Tag: AI

AI Revolution in Sales: How CROs Are Leveraging Technology to Extend Their Short Tenures
Chief Revenue Officers (CROs) are increasingly leveraging AI tools to stay competitive and drive revenue growth. Some think salespeople don’t like tech due to the low daily use rates of many CRMs. In reality, they are often first adopters- looking to get an edge. What they won’t put up with is software, AI or otherwise that doesn’t help them do a “thing” faster or better than what it might replace.

In that regard I posit that CRO’s aren’t technology adverse. Instead, their laser focused on tool and time ROI. If this tool doesn’t work as sold to me, I will not use it, nor waste my time and possibly get fired.

With that sage truth in mind, here are some specific AI tools that CROs are using:

Sales Process Optimization
1. Panoramata: This AI-powered tool provides a comprehensive view of competitor activities, including email campaigns and landing pages, helping CROs stay ahead of market trends1.
2. Glassbox: Its AI-powered Insights Assistant (GIA) analyzes user interactions and provides actionable insights to improve website performance and conversion rates2.
Customer Analysis and Engagement
1. Kompyte: This tool uses AI to monitor competitor data in real-time, tracking websites, reviews, content, social media, ads, and job posts. It helps CROs identify emerging competitors and create “battle cards” for quick competitor analysis1.
2. OptiMonk: This platform uses AI to enhance website personalization efforts, generate multiple variations of copy for headlines and descriptions, and create smart popups that customize messaging based on visitor behavior2.
Predictive Analytics and Testing
1. Evolv AI: This platform specializes in AI-driven experience optimization, using machine learning algorithms to conduct experiments and evaluate multiple variations to identify the most effective combinations for optimal user experience2.
2. Unbounce: Its Smart Traffic feature uses AI to automatically route visitors to the page where they’re most likely to convert, potentially increasing conversions by up to 30%3.
Sales Enablement and Performance
1. Attention Insight: This AI-powered platform provides predictive attention heatmaps to validate design concepts for ads, landing pages, and apps before launching, helping improve user experience and conversion rates3.
2. Sentia AI: A Smart AI Enablement layer that sits on top of CRMs & ERPs creating a bes in class CX, with 40 embedded AI features to tell sellers what to do next based on AI scoring.
3. AI-enhanced RFP vendors: These tools help CROs quickly create, send, and receive RFPs online to more efficiently select vendors, saving time and money in the process5.
Industry-Specific AI Applications for CROs

SaaS and Technology
AI-powered product recommendation engines analyze user behavior to suggest relevant upgrades or complementary products, increasing upsell opportunities.

Financial Services
AI algorithms assess credit risk and customer financial profiles to offer personalized financial products, improving conversion rates and reducing default risks.

E-commerce
AI chatbots provide 24/7 customer support, handling inquiries and guiding customers through the sales funnel, enhancing the overall shopping experience.

Healthcare
AI analyzes patient data to identify potential candidates for clinical trials or new treatments, streamlining the sales process for pharmaceutical companies.

Real Estate
AI-driven virtual property tours and predictive analytics help agents identify high-potential leads and tailor property recommendations, increasing closing rates.

Manufacturing
AI optimizes inventory management and predicts maintenance needs, allowing sales teams to proactively offer replacement parts or services.

New AI tools pop up daily. Tools that work are always in demand and CRO’s are always looking for an edge. By evaluating, trialing, and when ROI is there adopting these AI tools, CROs can optimize their sales processes, enhance customer engagement, improve decision-making through data analysis, and ultimately drive revenue growth in an increasingly competitive landscape.

Please tell me which CRO / Sales tools you use that we should consider in the comments section below.

Citations:
SEO Tags: AI in sales, Chief Revenue Officer, CRO tenure, sales automation, AI-driven decision making, industry-specific AI applications, sales efficiency, revenue growth
March 20, 2025
What the Hell are / is / an: AI Agent(s)?
AI Agents the next Frontier in artificial intelligence or just more AI Hype?

With Billions of venture capital dollars already sunk into it, the AI marketing buzz continues to build. The latest excitement centers around a term many cannot agree upon. AI agents are becoming increasingly prevalent or at least written and hyped more and more. Ask most people what an AI Agent is, and the first thing they think of is chatbot type things. But what exactly are AI agents, and how are they shaping our technological landscape or is it just a bunch of tech marketing hype?

