salesforce.com/agentforce/use-cases/
    7. https://www.salesforce.com/artificial-intelligence/use-cases/
    8. https://www.salesforce.com/news/stories/video/agents-at-scale-within-salesforce-platform/
  • How to Select Top AI-Enabled Salesforce Consulting Partners in ’25

    How to Select Top AI-Enabled Salesforce Consulting Partners in ’25

    In 24 short months, AI has transformed from gee whiz isn’t that’s interesting-what can it do next? To must have software that can clearly create a competitive advantage when properly deployed. The question is no longer should you consider leveraging AI? It’s now what is the best way to do it, which processes are most apt to see immediate impact and ensure with proper planning that we create a competitive advantage with our AI instances in the process.
    For sales teams that rely on Salesforce, AI can be tricky to understand, plan for, implement and train without some outside best practice help.
    The challenge is finding the best Salesforce AI consulting partners that can guide businesses through the AI evaluation and adoption process, while ensuring that companies stay focused on AI that provides significant impact vs negligible to no discernible business value. Another big challenge is linking the new AI to the rest of the enterprise software stack while providing the extra layers of security (both human and software) that AI requires to reduce legal exposure.
    As we dig into ‘25, completely integrating AI into Salesforce has become crucial for businesses aiming to stay competitive or more often leapfrog competitors. That really isn’t up for argument anymore. However, selecting the right AI-enabled Salesforce consulting partner is critical to successful implementations and maximizing CRM potential. Here’s a comprehensive guide to help you make the best choice.
    Salesforce AI Consulting Best Practie Checklist
    When evaluating potential partners, ensure they possess:
    1. AI Expertise: Look for partners with proven AI implementation experience and relevant certifications.
    2. Salesforce Certifications: Verify that the partner holds up-to-date Salesforce certifications, especially in AI-related areas.
    3. Industry-Specific Knowledge: Choose a partner with experience in your industry for tailored solutions.
    4. Comprehensive Services: Opt for partners offering end-to-end services, from strategy to implementation and training. Buying AI and trying to “figure it out” is a recipe for trouble.
    5. Data Management Skills: Ensure they can handle data preparation and integration for AI analysis. Garbage in / Garbage out is more critical with AI than ever before. Clean, properly accessible data matters.
    6. Scalable Solutions: Look for partners providing scalable AI services to grow with your business. AI isn’t set it and forget it. You need partners that can jump in and help with short notice.
    7. Strong Client References: Request and verify references from clients who have implemented AI solutions.
    Watch-Out-For Checklist
    Be cautious of partners who:
    1. AI innovation capabilities: Look for partners who stay current with AI trends, best practices and can customize Salesforce to leverage cutting-edge AI technologies.
    2. Offer Generic Solutions: Avoid partners who don’t tailor their approach to your specific business needs. How can you make AI your competitive advantage AI if your competition use the exact same thing?
    3. Have Limited Post-Implementation Support: Ensure they offer long-term support, as AI solutions often require ongoing adjustments.
    4. Overemphasize Certifications: While important, certifications alone don’t guarantee practical expertise. Listen to them – can they easily answer questions? Do you want to work with them?
    5. Neglect Data Privacy: Verify their commitment to data security and compliance with relevant regulations. AI legal exposure is increasing. Must be legal and software privacy systems in place.
    6. Have a Narrow Focus: Be wary of partners who can’t integrate AI across multiple Salesforce clouds. They need to know business and software to succeed.
    7. Lack Transparency: Choose partners who are clear about their processes, timelines, and pricing.
    8. Neglecting to check client references: Request and verify references from clients who have specifically implemented AI solutions with the partner.
    9. Overlooking post-implementation support: Ensure the partner offers long-term support for AI solutions, as they often require ongoing adjustments.
    10. Rushing the selection process: Avoid making hasty decisions without thorough research on the partner’s AI capabilities, past projects, and client feedback.
    11. AI Case Studies: What have they done you can look at and play with.
    Certified Salesforce Consulting Partners
    Here’s a list of some top Salesforce consulting partners known for their AI capabilities:
    1. Accenture
    2. Deloitte Digital
    3. Capgemini
    4. IBM
    5. Cognizant
    Remember, the best partner for your organization will depend on your specific needs, industry, and scale of operations. Conduct thorough research and engage in detailed discussions before making your final decision.
    By following this guide and avoiding common pitfalls, you’ll be well-equipped to select an AI-enabled Salesforce consulting partner that can drive your business forward in 2025 and beyond.
    References
    1. Salesforce Partner Finder: https://www.salesforce.com/partners/find-a-partner/
    2. Salesforce AI Capabilities: https://www.salesforce.com/products/einstein/overview/
    3. Gartner Magic Quadrant for Salesforce Implementation Services: https://www.gartner.com/en/documents/3991199
    4. Forrester Wave: Salesforce Consulting Partners: https://www.forrester.com/report/The+Forrester+Wave+Salesforce+Consulting+Partners+Q2+2021/-/E-RES161624
    5. IDC MarketScape: Worldwide Salesforce Implementation Services: https://www.idc.com/getdoc.jsp?containerId=US46733820

  • In Gratitude- Thanksgiving Season 365

    In Gratitude- Thanksgiving Season 365

    “You have it easily in your power to increase the total of this world’s happiness now. How? By giving a few words of appreciation to someone lonely or discouraged. Perhaps you will forget the kind words you say today tomorrow, but the recipient may cherish them forever.”

    Dale Carnegie

    November and Thanksgiving are the perfect times to reach out and communicate with those who have impacted our lives in the past and present. In our digital age, many ways of communicating exist, including social media, email, and text. AI (Artificial Intelligence) can make it more difficult since everyone is a prompt away from crafting the perfect message. Ironically, snail mail (a.k.a. United Postal Service is an easy, cost-effective way to communicate gratitude with a handwritten notecard.

