Category: AI Labs

Whenever AI is mentioned in the workplace, there are normally concerns over data privacy, security, and compliance (and rightfully so).

So, how can marketers safely integrate AI into their work?

We answered this question by showcasing 3 AI use cases that protect your data, while still producing high-impact results.

It all happened last week in our event titled: “AI + Marketo: How to Implement 3 High Impact, No Risk Solutions”.

Hosted by: Andy Caron (President, RP), Lucas Machado (Director of AI & Automation, RP), and Tyron Pretorius (Owner, The Workflow Pro).

If you missed it, you can watch the FULL recording above!

Here’s a quick overview of what we covered.

Before we get into the specific use cases, we went through a few “AI Fundamentals” including the differences between general models and fine-tuned models, pricing, and compliance.

Then, we went deep on the ChatGPT-Marketo connection, including the use of webhooks, integration platforms, and the Marketo API.

After that, we covered 3 specific use cases (with a bonus use case at the end):
 

1. Sentiment Analysis

For this, we demonstrate how to perform a sentiment analysis of your Marketo emails using ChatGPT, leading to enhanced content that resonates with your audience and improves open rates, click-through rates, and conversions.

Follow along with the webinar or read our in-depth guide here.
 

2. Finding the Best Email Send Times

Here, we show how you can extract email interaction data from your Marketo instance and use ChatGPT analysis to answer the age-old question: When is the best time to send emails?
Follow along with the webinar or read our in-depth guide here.
 

3. Persona Classification

Traditional classification methods often fall short due to constantly changing job titles, industry terms, and other parameters. The good news is, we can create our own fine-tuned GPT that understands the patterns of these term changes, then integrate directly into Marketo for enhanced persona classification.

Follow along with the webinar or read our in-depth guide here to learn how it’s done.
 

4. Sales Acceleration (BONUS)

For our final use case, we show you how to integrate ChatGPT, Marketo, and your CRM with an IPaaS solution like Zapier or Workato to automatically generate reports for your sales team – instantly contextualizing MQLs so your reps can have effective conversations that close more sales.

Follow along with the webinar or read our in-depth guide here.

If you have any questions about integrating AI with Marketo, don’t hesitate to reach out to us!

In this guide, we’ll show you how to integrate ChatGPT, Marketo, and your CRM with an IPaaS solution like Zapier or Workato to automatically generate reports for your sales team, granting them instant insights into MQLs.

Why does this matter?

In sales, context is key. Especially when interpreting how and why a lead has become a Marketing Qualified Lead (MQL).

Yet, too often, the data behind that qualification is buried in layers of engagement metrics, making it very time-consuming for sales reps to decipher and piece together the story on their own.

This solution directly addresses that issue by allowing ChatGPT to analyze lead engagement data and generate a clear, concise summary of their journey toward qualification.

By achieving a deep understanding of each lead’s journey more efficiently, your sales team will always be prepared to have effective and informed conversations that close.

Who is this guide for?

Before we get into the nitty gritty, this guide is designed for those with basic knowledge of Marketo, as well as foundational knowledge in an integration platform of their choice (whether that’s Zapier, Workato, Microsoft Power Automate, etc.)

Let’s get into it!
 

Why IPaaS?

For those who may not know, IPaaS stands for “Integration Platform as a Service”. Integrating ChatGPT with Marketo and our CRM without an IPaaS would’ve involved more manual coding work, AWS, and so on, making this entire process far more tedious.

For that reason, we’ve opted to lean on an integration platform to do some of the heavy lifting. We’re using Zapier, but any other one should get the job done. Your exact IPaaS will likely depend on which one your company has invested in.

And since the terminology, UI, and feature set will differ slightly depending on what you’re using, we’re going to keep this guide IPaaS-agnostic – focusing more on the logic behind the workflow, as well as the code snippets you’ll need at each step.

Here’s a graphic of what the entire workflow will look like at a high level:

 

Step 1: Initial setup in Marketo

Start by creating a “text” field in your Marketo instance and make sure it’s mapped to your CRM for sales team visibility. This is where we’ll house the text summary written by ChatGPT that contextualizes MQLs for sales reps.
 

