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Category: CRMs

10 Ways you can use Augmented & Blended AI to Sell on LinkedIn – Now!

There are so many ways you can use AI in sales these days, it’s hard to know where to start, especially if you’re a newbie to all of the excitement. So, this article will focus on something almost everyone in the business world uses, knows, and understands how It can be used right now, on every business persons favorite, LinkedIn.

Using AI with LinkedIn selling can enhance your sales and marketing efforts by automating tasks, providing valuable insights, and streamlining processes. As noted in previous articles, to maximize AI’s usefulness it requires a combination of Blended AI* and Augmented AI** to maximize its impact.

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10 ways to use Augmented & Blended AI with LinkedIn

1.    Personalized messaging: AI-powered tools can analyze data from LinkedIn profiles and interactions to create personalized messages that resonate with individual prospects, increasing engagement and response rates.

2.    Relationship insights: AI can analyze communication patterns and interactions with prospects to provide valuable relationship insights, such as the best times to reach out or the most effective ways to engage with specific individuals.

3.    Lead generation and prospecting: AI algorithms can help identify and prioritize potential leads based on specific criteria identified in your sales process, such as job titles, industries, or geographic locations, making your prospecting efforts more efficient.

4.    Sales forecasting: AI-driven analytics can analyze historical data, sales patterns, and market trends to provide more accurate sales forecasts, helping you plan and strategize your selling efforts better.

5.    Competitor analysis: AI can gather data on competitors’ activities and strategies on LinkedIn, providing valuable insights to refine your own selling approach and stand out in the market.

6.    Performance tracking and analytics: AI-driven analytics can measure the effectiveness of your LinkedIn selling efforts, tracking key performance indicators (KPIs) like response rates, conversion rates, and engagement metrics. This data allows you to continuously improve your strategies.

7.    Content recommendations: AI can analyze user behavior and preferences to suggest relevant content, such as articles, blog posts, or videos, that align with your prospects’ interests and pain points.

8.    Social listening and sentiment analysis: AI tools can monitor conversations and discussions on LinkedIn to gauge the sentiment around your brand and industry. This information can help you tailor your messaging and identify potential opportunities or challenges.

9.    Automated follow-ups: AI-powered sales automation tools can handle routine follow-up tasks, sending timely messages and reminders to prospects, helping to maintain engagement and nurture leads.

10.Chatbots for instant responses: Implement AI-driven chatbots on your LinkedIn profile to provide instant responses to common questions, qualify leads, and engage with prospects at any time, improving customer experience and response times.

There is no single AI tool (yet) that can accomplish all ten use cases noted on my list and it isn’t necessarily in order of usefulness. Instead, each seller must ascertain what is most important in their sales efforts and then use Blended AI to attack their sales patch in a coordinated and concise manner. Best practices are to pick a vendor that know what is actually working to advise when to use it and when not to, because:

The key isn’t using AI.

The key is using the right AI with your already successful, winning Sales Process and applying the right form of AI at the right stage in the sales process. That is the Winning Ticket!!!

Bottom line

By leveraging Augmented and Blended AI in your LinkedIn selling efforts, you can streamline working sales processes, deliver a significantly more personalized experience for your target audience, and make data-driven decisions (not just gut feelings), ultimately increasing each seller’s success rate by connecting with the best prospects and converting them into valuable customers.

*Blended Artificial Intelligence Is the amalgamation of different artificial intelligence techniques or approaches to achieve more robust and effective solutions. It involves integrating multiple AI models, algorithms, and / or technologies to leverage their respective strengths and compensate for their weaknesses. See Wikipedia:

** Augmented artificial intelligence is a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences.

December 14, 2023
5 Reasons VCs Want You to Use the best CRM / AI Sales Technology for Startup Success
Venture Capitalists weren’t born in the Matrix but sometimes it can feel that way if you’re a startup looking for capital.

They will invest in startups that have the right idea and key people to solve a big hairy problem with a large amount of potential revenue or users, needed for a successful VC exit. Whether its in the Metaverse or Matrix all VC’s want one thing.

Startups must scale very quickly to be successful.

Developing a scalable sales tech stack takes time and requires a very flexible and rapidly scalable CRM platform to manage all the sales tech data pipes. Startups must focus on keeping things as simple as possible for end users but still give them the right actions to do at the right time. This can be expensive for startups since they are often forced to pivot as they learn what is working or not. By building flexible and scalable tech systems from the beginning their odds of succeeding improve dramatically.