    Does writing a handwritten note take planning and effort? Absolutely, yes.  However, the person and relationship dividends are priceless. Are handwritten notes perfect? Absolutely not. Handwritten notes are perfectly imperfect. In three sentences, you can express appreciation, reference memory, and have something tangible for the recipient to keep.

    Every handwritten note is a souvenir (French verb to remember) for the writer and the recipient. Writing 365 cards in 365 days is my quest for gratitude for 2025. With all of AI’s advances, I cannot share my sincere appreciation and memories or be a tangible keepsake.

    One of my most prized possessions is a card I made for my Great Grandmother when I was 5.  I found it when I became the historian for my family.  She died at 87 and died when I was 11. However, she kept it all those years. A card made on notebook paper folded the wrong way, and I did not have blue eyes.  I keep the notecards that are written to me. Many have told me that they keep the handwritten notecards that I write and how my notes touched them.

    I admit that I used ChatGPT to explain that handwritten notes are unique and can’t be replicated.    

    From ChatGPT

    Essentially, a handwritten note lets the recipient know that you are thinking of them, that they are important, and that you care about their well-being. Secondly, it requires your full and undivided attention, and it takes time to give it a personal touch.

    AI can indeed influence the content of a handwritten note, though it typically works behind the scenes. While AI can enhance or facilitate the process, the emotional impact of a handwritten note still comes from the human element—choosing to send the note and personalizing its content.

    Here are some ways AI might be involved:

    1. Content Suggestions: AI can help draft or suggest thoughtful messages, particularly if the sender struggles with expressing themselves. For example, AI-powered writing tools or assistants can generate phrases, prompts, or entire messages tailored to specific situations, such as birthdays, thank-you notes, or condolences.
    2. Personalization: AI can help personalize notes by analyzing data about the recipient, such as their preferences, past interactions, or shared experiences. This can help the sender craft a more meaningful message.
    3. Handwriting Simulation: AI can also simulate handwriting styles. Some services allow users to type their message, and then AI-driven tools transform it into handwriting that appears more personal and authentic, though it isn’t truly handwritten.
    4. Grammar and Style: AI-powered tools like Grammarly can help proofread and improve the tone or flow of the message, ensuring that it is well-written and coherent.

    Differences between AI and the human element lie in authenticity, emotion, and intent. Here’s how they differ in specific aspects:

    1. Creativity and Emotion:

    • AI: AI can generate text, mimic styles, and produce content that appears human-like, but it needs more true understanding, emotions, and lived experiences. It analyzes patterns and produces responses based on data, but it doesn’t experience feelings or empathy. Algorithms and training on vast amounts of data drive AI’s creativity.
    • Human Element: Humans create from personal experience, emotions, and unique worldviews. When a human writes something, it often carries personal insights, emotional depth, and spontaneity that stem from real-life experiences. The intention behind human expression adds a layer of meaning that AI can mimic but not generate.

    2. Intent and Personal Connection:

    • AI: AI responds based on patterns and input data but doesn’t have personal relationships or emotional investment. It can simulate care and connection, but it doesn’t honestly “care.” The intent behind AI-generated content is utilitarian, driven by efficiency, not personal connection.
    • Human Element: When a human reaches out, whether through a handwritten note or conversation, there’s genuine intent and a personal connection. Human interaction involves empathy, understanding, and emotional resonance. A handwritten note from a person shows thoughtfulness, effort, and care that go beyond the content—it reflects the person’s desire to connect.

    3. Authenticity and Imperfection:

    • AI aims for precision and can create nearly flawless, polished messages. However, this perfection can sometimes feel impersonal or mechanical. Although the content it produces can be accurate, it lacks the small, unique imperfections that often make human communication feel authentic.
    • Human Element: Human communication is filled with nuance, subtle imperfections, and individual quirks—slightly messy handwriting, an unintended pause, or a personal anecdote. These imperfections are part of what makes human interaction feel genuine and relatable.

    4. Context and Depth of Understanding:

    • AI: AI can quickly process vast amounts of information and respond based on learned patterns, but it lacks proper comprehension. It doesn’t experience life, culture, or context as humans do. AI might miss a conversation or note’s deeper emotional, cultural, or situational nuances.
    • Human Element: Humans communicate through lived experience, cultural context, and emotional awareness. A human’s understanding of the world is shaped by personal experiences, relationships, and emotions, making human-generated content often more profoundly layered and contextually rich.

    5. Motivation and Effort:

    • AI: AI operates based on instructions and input without any personal motivation. Its effort is driven by algorithms and a pre-set goal to optimize output, but it doesn’t feel satisfaction or dedication in its tasks.
    • Human Element: Humans choose to invest time and effort, especially when it comes to meaningful gestures like writing a handwritten note. The personal effort, care, and attention a human puts into such a task make it special, signaling that they value the recipient.

    In summary, AI can assist, automate, and simulate aspects of human communication, but it needs more emotional depth, authenticity, and genuine intent to define the human element. The human touch carries real experiences, emotions, and connections that AI can mimic but never truly replicate.

    Sylpro Productions began with Sylvia France, and the goal was to work with Apple in Silicon Valley. Not only did she realize that goal, but she also launched new products and industry categories, including personal digital navigation and streaming television program guides. In addition to working with several startups, our client roster includes The Home Depot, Union Pacific, Verizon, and Right at Home. To learn more, contact me at sylvia@sylproproductions.

  • 10 Best Practices from World Class Sales Teams, Beyond Exceeding Quota!

    10 Best Practices from World Class Sales Teams, Beyond Exceeding Quota!

    Not an AI Agent Dave here to check in and give you the latest in what I’ hearing on the AI strip from the C-Suite AI conference bandwagon gang.