Step 2: Starting the workflow in your IPaaS

Now, we need to build the workflow that will fill the Marketo text field we just created. Head over to your integration platform and create a new workflow that is triggered by an HTTP request.

This trigger will be our first block in the flow:

 

Step 3: Retrieving data from Marketo

In this step, we’re going to tackle the next three blocks in the workflow:

There are a couple things we need from Marketo to extract lead engagement data that we’ll feed to ChatGPT later.

1) First, we need the Marketo Access Token. This will grant us access to the Marketo API, allowing us to programmatically talk to Marketo to enable the automation of tasks, integrate with other systems, and retrieve data.

Use this code to get the Marketo Access Token:

import requests
import pandas as pd
import json
MUNCHKIN = "EDIT HERE"
client_id = "EDIT HERE"
client_secret= "EDIT HERE"
leadid="EDIT HERE"
sinceDate="EDIT HERE"
gptAPIKey="EDIT HERE"
fieldName="EDIT HERE"
def get_access_token():
    global client_id
    global client_secret
    global MUNCHKIN
    params={'grant_type': 'client_credentials',
            'client_id': client_id,
            'client_secret': client_secret}
    headers={'Accept-Encoding': 'gzip'}
    url="https://"+MUNCHKIN+".mktorest.com/identity/oauth/token"
    response=requests.get(url=url,params=params,headers=headers)
    data=response.json()
    print(data)
    return data['access_token']

 

2) Second, we need the Marketo Paging Token. This is required to use the Activities API, which is used to interact with lead engagement data (opened web pages, attended webinars, etc.) We also use this to define the time frame for pulling engagement data. In Marketo, you can’t select any specific time frame; you can only set a start date (up to 3 months ago) and pull data from there to the present. In our case, we recommend setting that to 1 month ago.

Use this code to get the Marketo Paging Token:

url="https://"+MUNCHKIN+".mktorest.com/rest/v1/activities/pagingtoken.json"
token=get_access_token()
params={'access_token': token,
        'sinceDatetime': sinceDate}
response=requests.get(url=url,params=params)
data=response.json()
nextPageToken=data['nextPageToken']

 

3) Now, we’ll use the two tokens above, plus the lead ID from Step 2, to fetch all the engagement data from a specified back date. This will generate a large string of data that includes all the email interactions, web activity, program status, and more of a lead.

Use this code to fetch lead engagement data:

access_token=get_access_token()
def get_lead_activities(auth_token, lead_id, firstToken):
   url = f"https://"+MUNCHKIN+".mktorest.com/rest/v1/activities.json"
    params = {
        "access_token": auth_token,
        "leadId": lead_id,
        "activityTypeIds": "1,2,3,10,11,34,104",
        "nextPageToken": firstToken
    }
    activities = []
    more_results = True
    while more_results:
        response = requests.get(url, params=params)
        data = response.json()
        if 'result' in data:
            activities.extend(data['result'])
        more_results = data.get('moreResult', False)
        if more_results:
            params["nextPageToken"] = data['nextPageToken']
    return activities
all_activities = get_lead_activities(access_token, leadid,nextPageToken)
all_activities = str(all_activities).replace('"', "'")
activities=all_activities

 

Step 4: Analyze engagement data with ChatGPT

Let’s move on to the final two blocks of the workflow:

 

1) This is where we come up with an appropriate prompt about summarizing how and why a lead became an MQL, then send it to ChatGPT through the GPT-4o API.

Here’s a prompt that produced good results for us:

“Analyze the following lead activities and explain the activities that contributed to this lead being marked as MQL so a salesperson knows how they should approach the client, including which product or service this lead is most interested in and any other relevant insights. Include relevant URLs on form fills:" +activities+" – Remember this will only be read by a salesperson, so don't use technical explanations, just your best summary. Keep your response limited to 100 words.”