Entrepreneurs must consider what a VC or Investor maybe looking at when evaluating their company for a potential investment – at least from “What are they Going to Sell” perspective.

With that in mind. This is what VCs are looking for when evaluating your startup sales tech stack:

5 Reasons Why VCs Want You to use CRM / Sales Technology
1. Improved Revenue: A high-quality CRM properly implemented can help a Startup manage its sales pipeline more efficiently, resulting in improved sales and revenue. This helps boost the startup’s valuation and obviously more attractive to investors.
2. Superior Customer Engagement: A CRM can help a startup improve its customer experience by providing insights into customers behavior, preference and needs. This can help the startup develop better products and services, resulting in increased customer loyalty and higher revenue.
3. Operational Efficiency: A high-quality CRM can help a startup streamline its sales and marketing processes, resulting in improved operational efficiency. This can lead to cost saving and increases profitability, which can make the startup more attractive to investors.
4. Data Insights: A CRM can provide valuable data insights that can help a startup make more informed business decisions. This can help the startup develop more effective strategies, leading to improved revenue and profitability.
5. Scalability: A top CRM can help a startup scale its sales and marketing efforts more effectively and quickly. This can help the startup expand its customer base and grow its revenue, which can make it more attractive to investors.
Investors want startups to succeed or fail fast!

Their investment window is dependent on some of their investments growing quickly and that is where they need to focus, while others will fail and need to get off the books/harvest patents.

Many VCs prefer that you use Salesforce as it is the number one selling CRM.

They know they can get people to configure and implement the system, most sales people are familiar with it and it’s enterprise grade so scaling isn’t a big concern. It ticks a big funding box.

Next – whether a VC prefers a startup with Salesforce CRM or no CRM would depend on several factors. In general, if a startup already has a Salesforce CRM, it often indicates that they already have a well defined sales process and are using data to inform their decision-making. This makes their evaluation easier as they can see actual numbers vs. a hockey stick graph.

However, the absence of a CRM doesn’t always mean a startup is less attractive. VC’s maybe willing to invest in early stages of development – especially if a clear value proposition is backed by a clear sales process tracked to achieve rapid growth. Often VC’s have favorite CRM implementation partners that can quickly come in and set things up because they have a repeatable process in place.

One other way VC’s use CRM’s is to look at the numbers every day. Leading CRM AI packages (Such as Sentia) show VC’s every one of their investments in a simple dashboard showing daily sales, outbound activities and tracking to quota. They don’t have to call the CEO, they see it in real time. Most VCs like this because it reduces surprises and uncertainty.

Ultimately, whether a startup has a Salesforce CRM or no CRM it’s only one factor that VCs consider when evaluating potential investment opportunities. The most important factors are the startups overall business plan, the problem they plan to solve, quality of chosen team, potential for growth and profitability, and often most importantly how does your startup fit in the VC investment strategy and ten-year plan.
December 13, 2023
Top 10 New Applications of AI in Sales tech ’23
2023 will go down as the year of AI in business school history books. Rarely has a new technology evolved so quickly, gained wide spread use nor stirred as much concern as Artificial Intelligence. Regardless of where people stand on the use of AI (see 1984 “The Terminator“) it is already well entrenched in the business world and its use is only going to expand.

In this regard I’m going to focus on the top ten applications of AI in Sales systems so far. Sales tech stacks are usually the first ones impacted in any business organization because it is the most visible profit (or loss center). There is zero doubt that AI is rapidly transforming the sales landscape. We’re seeing the introduction of so many innovative applications that enhance efficiency, improve customer experiences, and boost sales performance its hard to know where to start, nor completely understand what is coming.