    The C-Suite / BOD has educated themselves and starting to get it. AI is either a competitive advantage or something to watch competitors leapfrog ahead. Yes, it can be awesome! No it doesn’t replace sales reps. Instead, with autonomous AI agents it helps humans do what they do best, create ongoing relationships with companies, products and services their customers need.

    Yikes you say. Have you been drinking the AI Kool-Aid you keep warning readers about?   

    “Kinda.” AI is super cool and autonomous AI agents are most definitely the rage with Microsoft and Salesforce at AI “war” with each other over whether their AI agents actually work.

    TBS as always with my 100K View on sales / marketing perspectives, it isn’t what’s cool new tech, it’s what tech gives you that unfair advantage when you properly apply it to a proven process! More simply. Get back to your MBA strategy 101 thinking.

    What can I do better, faster, cheaper and / or how can I differentiate my company with this new technology. It doesn’t matter if its 100% AI or not. Make sure it is actually providing a solid ROI or can in the near future. Think about it from a non tech perspective. These are the top ten problems my company is facing. Tech isn’t the answer, its applying AI to things we know can work and doing it faster and better than our competitors.

    If you go beyond numbers, focusing on best practices that foster long-term growth, customer satisfaction, and team cohesion, you’ll discover what it is that makes a top team tick. Let’s explore these AI best practices with real-world examples and lessons learned. *{Every use case has an article and a link behind at the end of the article}.

    1. Data-Driven Decision Making

    Leading sales teams leverage data analytics to inform their strategies and tactics.

    Use Case: Google uses data from reviews and surveys to understand the need for managers and improve their leadership development programs. This approach has helped Google identify effective manager qualities and support their growth.

    2. Consistent Sales Process Adherence

    High-performing teams closely follow their established sales processes across all departments.

    Use Case: Coca-Cola has woven data analytics into the fabric of their operations, informing decisions in sourcing, distribution, sales, and production. This consistent approach ensures a unified strategy throughout the organization.

    3. Value-Based Selling

    Top sales teams focus on communicating the value and ROI of their solutions rather than competing on price.

    Use Case: Amazon uses data insights to make personalized product recommendations to customers, increasing sales and maintaining their position as the world’s leading e-commerce platform.

    4. Effective Questioning Strategies

    Successful teams use sophisticated discovery and questioning approaches to uncover client needs.

    Use Case: Netflix analyzes user data to understand what triggers people to subscribe, what makes them stay longer, and where to invest in improving services. This data-driven approach helps them ask the right questions and make informed decisions about content creation and user experience.

    5. Continuous Sales Coaching

    High-performing teams prioritize ongoing coaching as part of their training and development strategy.

    Use Case: Google’s data scientists analyzed qualitative data like performance reviews and surveys about managers within the organization to improve their internal hierarchies and coaching practices.

    6. Strategic Hiring Practices

    Top sales organizations use advanced hiring tools and assessments to build stronger teams.

    Use Case: While not specifically mentioned in the search results, companies like Unilever and IBM are known to use AI-powered tools and data analytics in their hiring processes to identify the best candidates for sales roles.

    7. Omnichannel Sales Strategy

    Leading sales teams implement a coordinated approach across multiple channels for a seamless customer experience.

    Use Case: Starbucks uses big data through mobile apps and reward programs to gather insights directly from customers, creating a better omnichannel customer experience and personalizing their marketing strategies.

    8. Personalized Sales Presentations

    Top performers customize their presentations for each prospect, demonstrating thorough research and understanding.

    Use Case: Red Roof Inn combined weather reports and flight cancellation data to identify marketing opportunities, targeting mobile users in the vicinity of airports during bad weather. This personalized approach led to a 10% increase in check-ins.

    9. Effective Use of AI and Technology

    Leading sales teams leverage AI and other advanced technologies to increase productivity and gain competitive advantages.

    Use Case: Uber uses predictive analytics and big data to bridge the demand-supply gap in real-time, catering to customer demands and solving supply issues efficiently.

    10. Post-Sale Relationship Nurturing

    Top sales organizations focus on building long-term relationships and nurturing customers after the initial sale.

    Use Case: Amazon’s use of data analytics extends beyond the initial sale, analyzing customer reviews, rankings, and post-purchase behavior to continually improve their service and maintain customer loyalty.

    Checklist for CROs

    To evaluate your organization’s alignment with these best practices, consider this checklist:

    •  Do we have a robust data analytics system in place like Google’s people analytics?
    •  Is our sales process clearly defined and consistently followed across departments like Coca-Cola’s?
    •  Are we focusing on value-based selling rather than price competition, similar to Amazon’s personalized approach?
    •  Have we implemented advanced questioning strategies in our discovery process, akin to Netflix’s data-driven content decisions?
    •  Do we have a structured, ongoing sales coaching program informed by data like Google’s manager quality analysis?
    •  Are we using advanced hiring tools and assessments?
    •  Have we implemented an omnichannel sales strategy like Starbucks?
    •  Do our sales presentations demonstrate thorough customization for each prospect, similar to Red Roof Inn’s targeted marketing?
    •  Are we effectively leveraging AI and other advanced technologies like Uber’s predictive analytics?
    •  Do we have a strong post-sale relationship nurturing program similar to Amazon’s continuous customer engagement?

    By comparing your current practices against this best practice checklist and these industry-leading examples, you can identify areas for improvement and align your sales organization with the best practices of top-performing teams.