What are AI Agents?

AI agents are autonomous systems or programs capable of performing tasks on behalf of users or other systems. They go beyond simple chatbots, incorporating advanced decision-making, problem-solving, and execution capabilities1. These agents utilize large language models (LLMs) as their core, enabling them to understand and respond to complex inputs while adapting to user expectations over time1.

Definitions Across Industries

While there’s no universally agreed-upon definition, several key players offer their interpretations:
- IBM defines AI agents as systems that can autonomously perform tasks by designing workflows and utilizing available tools1.
- OpenAI describes them as automated systems that can independently accomplish tasks for users2.
- Microsoft distinguishes agents from AI assistants, positioning agents as more specialized and tailored to specific expertise2.
- Salesforce definition: AI agents are a type of AI system that can understand and respond to customer inquiries without human intervention. They are created using an agent builder, like Agentforce, and rely on machine learning and natural language processing (NLP) to handle a wide range of tasks. These intelligent agents can include anything from answering simple questions to resolving complex issues — even multi-tasking. Most importantly, AI agents can continuously improve their own performance through self-learning. This is distinct from traditional AI, which requires human input for specific tasks.
Companies Developing AI Agents

Several tech giants are at the forefront of AI agent development:
1. OpenAI: Developing the Operator agent2.
2. Google: Working on Project Mariner2.
3. Anthropic: Claude – a generative AI chatbot and family of large language models (LLMs).
4. Perplexity: Created a shopping agent2.
5. Amazon: Developing AI agents for e-commerce applications4.
6. Salesforce: Aiming to become a leading provider of “digital labor” through various agentic services2. See Agentforce.
5 Real-World AI Agent Use Cases
1. E-Commerce: AI agents on platforms like Amazon provide personalized product recommendations, automatically place orders, and track shipping, contributing significantly to revenue generation4.
2. Sales and Marketing: AI agents like Find AI build lead lists, send personalized communications, and qualify leads, often outperforming human counterparts in lead generation4.
3. Customer Support: Advanced AI agents, such as those developed by Chatbase, can handle complex technical support issues, process refunds, and provide product recommendations, leading to substantial reductions in support tickets4.
4. Autonomous Vehicles: Self-driving cars represent a complex application of AI agents, combining multiple agent types for navigation, decision-making, and real-time responses. Companies like Waymo are pioneering this technology in cities like San Francisco and across the United States4.
5. Healthcare: AI agents assist in diagnosing medical conditions, analyzing medical images, and creating personalized treatment plans. For instance, Google’s AI agent has demonstrated superior accuracy in diagnosing skin cancer compared to human dermatologists4.
As AI agents continue to evolve, they promise to revolutionize various sectors, enhancing efficiency and providing personalized experiences. However, challenges remain in standardizing definitions and managing user expectations. The coming years will likely see further advancements and refinements in this exciting field of artificial intelligence.1 2 4

Citations:
March 17, 2025
AI Banking Use Cases. How are Innovative COO’s Winning?
As a Chief Operating Officer (COO) in the banking sector, embracing Artificial Intelligence (AI) is no longer optional—it’s a necessity for staying competitive in our rapidly evolving industry. Let’s explore how AI, particularly agentic AI and Einstein GPT, is reshaping the COO’s role and the top AI use cases in banking today.

The AI Impact on Banking COOs
Recent studies show that 77% of COOs using Large Language Models (LLMs) report positive ROI, with customized tools leading the way. This underscores the growing importance of AI in banking operations and the need for COOs to champion this technology to stay ahead of competitors.