With that being said I’m going to put a stake in the proverbial ground and start with what I think are the top 10 things I see AI doing right now that are dramatically affecting sales systems:

2023’s Top 10 new applications of AI in sales tech:
1. AI-powered Sales Coaching and Training: Coaching tools can analyze every sales conversation or interaction, identify areas for improvement, and provide real-time feedback for sales reps. This AI augmented and personalized approach helps reps refine their communication skills, build stronger relationships with potential customers, and close more deals. It does this without a manager, trainer or supervisor needing to spend “time” with a sales rep. It can be self sourced and used when the rep wants it or by a manager to see how a rep is performing without having to have those “difficult conversations” where a customer calls and says XYZ is terrible, you need to get rid of them! It is no longer a she said, he said as it is all recorded.
2. AI-driven Predictive Sales Forecasting: AI algorithms analyze historical sales data, market trends, and customer behavior to predict future sales performance with greater accuracy (this assumes clean data!). This enables sales teams to make informed decisions about resource allocation, target customer segments, and sales strategies. As always the better the data that is input (Sales Reps are notorious for not using their CRMs) the more likely AI can improve predictive forecasting.
3. AI-enabled Lead Qualification and Prioritization: AI models assess leads based on various factors, such as firmographic data, website behavior, and engagement patterns, to identify the most promising leads. This helps sales reps focus their efforts on the most qualified leads, increasing their chances of closing deals.
4. AI-powered Sales Content Generation and Personalization: AI tools can generate personalized sales and marketing content, such as email templates, social media posts, and sales pitches, tailored to the specific needs and interests of each buyer persona/prospect. This personalized approach can increase engagement and conversion rates.
5. AI-driven Conversational Sales Chatbots: AI chatbots can engage in real-time conversations with potential customers, answering questions, providing product information, and scheduling demos. This 24/7 availability and personalized interaction can enhance customer experience and capture leads early in the sales cycle.
6. AI-powered Sales Incentive Compensation and Optimization: AI algorithms can analyze sales data and performance metrics to determine optimal incentive compensation plans that motivate reps and drive sales growth.
7. AI-enabled Sales Territory Planning and Optimization: AI models can analyze market data, customer demographics, and sales performance to identify optimal sales territories that maximize revenue potential.
8. AI-powered Sales Prospecting and Discovery: AI tools can identify and prioritize potential customers based on various factors, such as firmographic data, website behavior, and social media engagement, helping sales reps uncover new opportunities.
9. AI-driven Sales Opportunity Management and Deal Coaching: AI can analyze sales opportunities, identify buyer intent, potential risks and roadblocks, and provide real-time coaching to sales reps to increase their chances of closing deals.
10. AI-powered Sales Workflow Automation: AI tools can automate repetitive sales tasks, such as data entry, email follow-ups, and scheduling appointments, freeing up reps to focus on more strategic activities.
The key element to all ten of these AI use case applications is “CAN.” Every single one of these requires a blended AI experience to make it work. None of these can successfully operate without human interaction either in the development, use or tweaking of them. In fact AI is so new in most of these applications that if you don’t have a trained human available to constantly watch, improve and tweak any of these AI’s on a regular basis I would be very concerned about deploying them.

With that in mind we need to focus on the number one tool that most sales reps both hate and must use on a fairly regular basis, CRM (Salesforce says 26% daily usage for CRMs). CRMs or Customer Relationship Management software is an $80Billion dollar business but the failure rates are surprisingly high. I wondered if AI could solve this problem and turned to CRM experts for the answer.

I spoke with renowned CRM expert and best selling Author Mike Hudlow (see “How not to F*CK up a CRM system“) on these top ten AI solutions and asked him – What is the biggest problem with CRM systems right now?

Mike said: “”Human Change Management” is the key reason systems fail. But, unfortunately there are many reasons that this can occur. The reality is that people like to blame the technical system itself but any modern CRM system is capable of improving a companies CRM process/workflow. The challenge for any program/product/project manager, CIO, CTO, CMO or person in charge of the CRM to make it both welcomed and desired. Sounds scary human doesn’t it? A CRM can and will be resisted by: 1) those that don’t trust it, think its needed and/or believe that others will steal their own personal IP, 2) those divisions within a company that think it is too expensive, 3) People or teams that believe that the system will replace another system to which they are attached (career or emotionally ), 3) or people that actually or perceptually believe it creates more work, and 4) leaders that feel it infringes upon their own autonomy.”

My second question to Michael was can any of these top ten AI enhancements solve those problems?

Mike replied: “At the end of the day an AI supplemental tool can improve many of these as it makes the native CRM tool “bend” to their own style and will make them more comfortable with the AI’s ability to bridge the gap, bend to their own style, and allow them to let the CRM system they don’t feel comfortable with be more “human” and meet their own specific needs”

I then turned to AI Visionary and Sales Process Expert David Forder for an AI perspective on CRMs and AI. My question was simple yet nuanced. Can AI solve the biggest problems in CRM i.e., no sales rep wants to use it?