    1. Google’s People Analytics
      URL: https://www.reddit.com/r/compsci/comments/15pub3z/datadriven_decision_making_case_studies/
    2. Starbucks’ Store Location Analysis
      URL: https://online.hbs.edu/blog/post/data-driven-decision-making
    3. Amazon’s Product Recommendation System
      URL: https://online.hbs.edu/blog/post/data-driven-decision-making
    4. Red Roof Inn’s Weather-Based Marketing
      URL: https://gapscout.com/blog/5-data-driven-decision-making-examples/
    5. Uber’s Real-Time Ride Pricing and Allocation
      URL: https://gapscout.com/blog/5-data-driven-decision-making-examples/
    6. Google’s Manager Quality Analysis
      URL: https://programs.online.utica.edu/resources/article/data-driven-decisions
    7. Amazon’s Customer Behavior Analysis for Recommendations
      URL: https://programs.online.utica.edu/resources/article/data-driven-decisions
    8. Southwest Airlines’ Customer Data Analysis for Service Optimization
      URL: https://programs.online.utica.edu/resources/article/data-driven-decisions
    9. Google’s Data Center Energy Optimization
      URL: https://www.reddit.com/r/compsci/comments/15pub3z/datadriven_decision_making_case_studies/
    10. Service Hotel’s Data-Driven Culture Implementation
      URL: https://www.reddit.com/r/compsci/comments/15pub3z/datadriven_decision_making_case_studies/

    Tags: #starbucks #amazon #salesforce #redroofin #Google #Uber #data #datascience #sales #bestpractice #AI

  • CRM Dead? Sherlock Holmes- AI the Perp?

    CRM Dead? Sherlock Holmes- AI the Perp?

    Will AI be the Perpetrator of CRMs demise?

    On a weekly basis I get C-Suite execs calling me after AI events like Dreamforce and any of the myriad AI “you name it conferences” asking me if they can replace their salespeople with AI agents right now, or…use AI to replace their pesky CRMs…Or both! Yikes – the Kool-Aid is flowing in AI land!

    I ask them to tell me about the last AI conference they attended, review the event marketing puffery and often outlandish claims of what AI can do “just by buying our XYZ AI Tool and sales will Soar,” and let out an inaudible sigh.

    These conferences are designed to get attendees to drink the Alice in Wonderland vision of an utopian AI and just like every other tech hype cycle before it (yep AI isn’t the first “revolutionary technology”) there is a kernel of truth in what AI “maybe able to do.”  That being said. I see a lot of could do vs. can do/doing right now in the AI adoption cycle.    

    So, let’s get it out of the way. AI is unlikely to fully replace CRM systems in the next few years. But top enterprise grade AI enabled CRMs are already doing far more than traditional CRMs ever could. Most significantly. The most advanced AI enabled CRMS are already enhancing and transforming enterprise CRM capabilities. This will accelerate as cutting-edge AI technologists and tech savvy AI enabled salespeople work together to enhance what AI does well and keep what was working. (See Blended AI).

    With that in mind, let’s look at what the research and our own inhouse AI fortune tellers have forecasted for AI CRM Advancements:

    AI Evolution Timeline for CRM Systems

    2024-2025: Enhanced AI Features

    In the next 12 months, we’ll see more widespread adoption of AI-powered features within existing CRM platforms:

    – Advanced predictive analytics for sales forecasting and lead scoring

    – Natural language processing for improved chatbots and virtual assistants

    – AI Agents deployed

    – Automated data entry and cleansing

    -Conversational sentiment analysis post call – heading to real-time

    -Training recommendations based on AI evaluation of seller daily actions

    -Email comprehension, writing, reply and auto CRM addition (no data entry)

    2026-2028: AI-First CRM Platforms

    Within 5 years, we’ll likely see the emergence of AI-centric CRM systems:

    – Full voice activated conversational interfaces for querying and updating CRM data

    – AI-driven workflow automation and task prioritization

    – Hyper-personalized customer interactions at scale

    – AI driven B2B sales without human intervention or approval

    – AI driven procurement – AI Agents talking to Agents lowest price, shipping

    -AI driven manufacturing risk mitigation in procurement, sales and maintenance  

    -Human involvement / need for “human” salespeople evolving with blended AI at its core

    2029 and beyond: Potential AI Dominance

    In 5+ years, AI could become the primary driver of CRM functionality / replace many CRM based features:

    – Autonomous decision-making for many customer-facing processes

    – Predictive modeling of complex market trends and customer behaviors

    – Seamless integration with other business systems for holistic insights

    -Significantly faster response times with AI driven reporting and customer centric personalization enhancing customer service while reducing costs

    AI Features Poised to Transform CRM

    Intelligent Automation

    AI will take over routine tasks, freeing up human workers for higher-value activities. This includes automated data entry, scheduling, and follow-ups [1].

    Hyper-Personalization

    AI-powered CRMs will analyze vast amounts of data to deliver truly personalized experiences for each customer, far beyond current segmentation capabilities [1].

    Predictive Analytics

    Advanced AI models will provide increasingly accurate forecasts of customer behavior, sales trends, and market shifts [2].

    Natural Language Interfaces

    Conversational AI will allow users to interact with CRM systems using natural language, dramatically improving ease of use and adoption rates [3].

    Autonomous Decision-Making

    As AI systems become more sophisticated, they’ll be able to make certain decisions without human intervention, such as adjusting pricing or inventory levels based on real-time data [4].