 

And use this code to send it to ChatGPT via the API:

def send_to_chatgpt(activities):
    url = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Authorization": gptAPIKey,
        "Content-Type": "application/json"
    }
    prompt = """Analyze the following lead activities and explain the activities that contributed to this lead being marked as MQL so a salesperson knows how they should approach the client, including which product or service this lead is most interested in and any other relevant insights. Include relevant URLs on form fills:""" +activities+""" – Remember this will only be read by a salesperson, so don't use technical explanations, just your best summary. Keep your response limited to 100 words."""
    data = {
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 250
    }
    response = requests.post(url, headers=headers, json=data)
    return response.json()
gpt_response = send_to_chatgpt(activities)['choices'][0]['message']['content']

 

2) Once we’ve prompted GPT, we need to capture the text summary it sends back to us and use an API request to store it in the Marketo text field we created in Step 1.

Use this code to capture and store ChatGPT’s response:

def update_marketo_field(lead_id, field_name, gpt_response):
    url = "https://+MUNCHKIN+.mktorest.com/rest/v1/leads.json?access_token="+str(input_data['access_token'])
    headers = {
        "Content-Type": "application/json"
    }
    payload = {
        "action": "updateOnly",
        "lookupField": "id",
        "input": [
            {
                "id": int(lead_id),
                field_name: gpt_response
            }
        ]
    }
    response = requests.post(url, headers=headers, json=payload)
update_response = update_marketo_field(leadid, fieldName, gpt_response)

 

Step 5: Automate the workflow

In our fifth and final stage, we need to ensure this IPaaS workflow is triggered any time a lead becomes an MQL in our Marketo instance. This is achieved by creating a webhook in Marketo that looks like this:

 

Example response:

 

And that’s it!

Now, whenever a lead becomes an MQL in Marketo, your IPaaS workflow should be triggered.

This will automatically send a full text summary of why that lead became an MQL to a Marketo text field that is also mapped to your CRM for sales reps to see.

Instant insights into the lead journey will save your sales reps tons of time they’d otherwise spend sifting through metrics to piece together the story.

Ultimately, they’ll be more informed more quickly, so they can have better conversations with prospects and close more sales.

If you need help setting this up or have any other questions, feel free to reach out here.

In this guide, we’ll show you exactly how to create a fine-tuned GPT model that’s integrated directly into Marketo for enhanced persona classification.
 
Persona classification plays a crucial role in successfully delivering targeted and personalized content to your audience.
 
However, traditional classification methods often fall short due to the dynamic nature of job titles, industry terms, and other parameters.
 
For example, Persona A may include a job title with the word “Tech”. Then, some months or even years later, that same job title drops “Tech” and uses “IT” in the title instead. This can be true for several different job titles and even entire industries.
 
All these words and terms must be constantly updated for your personas to accurately reflect the roles you want to target – and this takes a lot of manual work.
 
Put simply, marketers spend a lot of time defining personas only for them to quickly become outdated.
 

This is where GPT comes in

The good news is, we can create our own fine-tuned GPT that understands the patterns of these term changes.

It can take “Technology Analyst” and “IT Analyst” and feed them into the same group, for example. Then, when a new term like “Python” comes up in a job title, it’ll understand where to categorize that role based on your existing persona instructions and examples.

And since we’ll be training our model on job titles and industry terms rather than actual private identifiers, this application of GPT has no privacy or compliance risks whatsoever.

Here’s our guide on how to set it up!

(This guide is for tech professionals and enthusiasts who use Marketo. Not a lot of coding is required, but it would help to know coding basics or be willing to learn!)
 

Understanding persona classification

Before we get into the technical aspects of tuning your GPT model and integrating it with Marketo:

None of this will be very useful if you haven’t already taken the time to define your personas.

This almost goes without saying, but we still wanted to quickly mention it.

Defined persona groups based on job title, industry, pain points, and other characteristics are information that your fine-tuned GPT will need in order to automatically update and recategorize contacts when terms change.
 

1) Prepare your data for GPT fine-tuning

The first thing we need to do is prepare our data for fine-tuning. Let’s walk through what that looks like.