David replied: “The biggest issue that CRMs face is a lack of user adoption. 74% of customer facing staff refuse to use their CRM every single day. Yet companies spend billions of dollars on it! Adding AI into the mix has the potential to make this even worse! Unless any AI integration improves the underlying complexity of using a CRM and makes them simpler and more effective it will definitely make it worse not better! This has to be the focus of AI.”

I then asked Mr. Forder – how do you make it simpler? Get sellers using it?

David’s reply: “The key to making the CRM simpler is to ignore what will appeal to C-Suite i.e. ignore the focus on data heavy screens, reports and dashboards and re-engineer the CRM to meet the needs of the bulk of the users – those customer facing teams. Design the CRM to meet their daily needs, so they want to use it as it does the work for them. If you do this they will embrace their CRM and it can then work effectively for C-Suite as the data will actually be in the system! That’s our focus at Sentia AI! Have your CRM do most of the work for them, tell them what to do next and watch usage rates explode!”

Both Mike’s and David’s battle scars, extensive CRM implementations, sales experience and knowledge made me think I should interview both of them and get their thoughts on video for everyone to learn from. So we’re going to do it and here is the link:

How Not to F*CK Up your CRM with AI

This may turn into two video segments as the topic is so timely, important and I found that the breadth and depth of their CRM and AI system implementation experience was quite simply amazing!

These gentlemen have their finger on the pulse of sales, AI & CRM implementation success. It will be a great how to “fix it” roundtable.

Please join us on November 29th for an in-dept on how not to F*CK up A CRM system with AI!

David is an investor and executive director at Sentia AI – The Intelligence Engine, 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.
December 7, 2023
Top Tips to Select the Best AI Enabled Salesforce Partner
Unless you have been like the proverbial Ostrich with its head under the sand AI is here and making its presence felt quickly! Within a couple of years it will no longer be which company is using AI, rather it will be which company isn’t?

Since sales has the most scrutiny and oversight within the majority of companies (see CRO’s Get Fired First) it is often the first place AI is applied, for many good reasons. Sales rep turnover is usually high, there is lots of training (see Blended AI for Sales Rep Training) that needs to take place and companies spend a small fortune on hiring, training and getting reps “field ready). Also reps want new tech that works (if it doesn’t they will refuse to use it) and are used to being the first to use it.

AI is that “new” tech and many companies have already put AI implementation strategies into place (See CEO’s Must have an AI Strategy Plan) and those that don’t will, or they can fall behind competitors. The biggest challenge is AI is so new that top quality talent is hard to source, evaluate and hold onto. Many organizations (especially those using the number one CRM Salesforce) want to use AI strategy consultants that are also Salesforce partners. They know that these certified partners are the key to plan, strategize, and successfully implement AI.

The biggest challenge is finding the right one. There aren’t many case studies or client testimonials out there (yet) since it is so new. Falling back on past partner evaluation techniques are a starting point. However, the most advanced AI Salesforce partners are often smaller and harder to find (See Sentia AI).