    Benefits of AI-Enhanced CRM Available Now

    1. Improved efficiency, data capture & access, with blended AI productivity gains
    2. More accurate sales forecasting and lead scoring – enhancing C-Suite confidence
    3. Gold standard customer service experiences through personalization
    4. Better data quality and insights
    5. Reduced human error in data entry and analysis
    6. Significantly reduced data entry and time devoted to CRM maintenance activities
    7. Email writing, reply and auto CRM entry based on sales rep skill & role personalization
    8. Personalized training based on AI assessment of activities and results for all reps
    9. Hyper focused AI enhanced sales processes based directly on CRO sales goals, tracking requirements and 24/7 view of all sales activities with AI driven recommendations
    10. AI enhanced reporting on rep performance with training recommendations, improvement opportunities, or other rolls the AI “sees” they could be much better at than sales
    11. Cull ranking based on unbiased review of performance, potential, opportunities, training performance and client recorded sentiment analysis
    12. Enhanced AI driven cross sell and upsell opportunities    

    Why AI Won’t Completely Replace CRMs

    As noted in the features and benefits previously, AI has enhanced CRM capabilities and will undoubtedly transform enterprise CRM systems in amazing ways we haven’t envisioned yet. With that as a base AI is unlikely to fully replace them in the next five years. This is a mix of legacy CRM systems already deployed, reluctance to change (IT and C-Suite) and legacy enterprise integrations which can slow transitions.

    Companies facing difficult situations that know they must change or go bankrupt, profitable with innovative leadership teams or emerging companies building their AI infrastructure now or in the coming years will be far more likely to be fully AI integrated / replace CRMs.  

    Here are the things that AI can’t do by itself and will require Blended AI to accomplish

    1. Human Touch: Many aspects of customer relationships require human empathy, understanding and perceptual judgment that AI cannot replicate
    2. Complex Decision-Making: High-stakes decisions often require human oversight and contextual understanding. Think internal politics – who know who and how to get things done
    3. Data Privacy Concerns: Regulations and customer trust issues may limit the extent of AI’s autonomy in handling sensitive data
    4. Integration Challenges: Fully replacing existing CRM systems with AI would be a massive undertaking for most organizations (see current legacy system replacement timelines)
    5. Ethical Considerations: The use of AI in customer relationships raises ethical questions that will need to be carefully addressed (see EU AI ACT and recent California AI Act)

    10 Things AI Can Do That Traditional CRMs Cannot

    AI Capability

    Traditional CRM

    Real-Time Sentiment Analysis

    Static Customer Data

    Automated Data Enrichment

    Manual Data Entry

    Predictive Lead Scoring

    Rule-based scoring

    Natural language querying

    Complex Query Languages

    Autonomous Task Prioritization

    Manual Task Management

    Adaptive Sales Forecasting

    Personalize Content Generation

    Anomaly detection in customer behavior

    Cross-Channel Interaction Analysis

    Continuous self-optimization

    Fixed Forecasting Models

    Template Based content Generation

    Threshold based alerts

    Siloed Channel data

    Periodic Manual Updates

    If we stick with the facts vs. the AI CRM enhanced industry marketing puffery CRM is here for the long run (at least for the next 5 years (see legacy systems like Siebel CRM on premise still running). While AI already is and will dramatically enhance CRM capabilities in the coming years, it’s more likely to augment rather than replace CRM systems entirely.

    The future of CRM lies in blended ai. The seamless integration of AI technologies with human expertise to deliver superior customer experiences and business outcomes.

    Thanks for reading and please add your thoughts and comments to the future of AI & CRM!

    Research and Citations:

    [1] https://ddi-dev.com/blog/programming/ai-in-crm-benefits-and-use-cases-of-ai-powered-crm-system/

    [2] https://www.itransition.com/ai/crm

    [3] https://blog.hubspot.com/sales/crm-with-ai

    [4] https://callhippo.com/blog/telephony/how-ai-technologies-are-changing-the-face-of-crm

    [5] https://www.cio.com/article/465624/5-ways-ai-will-transform-crm.html

    [6] https://technologyadvice.com/blog/information-technology/ai-crm/

    [7] https://foundationinc.co/lab/the-rise-of-ai-in-crm/

    [8] https://www.blobr.io/post/ai-crm-industry

    Social Media Hashtags:

    #CRM #AIadoption #AIenabledCRM #AICRM #AI2025 #IndustryInnovation #AIRobotics #TechTransformation #Manufacturing #SAP #AIadoption #AITools #Siebel 

     

  • The AI Revolution in Customer Experience: How Chief Customer Officers Are Leveraging Technology in 2024 and Beyond

    The AI Revolution in Customer Experience: How Chief Customer Officers Are Leveraging Technology in 2024 and Beyond

    As the business landscape evolves, the role of Chief Customer Officer (CCO) has become increasingly crucial. These executives are responsible for overseeing and optimizing the entire customer experience, from initial contact to post-purchase support. In today’s digital age, CCOs are turning to artificial intelligence (AI) to revolutionize how they understand, engage with, and satisfy customers.

    The Role of a Chief Customer Officer

    A Chief Customer Officer is the executive responsible for creating a customer-centric culture within an organization. Their primary duties include:

    1. Developing and implementing customer experience strategies
    2. Analyzing customer data to drive insights
    3. Overseeing customer service operations
    4. Collaborating with other departments to ensure customer-centricity
    5. Measuring and reporting on customer satisfaction metrics