Top 10 AI Use Cases in Banking
1. Fraud Detection and Prevention: Agentic AI systems excel at identifying fraudulent activities by scrutinizing transaction patterns and detecting anomalies in real-time7.
2. Customer Service Chatbots: AI-powered virtual assistants handle customer inquiries, with Einstein GPT enabling more personalized and contextually relevant interactions2.
3. Risk Management: Agentic AI facilitates regulatory compliance by automating monitoring and reporting processes, including anti-money laundering (AML) and “know your customer” (KYC) initiatives7.
4. Credit Scoring: AI models analyze a broader range of data for more inclusive and accurate creditworthiness assessments.
5. Personalized Recommendations: Agentic AI analyzes vast amounts of customer data to offer bespoke financial advice, empowering clients to make well-informed decisions7.
6. Automated Underwriting: AI-powered underwriting solutions streamline loan decisioning processes, enabling faster approvals5.
7. Investment Management: Agentic AI-powered portfolio management tools offer tailored investment strategies based on individual risk tolerance and financial objectives7.
8. Regulatory Compliance: Einstein GPT helps banks navigate complex regulatory landscapes by providing accurate and up-to-date information2.
9. Document Processing: AI automates the analysis and processing of financial documents, improving efficiency.
10. Operational Efficiency: Agentic AI is transforming banking by delivering autonomous, adaptive, and proactive solutions, streamlining operations and enhancing decision-making processes1.
New Use Cases and Latest Applications
1. Autonomous Trading: Agentic AI systems can execute trades with remarkable speed and precision, utilizing advanced algorithms to optimize returns7.
2. Customer Onboarding: AI streamlines the onboarding process using biometrics, ID verification, and data validation to speed up “Know Your Customer” (KYC) checks5.
3. Marketing Automation: AI Agents analyze customer behavior to help banks design targeted marketing campaigns, making customer segmentation and campaign effectiveness faster and more precise5.
4. Predictive Banking: Einstein GPT enables banks to offer personalized financial coaching tailored to individual behaviors1.
5. Compliance Refinement: Agentic AI can refine risk assessments in real-time, dynamically responding to emerging threats and anomalies1.
COO Checklist: AI Leaders vs. Laggards
AI Leaders:
- Implement customized AI tools, including agentic AI and Einstein GPT
- Establish an AI center of excellence (CoE)
- Invest in AI foundations and cloud infrastructure
- Focus on AI-driven efficiency gains and personalized customer experiences
- Leverage AI for complex decision-making and autonomous processes
- Continuously upskill staff on AI technologies, including agentic AI
- Partner with top-tier tech firms for AI development
- Use AI for predictive analytics and forecasting
- Implement AI-powered risk management systems
- Utilize AI for automated regulatory reporting and compliance
AI Laggards:
- Rely on off-the-shelf AI solutions
- Limited internal AI adoption
- Lack of strategic AI investment
- Focus solely on cost-cutting through AI
- Use AI only for basic tasks
- Minimal AI training for employees
- No significant tech partnerships for AI
- Limited use of AI in analytics
- Traditional risk management approaches
- Manual regulatory reporting processes
As an innovative COO working to stay ahead, it’s crucial to lead the AI transformation in your bank. By leveraging agentic AI and Einstein GPT across various operations, COOs can drive efficiency, enhance customer experiences, and position their banks for future success in the age of AI.

Are you leading the AI revolution in your bank or falling behind?

Share your experiences and thoughts in the comments below.

#AIinBanking #FinTech #BankingInnovation #AgenticAI #EinsteinGPT #OperationalEfficiency #aichecklist #aiadoption #AIAgents #financialservices

Citations:
March 4, 2025
Rise of the CAIEO: Navigating new AI Roles
AI continues to break new ground in many ways including new job titles for roles that didn’t exist a year ago. Let’s jump into the role of the CAIEO -part IT, HR, law, philosophy and business ethics rolled into one job in the age of AI.

Large complex organizations, that have embraced AI, are increasingly recognizing the need for ethical oversight in their AI initiatives. Enter the Chief AI Ethics Officer (CAIEO) -bridging the gap between technological innovation and ethical responsibility.

What is a CAIEO?

A Chief Artificial Intelligence Ethics Officer is a senior executive responsible for ensuring that an organization’s AI technologies and strategies align with ethical principles, legal requirements, and societal values. They play a pivotal role in developing, implementing and monitoring responsible AI practices across the company.

10 Key Responsibilities of a CAIEO
1. Develop, codify, train and enforce internal AI ethics policies and guidelines1 4.
2. Oversee compliance with AI-related legal and regulatory frameworks4.
3. Promote transparency, fairness, and accountability in AI systems4.
4. Manage AI-related risks, including data privacy and security vulnerabilities4.
5. Collaborate with various departments to establish AI performance evaluation standards4.
6. Lead the creation and implementation of AI governance structures4.
7. Ensure AI initiatives align with the company’s values and ethical standards1 4.
8. Conduct regular reviews and updates of AI policies to reflect industry best practices4.
9. Provide expert advice on navigating ethical challenges in AI development and deployment4.
10. Foster a culture of responsible AI use throughout the organization1 4.
Who Needs a CAIEO?