With that in mind here are 7 high level tips for Selecting the right Salesforce partner with AI capabilities. Organizations looking to leverage artificial intelligence within their Salesforce ecosystem often have to look far and wide to find them. The following tips will act as a guide to help you in making an informed decision:
1. 1. Check AI Certifications and Expertise: Ensure that the Salesforce partner possesses relevant certifications and a track record in AI. Look for certifications or experience with Salesforce Einstein Analytics and Discovery Consultant, which demonstrate expertise in leveraging AI within the Salesforce platform. If you’re not going to use Einstein / Discover – it isn’t as important.
1. 1. Evaluate AI Use Cases and Experience: Assess the partner’s experience in developing as well as implementing AI solutions, especially within the Salesforce environment. Look for case studies or examples of successful AI integrations that align with your business needs. A partner with diverse AI use cases demonstrates versatility. Don’t pass on an AI Salesforce certified partner if they have limited or even no case studies – remember AI is in its infancy – be on the people at the company first and foremost, not slick marketing materials.
1. 1. Assess Industry-Specific Knowledge: Industry expertise is crucial for understanding the unique challenges and opportunities within your sector. Choose a partner with a deep understanding of your industry, as this knowledge will be beneficial when tailoring AI solutions to your specific requirements.
1. 1. Check Integration Capabilities: AI solutions often require seamless integration with existing systems. Ensure that the Salesforce partner has experience integrating AI capabilities with Salesforce and other relevant tools in your technology stack. This capability is crucial for a cohesive and efficient implementation.
1. 1. Verify Data Security and Compliance: AI involves handling sensitive data, so it’s vital that your Salesforce partner prioritizes data security and complies with relevant regulations. Request information on their data protection measures and ensure they align with your organization’s compliance requirements.
1. 1. Evaluate Training and Change Management Support: Assess the partner’s approach to training and change management. AI implementations often necessitate a shift in processes and workflows, so your partner should provide comprehensive training programs and support for change management to ensure successful adoption.
1. 1. Consider Scalability and Future-Readiness: Select a partner with a focus on scalability and future-readiness. The AI solutions implemented should be scalable to accommodate your organization’s growth, and the partner should stay abreast of advancements in AI technology and Salesforce updates to ensure ongoing relevance.
Bonus Tips: Look for all in one help. Salesforce partners that can evaluate your business needs, sales process, configure Salesforce, then train your people are far more valuable than having to go out and find 3 separate partners to do each phase. It will cost a lot more in both time and money to do this and perhaps most importantly something is almost always lost with every handoff along the way. Also make sure the partner is going to be with you long term – AI is so new it is guaranteed to require tweaks, adjustments and outright replacement along the way. A partner with a history of run and gun is the last one you need right now. Check references to ensure they will work with you long after the sale is completed.

Seek Client References and Feedback: Request references from the Salesforce partner’s past clients who have implemented AI solutions. Gather feedback on their experiences, challenges faced, and the overall success of the projects. This firsthand information can be invaluable in your decision-making process.

By considering these tips, doing your homework and keeping an open mind to new partners that are ahead of the game, you can enhance your chances of selecting a Salesforce partner with the right AI capabilities. Finding that special, highly qualified partner to effectively integrate artificial intelligence into your Salesforce environment is the key to your future AI success!
December 6, 2023
AI & Sales Process – Maximize Your Outcomes!

As a sales professional, one of the key skills that can make or break your success is how to manage and execute effective sales processes for B2B selling. In this post, I want to share some insights and best practices that have helped me grow my business and build strong relationships with my clients.

Firstly, it’s important to understand the different stages of a typical B2B sales cycle, which usually involves:

– Identifying and qualifying potential customers or prospects

– Establishing rapport and trust through communication and research

– Presenting and demonstrating your product or service features and benefits

– Overcoming objections and closing the deal

– Providing after-sales support and feedback to ensure customer satisfaction and loyalty

Secondly, it’s crucial to tailor your sales approach according to each stage of the cycle and the specific needs and preferences of your target market.

For example, you might need to use different types of communication channels, such as email, phone, video conferencing, or social media, depending on how you connect with your prospects. You also might have to adjust your tone, language, and content based on their level of interest, familiarity, or readiness to buy.

Thirdly, it’s valuable to leverage data and analytics to optimize your sales performance and improve your decision-making.

For instance, you can use tools like CRM software, marketing automation platforms, or lead generation services to track and manage your leads, opportunities, and deals. You can also analyze the feedback and results of your sales activities using metrics such as conversion rates, response times, closing ratios, or customer lifetime value.

By following these insights and best practices, you can increase your chances of success in B2B selling and create more value for yourself and your customers.

So how can AI assist with this?

AI can help B2B selling processes in various ways, such as:

– Automating and streamlining repetitive or time-consuming tasks, such as data entry, lead scoring, or email follow-up. This can save you time and effort and allow you to focus on more strategic or creative aspects of your sales.

– Enhancing and personalizing your communication with prospects and customers by using natural language processing and generative models. For example, you can use AI to generate customized messages, emails, or scripts that adapt to the context, tone, and style of each interaction. This can help you build rapport, trust, and credibility with your audience.

– Improving and optimizing your decision-making by using machine learning and deep learning models. For example, you can use AI to analyze customer data, behavior, or feedback and provide insights that can inform your sales strategy, pricing, or offer. You can also use AI to predict customer needs, preferences, or behaviors and tailor your products or services accordingly.