    10 Ways CCOs Are Using AI in 2024

    1. Predictive Analytics for Customer Behavior
      AI-powered predictive analytics to anticipate customer needs and behaviors, allowing for proactive engagement and personalized experiences.
    2. AI-Driven Personalization
      Machine learning algorithms, are creating hyper-personalized customer journeys across all touchpoints. Job title, function, purchasing power, experience can all be used to personalize each interaction.
    3. Chatbots and Virtual Assistants
      AI-powered chatbots are being deployed to provide 24/7 customer support, handling routine inquiries and freeing up human agents for more complex issues. This is one of the areas most AI enthusiasts see as low hanging fruit. The trick is making the customer experience better than with a well-trained human. That is still a goal.
    4. Sentiment Analysis
      Sales, Marketing and CCOs are all using AI to analyze customer feedback across various channels, gaining real-time insights into customer sentiment and satisfaction levels. Outliers – really good / bad customer interactions will be immediately flagged by AI and sent to management for review.
    5. Voice of Customer (VoC) Programs
      AI is enhancing VoC programs by processing and analyzing vast amounts of unstructured customer feedback data. The key is creating the right prompts to gain the best actionable insights. AI is perfect for this.
    6. Customer Churn Prediction
      Machine learning models are being employed to identify customers at risk of churning, allowing for targeted retention efforts. This has been done for a long time already. See Sentia.
    7. Automated Quality Assurance
      AI is being used to monitor and analyze customer interactions, ensuring consistent service quality across all channels. (See #4)
    8. Intelligent Routing
      CCOs are implementing AI-powered systems to route customer inquiries to the most appropriate agent or department based on the nature of the request. The most important calls (highest paying customers) usually have the top tier of service. Regardless getting customers to the right answers (AI or People) is critical to customer happiness and retention.
    9. Predictive Maintenance
      For product-based companies, AI is being used to predict when customers might need maintenance or support, enabling proactive outreach. They are also using IoT on machines to max output / maintenance downtime. Manufacturing is generally a bit behind in the AI race but will rapidly evolve.
    10. Customer Lifetime Value Optimization
      AI algorithms are helping CCOs identify high-value customers and optimize strategies to increase their lifetime value. The best customer is your current one. The cost of getting new clients is almost always higher than upselling, cross selling and keeping current customers happy!

    How AI Enhances Customer Satisfaction

    That sounds swell. Tell me how can AI play a crucial role in elevating customer satisfaction levels across various touchpoints? *(See research links at end of article and additional reading)

    Personalized Experiences at Scale

    AI enables businesses to deliver personalized experiences to each customer, regardless of the size of their customer base. By analyzing vast amounts of data, AI can tailor interactions, recommendations, and offers to individual preferences and behaviors. This level of personalization makes customers feel valued and understood, leading to higher satisfaction rates.

    Faster Resolution Times

    AI-powered chatbots and virtual assistants can provide instant responses to customer queries, significantly reducing wait times. For more complex issues, AI can quickly route customers to the most appropriate human agent, ensuring faster resolution. This efficiency in handling customer inquiries leads to improved satisfaction and reduced frustration.

    Proactive Customer Service

    By leveraging predictive analytics, AI can anticipate customer needs and potential issues before they arise. This allows businesses to reach out proactively with solutions or support, demonstrating attentiveness and care. Proactive service not only solves problems more efficiently but also shows customers that the company values their experience.

    24/7 Availability – When does AI Sleep?

    AI-driven customer service solutions offer round-the-clock support, catering to customers across different time zones and schedules. This constant availability ensures that customers can get help whenever they need it, enhancing their overall experience with the brand. It also doesn’t need to eat, sleep, have a bad day or take a break.  

    Consistent Quality of Service

    AI systems can maintain a consistent level of service quality across all interactions. Unlike human agents who may have varying levels of knowledge or experience, AI can provide standardized, accurate information every time. This consistency builds trust and reliability in the customer’s mind. However, the best approaches are almost always hybrid. Using Blended AI or the integration of humans and AI for optimal outcomes.

    Emotional Intelligence in Interactions

    Advanced AI systems are now capable of detecting customer sentiment and adjusting their responses accordingly. This emotional intelligence allows for more empathetic and appropriate interactions, which can significantly improve customer satisfaction, especially in challenging situations. (See #4 how sentiment analysis can be used)

    Seamless Omnichannel Experience

    AI facilitates a seamless experience across multiple channels by integrating data from various touchpoints. Customers can switch between channels (e.g., from chat to phone) without losing context, creating a more cohesive and satisfying journey.

    By implementing these AI-driven strategies, businesses can significantly enhance customer satisfaction, leading to increased loyalty, positive word-of-mouth, and ultimately, business growth. As AI technology continues to evolve, its potential to improve customer satisfaction will only grow, making it an essential tool for businesses focused on delivering exceptional customer experiences.

    Bonus: AI Innovations on the Horizon for 2025

    1. Emotion AI: Advanced systems that can detect and respond to customer emotions in real-time during interactions.
    2. Augmented Reality (AR) Customer Support: AI-powered AR solutions for visual, interactive customer assistance.
    3. Predictive Personalization: AI systems that can anticipate and fulfill customer needs before they’re expressed.
    4. Voice-First Customer Experiences: AI-driven voice interfaces becoming the primary mode of customer interaction for many services.
    5. Quantum Computing in CX: Leveraging quantum computing for unprecedented data processing and predictive capabilities in customer experience management.

    As we move towards 2025, CCOs who embrace these AI innovations will be well-positioned to deliver exceptional customer experiences and drive business growth.

    Tags: #CustomerExperience #AI #ChiefCustomerOfficer #CX #ArtificialIntelligence #CustomerService #BusinessInnovation #CCO #bestpractices #GenAI #ChatGPT

    Research Links:

    1. 13 Ways AI Will Improve the Customer Experience in 2024 – Zendesk
    2. 10 Excellent Ways AI Will Improve Customer Experience in 2024 – Survey Sparrow
    3. How AI Will Transform Customer Satisfaction in the Future – Delegate
    4. How AI is Transforming Customer Experience for Businesses – Full Story
    5. AI in Customer Service: All You Need to Know – Zendesk
  • Is AI capable of Breaking the Solow Paradox?

    Is AI capable of Breaking the Solow Paradox?

    Breaking the Solow Paradox: Unlocking AI’s Productivity Potential in the Enterprise

    In 1987, Nobel Prize-winning economist Robert Solow famously quipped, “You can see the computer age everywhere but in the productivity statistics.” This statement, known as the Solow Paradox, encapsulates a dilemma that still resonates today: despite massive investments in technology, particularly in AI and enterprise software, measurable productivity improvements remain elusive for many businesses.