Most businesses really don’t need a CAIEO, yet. Organizations that heavily rely on AI technologies, have outsize legal exposure to their use of AI, or are planning significant AI initiatives should consider appointing a CAIEO.

This role is particularly crucial for:
- Large tech companies developing AI products or services – Be Good, Do Good!
- Financial institutions using AI for decision-making
- Healthcare organizations implementing AI in patient care
- Retail companies leveraging AI for customer insights and personalization
- Manufacturing firms integrating AI into their production processes
- Large service-based companies using AI in human resources, hiring, firing, compliance
While the need for a CAIEO is more pronounced in larger corporations, mid-sized companies with significant AI investments should also consider this role (but can be a job that doesn’t necessarily require a C-Suite title). Startups focusing on AI-driven products or services may benefit from having a CAIEO or a similar role to ensure ethical considerations are built into their core offerings from the outset.

CAIEO Necessity Checklist

Consider appointing a CAIEO if your organization:
- Develops or uses AI systems that impact decision-making processes
- Handles sensitive data processed by AI algorithms
- Operates in a highly regulated industry (e.g., finance, healthcare)
- Has a global presence with diverse ethical and regulatory landscapes
- Faces potential reputational risks related to AI use
- Aims to be a leader in responsible AI practices
- Lacks internal expertise in AI ethics and governance
- Experiences rapid growth in AI adoption across multiple departments
- Receives stakeholder inquiries about AI ethics and transparency
- Plans to scale AI initiatives significantly in the near future
Real-World CAIEO Use Cases by Industry
1. Tech Giant: A leading social media company appoints a CAIEO to address concerns about algorithmic bias in content recommendation systems.
2. Financial Services: A global bank hires a CAIEO to ensure fair lending practices in AI-driven credit scoring models.
3. Healthcare Provider: A hospital network brings on a CAIEO to oversee the ethical implementation of AI in diagnostic tools and patient data management.
4. E-commerce Platform: An online marketplace employs a CAIEO to manage the ethical implications of AI-powered pricing and product recommendation engines.
5. Automotive Manufacturer: A car company appoints a CAIEO to guide the development of ethical AI systems for autonomous vehicles.
As AI continues to shape our world, the role of the CAIEO becomes increasingly vital. By prioritizing ethical considerations in AI development and deployment, organizations can build trust, mitigate risks, and ensure their AI initiatives contribute positively to society.

For More information on AI roles like CAIO please visit the Agentic AI Community site for AI enthusiasts and knowledge seekers.

#AIEthics #ChiefAIEthicsOfficer #ResponsibleAI #TechLeadership #AIGovernance

David is an investor and executive director at Sentia AI, 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 here on LinkedIn, X or YouTube for my interviews and videos.
February 27, 2025
AI in Big Pharma: How is it Being Applied in 2025?
Valued at $1.5 trillion, the pharmaceutical industry has long grappled with significant challenges. R&D development of a new drug costs $2 billion plus, takes over a decade, and faces a 90% failure rate in early stages. However, with AI adoption in life sciences growing at 29% annually, a unique opportunity emerges to address these inefficiencies and pain points in drug development1 2.

Pain Points in Pharma

Big Pharma executives and life sciences investors face uncertainty and risk due to four major issues:
- Disconnected Data: Decision-making relies on vast volumes of structured and unstructured scientific data.
- Knowledge Gaps: Significant gaps persist across scientific, clinical, regulatory, and commercial decision-making processes.
- Inefficient Processes: Integrating and analyzing datasets is time-consuming and resource-intensive.
- Lack of Standardization: Inconsistent evaluations hinder reproducibility in asset assessments.
AI Applications in Drug Development – Recent Examples

AI is transforming various stages of drug discovery, development, and commercialization:
1. Target Identification and Validation: Atomwise and Pfizer’s partnership reduced initial drug discovery timeline from 4-6 years to approximately 18 months.
2. Clinical Trial Optimization: Deep 6 AI’s implementation reduced patient recruitment time by 85% and increased trial enrollment rates.
3. Manufacturing Process Optimization: GSK and Gatehouse Bio’s collaboration led to a 25% increase in production yield and $20 million annual cost savings at a single site.
4. Post-Market Surveillance: FDA and Novartis’ AI-powered systems enabled earlier detection of safety signals and more proactive risk management.
CRM & AI in Healthcare