– Innovating and creating new value by using artificial intelligence and robotics process automation. For example, you can use AI to design, prototype, or test new features or solutions that can differentiate your product or service from the competition. You can also use AI to generate content, such as blogs, articles, or videos, that can showcase your expertise and authority in your industry.

November 25, 2023
Demystifying AI: Shared Tenancy vs. New Siloed LLMs – Why Understanding Matters to You!

G’day readers! Today, let’s delve into the world of artificial intelligence and unravel the complexities of shared tenancy models versus what I like to call siloed LLMs. As a thought leader in this space, I’m keen to shed light on why grasping these distinctions is crucial, especially considering the concerns I have about many SaaS companies seemingly not taking these matters as seriously as they should and offering solutions that are not truly mitigating the risks.

The AI Landscape: A Bird’s Eye View

Artificial Intelligence has become the driving force behind innovation, transforming the way businesses operate and make decisions. Within this AI landscape, most companies that offer AI, utilize APIs to shared “Large Language Models” (LLMs). I see a distinct issue with this and would like to explore the alternative approach that has emerged – siloed LLMs and other AI models.

The Shared Tenancy Conundrum:

Shared tenancy models involve multiple organizations sharing a common AI model fed with data from their respective enterprise systems. It’s like a shared workspace for AI, where diverse data mingles in pursuit of collective insights. However, despite this, concerns arise, particularly in the context of data security and obvious patterns emerging.

Why I Stand as a Thought Leader:

As a thought leader in the AI domain, my concerns echo through the corridors of innovation. I’ve observed a trend among many SaaS companies claiming to have tackled the challenges associated with shared tenancy models via the use of anonymizing data and using encryption. However, the reality often falls short of the assurances, leaving organizations vulnerable to potential risks. For example, if a model is asked by several staff members to draft a response to an email, the AI could potentially discover patterns in the content of all the emails being piped through the AI model (from staff in that organization) and then deduce from there, even though specifics on individuals has been redacted.

Data Security: A House of Cards?

Shared tenancy models walk a fine line when it comes to data security. While encryption layers are touted as safeguards, the intermingling of data from different organizations raises red flags. My qualms stem from the fact that, in practice, not all companies are as fortified against breaches as they profess to be. The shared nature of these models poses a risk – a breach in one organization’s data could ripple across the shared space, affecting others in the consortium.

Customization Woes: One Size Fits None:

A shared model, by its nature, tends to be a jack of all trades, master of none. My concerns deepen when considering the limited room for customization. Organizations, each with unique objectives, find themselves constrained by a model that might not align seamlessly with their specific needs. This lack of adaptability can stifle innovation and hinder the full potential of AI for individual organizations. A powerful integration of AI would mean the appropriate use of multiple different AI models or even better, a series of trained models that are tailored to the organization.

Siloed LLMs: An Oasis of Security and Customization:

Contrastingly, siloed LLMs stand as a beacon of security and tailored precision. As a thought leader in this space, I champion the cause of siloed models within secure containers for individual organizations. The benefits are clear – enhanced data security and the freedom to fine-tune the AI model according to the distinct requirements of each organization, coupled with the use of “Vector” storage, will dramatically improve the benefits of AI in a given use case.

Why Understanding Matters to You:

Now, why should you care about this AI showdown? As businesses increasingly integrate AI into their operations, your data security and the adaptability of the AI tools at your disposal matter. If you’re relying on a shared tenancy model, it’s vital to scrutinize the assurances provided by your AI service provider. I implore you to question whether your organization’s valuable data is truly shielded from potential breaches, and if the promised customization aligns with your unique goals. This may be hard to ascertain from your vendors, especially when their marketing and comms touts “Trust” but falls short on the delivery.

My Final Thoughts:

In the fast and ever-evolving landscape of AI, understanding the nuances between shared tenancy models and siloed LLMs is not just an academic exercise – it’s a practical necessity. My concerns about the nonchalant approach of many vendors are a call to action. As you navigate the AI terrain, consider the path that aligns best with your organization’s security and customization needs. In this journey of demystifying AI, your informed choices pave the way for a more robust, secure, and tailored AI experience.