    The Solow Paradox: A Persistent Challenge

    Solow’s observation was based on decades of studying business performance. During the period from the 1960s through the 1990s, companies dramatically increased their spending on technology—by 25% year-over-year in some cases. Despite this investment, productivity, measured by outputs per unit of input, didn’t follow suit. In fact, it often declined, with productivity dropping as much as 3% annually over that time frame.

    Fast forward to today, and the paradox is still very much alive. In industries like customer relationship management (CRM), sales productivity has fallen by 25% in the last five years, even as spending on CRM software has tripled. This mirrors Solow’s paradox—technology is advancing at breakneck speed, but many organizations are still grappling with how to translate these advancements into productivity gains.

    Why Doesn’t Technology Always Improve Productivity?

    At the heart of this issue lies a misalignment between technology adoption and actual user needs. Let’s explore a few key reasons why technology, and especially enterprise software like CRMs and AI, often fails to drive the productivity it promises:

    Low User Adoption

    Despite significant investments in CRM software, a mere 26% of customer-facing teams actively use their CRM every day. This is a staggering statistic considering that CRM systems are designed to streamline sales and service processes, but if the majority of users are not engaging with the platform, it can never deliver on its promises of efficiency and productivity gains.

    Tool Overload

    Sales teams today are overwhelmed by tools—on average, they use 10 different applications just to close deals. Studies show that sales reps spend just 28% of their week actually selling, a number that has declined by 20% over the past five years. Much of this lost time is due to the juggling of tools and the associated administrative work.

    User Frustration

    The cognitive overload from having to switch between multiple applications is significant. 66% of sales reps report feeling “drowned” in tools, and 60% of staff in some studies admitted that they’ve considered leaving their jobs due to frustrations with their CRM software.

    The Solow Paradox in the AI Era

    As we enter the age of AI, the Solow Paradox takes on new dimensions. While AI promises to revolutionize productivity, we’re seeing similar patterns of investment without corresponding gains. Here’s how the paradox applies to AI today:

    1. Implementation Lag: Just as with computers in Solow’s time, there’s a significant lag between AI adoption and productivity gains. Organizations are investing heavily in AI, but many are still in the early stages of figuring out how to effectively integrate it into their workflows.
    2. Skill Mismatch: The rapid advancement of AI technology has created a skills gap. Many employees lack the necessary training to effectively use AI tools, leading to underutilization and reduced productivity.
    3. Overinvestment in Technology, Underinvestment in Complementary Factors: Companies often focus on acquiring the latest AI technology without investing sufficiently in the organizational changes, process redesigns, and employee training necessary to fully leverage these tools.
    4. Data Quality Issues: AI systems are only as good as the data they’re trained on. Many organizations struggle with data quality and integration issues, limiting the effectiveness of their AI implementations.
    5. Measurement Challenges: Traditional productivity metrics may not capture the full value of AI. Improvements in decision-making quality or customer experience, for instance, may not be immediately reflected in standard productivity measures.

    Chart: AI Investment vs. Productivity Growth

    Solow Paradox
     

    This chart illustrates the growing gap between AI investment and productivity growth, reminiscent of the original Solow Paradox.

    Moving Beyond the Paradox

    To break the cycle of unproductive technology spending, businesses need to focus on these core principles:

    1. User-Centric Design: Ensure AI solutions simplify day-to-day tasks and remove administrative burdens.
    2. Integrated Workflows: Seamlessly integrate AI with existing tools to reduce context switching.
    3. Focus on Real-Time, Actionable Insights: Emulate consumer apps by pushing relevant, timely information to users.
    4. Invest in Training and Change Management: Prioritize upskilling employees to effectively use AI tools.
    5. Redesign Processes: Rethink workflows to fully leverage AI capabilities, rather than simply automating existing processes.
    6. Improve Data Quality: Invest in data infrastructure and governance to ensure AI systems have high-quality inputs.
    7. Develop New Metrics: Create new ways to measure productivity that capture the full value of AI implementations.

    By addressing these factors, businesses can start to bridge the gap between AI investment and productivity gains, potentially breaking the cycle of the Solow Paradox in the AI era.

    For further research:

    1. McKinsey & Company: The Solow Paradox in the AI Age
    2. MIT Sloan Management Review: Productivity Paradox 2.0
    3. National Bureau of Economic Research: Artificial Intelligence and the Modern Productivity Paradox

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  • AI Replacing You? Remember the Last Tech Hype Cycles?

    AI Replacing You? Remember the Last Tech Hype Cycles?

  • Composite AI: The Synergy of Multiple Artificial Intelligence Techniques

    Composite AI: The Synergy of Multiple Artificial Intelligence Techniques

    There are so many AI buzzwords being thrown about it is hard to keep up at times.

    The majority of terms aren’t so much “new things,” its more like a novel way of using AI on a old process then being first to name the new version with AI. With that in mind lets dive into Composite AI. What is it? How is it being used and is it something you should be using at your company now or in the near future?

    Composite AI stands out as a blended approach that integrates various AI techniques to tackle complex challenges. Unlike traditional AI, which often relies on a single methodology, Composite AI creates a cohesive system by combining strategies such as machine learning, natural language processing, and computer vision. This integrated approach enhances problem-solving capabilities, allowing for the addressal of more intricate issues with greater precision. Blended AI is the mesh of human and AI.

    What is Composite AI?

    The term refers to the systematic integration of different AI technologies into a unified solution pipeline. It brings together diverse analytical techniques, including machine learning (ML), natural language processing (NLP), computer vision (CV), knowledge graphs, and more, based on their strengths to build an end-to-end AI solution tailored to the problem at hand. It isn’t just one thing rather a combination of AI tech.