1. Start by reaching out to your Marketing and Sales teams and gather about 200 leads/customers that will be used as examples for a given persona. The job titles, industries, and other important characteristics of these contacts need to fit your previously defined persona as closely as possible.
2. Once you have those contacts, it’s time to clean the data. Eliminate any duplicate entries, weird characters, or other erroneous inputs.
3. Now, we need to transform this data into a JSONL format (for those who may not know, this is a JSON but without any commas). This will allow us to feed it into our custom GPT with instructions and descriptions about persona classification. Since this step is very important, we’ve created a resource that will help you through it. Follow the guide below:

How to convert your data to JSONL format

1. Open up this JSONL formatting tool we created and create a copy for yourself to edit.
2. The “System” column is where our instructions for GPT go. The message can be something like: “You are going to analyze lead job titles to fit them into the correct persona. I’ll send you the job title only, and you should respond with the persona classification only.”
3. In the “User” column, input the job titles of each of your contacts. In our example, we’ve put “Cloud Infrastructure Analyst” as the job title.
4. In the “Assistant” column, we are going to input the persona “type” that we want GPT to give us. In other words, when it receives the job title from the “User” column, we want it to categorize that as “Persona A” or simply “A”. Here is an example of what your first row should look like at this point:

    

 

5. From here, you can repeat the same “System” column message for every single row. Then copy and paste the rest of the job titles from your contacts in the “User” column, and “A”, “B”, “C”, etc. for the corresponding persona type as your output in every row of the “Assistant” column. You don’t need to touch anything in the “JSONL” column, as this has been set up to automatically populate based on the inputs from the other columns.
6. Once all your data is in there, we need to get it out of the sheet and into a .txt file. Simply copy and paste everything in the JSONL column and paste that into your .txt file.

Important note: Take 80% of your data (if you have 600 rows, then take 480 rows) and put that into one .txt file, then take the remaining 20% of your data (120 rows) and paste those into a second .txt file. We do this because we want two separate JSONLs: One for training (80% of our data) and one for testing (20% of our data).

We do this because GPT will not only train itself on the larger file but will also optimize itself using the test file as a reference – leading to better performance and results.

 

2) Creating a fine-tuned GPT model

Now that our data is cleaned and formatted into 2 JSONL files (one for training, one for testing), we can send it to the OpenAI API to fine-tune our GPT model.

If you want, you can check out OpenAI’s extensive tutorial on how to create a fine-tuned model here. But we will quickly walk you through the basic, high-level steps.

1. When you start the process of fine-tuning a model through the OpenAI SDK, use a snippet of Python code to upload your JSONL training file like this:

import openai
openai.api_key = ""
openai.File.create(
  file=open(r"train_file_path", "rb"),
  purpose='fine-tune'
)

 

2. Do this again, but now upload your JSONL testing file.

openai.File.create(
  file=open(r"test_file_path", "rb"),
  purpose='fine-tune'
)

 

3. Check that both files were uploaded and processed successfully using the command “openai.file.retrieve” to check their status.

openai.File.retrieve("Train-File-ID"),openai.File.retrieve("Test-File-ID")

 

4. Now, we can actually fine-tune the model using the code below. Here, GPT will optimize itself by measuring the training file against the test file reference.

openai.FineTuningJob.create(training_file="Train-File-ID", validation_file= "Test-File-ID", model="gpt-3.5-turbo or gpt-4o-mini")

 

5. Lastly, we can confirm that the model is fine-tuned. This is also the step where we receive the fine-tuned model ID, which we will use in our webhook in Marketo. You can also take it upon yourself to test your model before committing it to Marketo using this line of code:

openai.FineTuningJob.retrieve("FT-ID")
completion = openai.ChatCompletion.create(
  model="MODEL_ID",
  temperature=0,
  max_tokens=100,
  messages=[
    {"role": "user", "content": "Your Test Message"}
  ]
)
print(completion.choices[0])

 

Note: When creating a fine-tuned model, we are currently limited to GPT 3.5 turbo and GPT4o Mini.
 

3) Integrate your fine-tuned GPT model with Marketo

Our fine-tuned GPT model is now ready to be integrated with Marketo. This is a relatively short step that involves creating a webhook in Marketo (which we covered in Step 4 of this guide) with the following fields:
 

 

4) Integrate your fine-tuned GPT model with Marketo

So we have our fine-tuned GPT model set up (Step 2) and we have our webhook in Marketo created (Step 3). Now we’ll set up some automation in Marketo to use them together.

We’ll do this by creating a smart campaign in Marketo that will be triggered when a new lead is created or when lead information changes.