If you would like to talk to an AI professional, please feel free to reach out to me (or the team at Sentia AI) so we can discuss your current and potential future state with AI.
[email protected]

November 23, 2023
Paul O’Sullivan, Salesforce: Transforming work

In the wake of the generative AI (GenAI) revolution, UK businesses find themselves at a crossroads between unprecedented opportunities and inherent challenges.

Paul O’Sullivan, Senior Vice President of Solution Engineering (UKI) at Salesforce, sheds light on the complexities of this transformative landscape, urging businesses to tread cautiously while embracing the potential of artificial intelligence.

Generative AI has stormed the scene with remarkable speed. ChatGPT, for example, amassed 100 million users in a mere two months.

“If you put that into context, it took 10 years to reach 100 million users on Netflix,” says O’Sullivan.

This rapid adoption signals a seismic shift, promising substantial economic growth. O’Sullivan estimates that generative AI has the potential to contribute a staggering £3.5 trillion ($4.4 trillion) to the global economy.

“Again, if you put that into context, that’s about as much tax as the entire US takes in,” adds O’Sullivan.

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses.

Read More

November 23, 2023
What Happens When A Sales Rep Takes His CRM Out For A Drink.

November 15, 2023
Microsoft adds Copilot for Sales & Service!

The Copilot for Service application integrates with customer relationship management (CRM) applications, and contact or call center applications — including Salesforce, ServiceNow, and Zendesk — to provide “guidance,” said Microsoft. The program can be trained to retrieve answers from a company’s knowledge base and from the CRM and contact center application repositories.

The results, said Microsoft, are “AI-guided answers and resources personalized for each customer issue and conversation.”

Further customization of functions is made possible by Copilot Studio, a development application also unveiled at Ignite.

The Service application, expected to be available “in early 2024,” is priced at $50 per user per month.

The second application, Copilot for Sales, is an “evolution” of a program unveiled for Dynamics 365 this summer called Sales Copilot. Enhancements to the program include integration with Microsoft Word.

“Sellers can prompt Copilot to create a meeting preparation brief in Microsoft Word, automatically populated with customer information such as an account and opportunity summary, names and titles of meeting participants, open tasks, highlights from recent meetings and email threads, and more,” said Microsoft.

Another integration is with Microsoft Teams, which “can surface action items and tasks, conversation key performance indicators (KPIs), and sales keywords.”

Copilot for Sales has the same pricing and availability as Service.

Both the Service and Sales applications are included with Microsoft 365 Copilot.

In addition to Service and Sales, Microsoft announced additional features for Dynamics 365. For example, in Dynamics 365 Sales is gaining a feature for a salesperson to “use natural language or pre-built prompts to gain a quick understanding of customers, deals, meetings, forecast, and more,” said Microsoft.

Read More

November 15, 2023
Salesforce Unveils New AI-Powered Sales Innovations to Accelerate Productivity and Growth
Salesforce has introduced new AI innovations to sales teams to narrow the gap. These innovations include:
- Generative AI-powered sales assistant: Copilot for Sales gives sellers an AI assistant to research accounts, update their CRM, and prepare for meetings from anywhere, including their mobile device and browser.
- AI-powered Call Summarization and Exploration: Automatically extracts call details, helping reps create and share meeting summaries with their teams.
- Sales Email: Uses generative AI to create personalized outreach emails for common sales scenarios, such as introductions or follow-up notes, in the seller’s unique tone and style.
- Automated Prospecting: Powered by Data Cloud – which unifies internal Salesforce and external data in real time – Automated Prospecting creates prospect lists based on account segmentation, customer fit, and buyer engagement scores to help sellers engage customers when they’re most likely to purchase. These scores can include metrics such as recent company expansion and levels of relationships with decision makers.
- Process Intelligence: Sales operations teams can easily build data models from internal and external data sources to optimize performance indicators, such as win rate or attrition metrics.
- Sales Cloud Everywhere: Sellers can bring the power of Sales Cloud to Outlook, Gmail, and web browsers to effortlessly manage opportunities, update accounts, and look up CRM data from wherever they’re working. Additionally, it streamlines email communication by auto-generating and sending emails directly from these platforms.
These innovations are powered by unified data that powers trusted AI, and all sales processes are integrated on a single platform. This allows sales organizations to transform how they work and shift their attention from processes to connecting with people, giving them more time to focus on what they do best – selling.

Read More
November 15, 2023