    How is it being used?

    The use of Composite AI is growing swiftly, with its market value projected to reach USD 4.4 billion by 2028. This growth underscores the increasing recognition of Composite AI’s potential across various business sectors, including healthcare, finance, and manufacturing. Organizations are leveraging Composite AI to enhance diagnostics, improve customer service, and optimize manufacturing processes, among other applications.

    5 Use Cases for your Review

    1. Healthcare Diagnostics: Companies like Leeway Hertz are using Composite AI to improve medical diagnostics by combining imaging analysis with patient data, leading to more accurate and timely treatment decisions.

    2. Finance Fraud Detection: In the financial sector, Composite AI is utilized for fraud detection by integrating various techniques such as anomaly detection algorithms and predictive analytics to identify fraudulent activities.

    3. Retail Customer Experience: Retailers are enhancing customer service by using Composite AI to analyze customer behavior, predict purchasing patterns, and personalize shopping experiences.

    4. Urban Planning: Smart city management is another area where Composite AI is applied, using a combination of spatial analysis, predictive modeling, and sensor data interpretation to optimize urban development and services.

    5. Manufacturing Optimization: In manufacturing, Composite AI is employed to monitor equipment, predict failures, and adjust production processes, thereby improving efficiency and reducing downtime.

    Lessons Learned?

    Composite AI represents a significant leap forward in the AI domain, offering a more sophisticated, flexible, and intelligent system. By strategically combining various AI methods, Composite AI addresses complex problems more effectively and is set to transform industries with its robust decision-making capabilities. As we continue to witness its adoption and growth, Composite AI is poised to become an indispensable tool in the enterprise AI toolkit.

    At the end of the day, it isn’t about AI or tech – it is about what business problems can you tech solve for me?

    For further information on Composite AI and its applications, please take a look at the following articles and resources for your perusal.

    Gartner Information Technology – This is Composite AI

    Blended AI – How can Blended AI make an impact on Sellers

  • The Future of AI in Sales Tech. What’s Coming you Need to Know

    The Future of AI in Sales Tech. What’s Coming you Need to Know

    The Future of AI in Sales Tech: What to Expect by 2025

    As the year winds down, every C-Suite executive I work with is eager to leverage AI in sales tech to stay ahead. Board meetings today prioritize shareholder expectations and the pressing question: “How will AI drive our growth faster than competitors?”

    The challenge for corporate strategists is navigating the “Irrational Exuberance of AI.”

    Just because AI “can” do something doesn’t mean it “should.” ROI remains crucial – that will never be a passing fad. It’s vital to create realistic AI adoption timelines, implement risk mitigation strategies, and plan for inevitable failures as tech teams learn to optimize AI.

    Looking ahead to 2025, AI is set to transform sales technology with hyper-personalized outreach, advanced predictive analytics, live sentiment analysis, and PCLV models. AI will continue to revolutionize sales operations—and it will be an adapt or get left behind activity. The future belongs to those who embrace blended AI.

    Based on my research, the most impactful AI features aren’t always the flashiest. Focus on those with significant ROI potential. Make safe bets, stretch goals, and a few long shots in your AI strategy. Remember, AI isn’t a set-it-and-forget-it tool; it requires ongoing tuning and parallel operation with current systems for risk mitigation and maximization.

    Here are the top ten AI features to consider, evaluated on their potential ROI impact.

    Top Ten AI Sales Tech Features for 2025

    1. AI-Driven Sales Strategy Optimization
    2. Advanced Sentiment Analysis
    3. AI-Powered Virtual Reality (VR) Sales Demos
    4. Predictive Customer Lifetime Value (CLV) Models
    5. AI-Enhanced Sales Training Programs
    6. Dynamic Pricing Algorithms
    7. AI-Generated Sales Scripts
    8. Automated Contract Generation and Management
    9. AI-Driven Market Segmentation
    10. Real-Time Competitive Analysis

    AI Sales Tech in Manufacturing

    The manufacturing sector is rapidly adopting AI technologies, often outpacing other industries in terms of investment and implementation. According to a study by MIT Technology Review Insights, 64% of manufacturers are currently researching or experimenting with AI, and 35% have begun to put AI use cases into production2. This sector’s focus on AI is driven by the need for enhanced product and process innovation, reduced cycle times, and improved operational efficiency. Compared to other verticals, manufacturing shows a higher rate of AI adoption, with significant investments expected to continue growing over the next few years1.

    Top 3 AI Features in AI-Enabled Sales Tech for Manufacturing

    1. Predictive Maintenance Sales Solutions
    2. AI-Enhanced Supply Chain Optimization
    3. Intelligent Product Recommendations

    Conclusion

    The integration of AI in sales technology is set to bring unprecedented efficiency and effectiveness to sales processes. From hyper-personalized outreach to advanced predictive analytics, these innovations will empower sales teams to better understand and serve their customers.

    In the manufacturing sector, AI will play a crucial role in predictive maintenance, supply chain optimization, and intelligent product recommendations, driving significant improvements in sales strategies and outcomes.

    Stay tuned as we continue to explore the exciting developments in AI and their impact on the future of sales!

    David is an investor and executive director at Sentia, a next generation AI sales enablement technology company and Salesforce partner. Dave’s passion for helping people with their AI, sales, marketing, business strategy, startup growth and strategic planning has taken him across the globe and spans numerous industries. You can follow him on Twitter LinkedIn or on Sentia Says.

    Tags: #manufacturing #CEO #BOD #AI #AIadoption #AIRisk #AIStrategy #Boardofdirectors #AItesting #AIusecase #AIplan #AIConsulting