Customer Relationship Management (CRM) systems, enhanced by AI, are improving healthcare delivery and patient engagement:
- Personalized Patient Care: AI-powered CRM systems analyze patient data to provide tailored treatment recommendations and follow-ups.
- Predictive Analytics: These systems can forecast patient needs, potential health risks, and treatment outcomes.
- Streamlined Administrative Tasks: AI automates appointment scheduling, billing, and record-keeping, allowing healthcare providers to focus more on patient care.
- Enhanced Patient Engagement: AI-driven CRM tools facilitate better communication between patients and healthcare providers, improving adherence to treatment plans.
The AI Solution

AI addresses several critical issues in healthcare and pharma:
1. Data Integration: AI can seamlessly integrate and analyze vast amounts of disparate data, providing comprehensive insights for decision-making.
2. Accelerated Research: Machine learning algorithms can rapidly identify potential drug candidates and predict their efficacy, significantly reducing the time and cost of drug discovery6.
3. Improved Clinical Trials: AI optimizes patient recruitment, monitors responses, and predicts potential side effects, making trials more efficient and reliable5.
4. Personalized Medicine: By analyzing individual patient data, AI enables the selection of treatments tailored to each person, improving outcomes and reducing adverse reactions4.
5. Operational Efficiency: AI automates routine tasks and provides insights into market trends and patient needs, streamlining pharmaceutical operations2.
The Impact of AI on Drug Development

The benefits of AI in pharmaceutical industry are clear:
- 30-50% reduction in time-to-market for new drugs
- Estimated $100-200 million cost savings per successful drug
- Higher success rates in clinical trials due to better candidate selection
- More efficient use of research resources
- Improved safety monitoring and risk management
- Faster response to emerging health threats through drug repurposing
Looking Ahead

2025 is the AI “Go Year” for the entire Pharmaceutical Industry.

AI is expected to drive 30% of new drug discoveries, cutting costs and accelerating personalized treatments7. Challenges, including regulatory hurdles, data privacy concerns, and the need for specialized skills loom large! 3.

The future of pharma lies in embracing these AI-driven innovations while navigating the complex regulatory landscape. As the industry evolves, collaboration between tech companies, pharmaceutical firms, and regulatory bodies will be crucial in realizing the full potential of AI in healthcare and drug development.

What are your thoughts on AI’s role in transforming the pharmaceutical industry? How is AI changing your processes and workflows in pharma? Share your experiences and let’s continue this important conversation.

Connect with me so you don’t miss any future articles!

Please follow me on X (@Intmktentry) on Linkedin, YouTube or via our AI Community Site.

Citations:
February 24, 2025
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
January 29, 2025
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

January 14, 2025
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.
November 27, 2024
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
November 8, 2024

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

Improved efficiency, data capture & access, with blended AI productivity gains
More accurate sales forecasting and lead scoring – enhancing C-Suite confidence
Gold standard customer service experiences through personalization
Better data quality and insights
Reduced human error in data entry and analysis
Significantly reduced data entry and time devoted to CRM maintenance activities
Email writing, reply and auto CRM entry based on sales rep skill & role personalization
Personalized training based on AI assessment of activities and results for all reps
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
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
Cull ranking based on unbiased review of performance, potential, opportunities, training performance and client recorded sentiment analysis
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

Human Touch: Many aspects of customer relationships require human empathy, understanding and perceptual judgment that AI cannot replicate
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
Data Privacy Concerns: Regulations and customer trust issues may limit the extent of AI’s autonomy in handling sensitive data
Integration Challenges: Fully replacing existing CRM systems with AI would be a massive undertaking for most organizations (see current legacy system replacement timelines)
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

October 18, 2024

Tag: AI

Top 10 AI Use Cases in Banking

New Use Cases and Latest Applications

COO Checklist: AI Leaders vs. LaggardsAI Leaders:

AI Laggards:

Citations:

The Evolution of Salesforce AI Agents: Transforming Business Operations

Real-World Examples of Successful Agentforce Implementation

Checklist for CROs