Once triggered, the smart campaign will send the information via webhook to the fine-tuned GPT, which will respond with the correct persona type (A, B, C, or whatever signifiers you used in your JSONL files).

Finally, if the persona for that lead has changed, the lead record will be automatically updated with the new classification.

Smart List:

 

Flow:

 

Outcome:

 

The Result

When all of this is set up properly, you will have an automatically updating field in Marketo for each lead that will signify which persona they fit into.

• Here’s a quick example that demonstrates how this new setup will operate:
• Jim is an “IT Analyst” and has been classified as Persona A.
• Jim’s role changes to “Technology Analyst”. This new role information could’ve come from a new form Jim filled out, a salesperson on your team updating Jim’s information, etc.
• Your smart campaign in Marketo is triggered (the one from Step 4).
• This will call on your fine-tuned GPT model and ask it something like “Which persona is Tech Analyst?” It will respond with Persona A or whichever persona is most appropriate.
• Then, the Marketo field for Jim’s persona will be automatically updated.

Note: This entire process is not limited to the language you are working in either. It can apply to any language worldwide.

The upfront work required to set this up is definitely worth it in the long run.

By leveraging a fine-tuned GPT model with proper integration and automation in Marketo, your persona classifications will be far more accurate and up to date, with minimal manual intervention required.

Ultimately, this will improve the effectiveness of your campaigns through better targeting, while freeing up more time for strategic and creative thinking.

And if you need help setting this up or have any other questions about how AI can improve marketing operations efficiency, send us a message here!

In this guide, we’re going to outline the top 25 AI tools for marketers.

The hype surrounding AI over the past year has started to settle. Now we’re entering an exciting phase where practical AI tools and solutions are being integrated into our daily work. Many of these tools can be used to expedite routine tasks, streamline operations, and ultimately free up more time and focus for creativity and strategic thinking.

When utilized well, AI-driven solutions can help marketers automate efficiently, gain deeper insights from data, create more personalized customer experiences at scale, and much more – leading to campaigns with greater impact.

But in order to maximize the benefits of AI, it’s important to experiment with how these tools can fit into your daily workflow and processes. The application will look slightly different for each person, but leveraging AI in a way that works for you is massively empowering.

So, without further ado, here are the top 25 AI tools for marketers (as of July 2024 – prices are USD):

• All-Purpose LLMs
• Specialized Writing Tools
• Visual Content Creation Tools
• Productivity Enhancement Tools
• Strategy Enhancement Tools
• Major Marketing & Sales Platforms

 

All-Purpose LLMs

These are the major LLMs that are publicly available right now. They represent the forefront of generative AI technology and lead the way for the development of other tools and applications. All of them utilize a chatbot interface, allowing users to submit prompts and instructions in a conversational format. They can help with writing tasks, coding, research, planning, and much more. We’ve labeled them “all-purpose” because they have the widest range of possible applications out of any tools on this list.

 

1. ChatGPT

What it is:

This one hardly needs an introduction, but for those who don’t know, ChatGPT is essentially a conversational AI chatbot and virtual assistant.

Ever since OpenAI released ChatGPT-3.5 in November 2022, its popularity has reached unprecedented levels worldwide. And it’s already come a long way. ChatGPT-4o was released only a few months ago and it came with several new features and improvements over previous models, including enhanced language understanding and improved natural language processing.

Use case:

The use cases of ChatGPT are vast. Marketers can use it to write blog and article outlines, brainstorm copy headlines, generate images with DALL·E 3, perform code writing tasks, analytical tasks, and much more.

Price:

Free: For individuals just getting started with ChatGPT.
$0/Month

Plus: For individuals looking to amplify their productivity.
$20/Month

Team: For fast-moving teams and organizations ready to supercharge work.
$25 per user/month billed annually
$30 per user/month billed monthly

Enterprise: For global companies looking to enable their workforce with AI.
Contact Sales for a quote.

 

2. Custom GPTs

What it is:

Custom versions of ChatGPT, known as GPTs, are where you can really push the limits of this technology.
This includes the GPT creation tool that comes with ChatGPT Plus subscriptions, as well as the GPT API which can be integrated into other applications directly.

Use case:

By tailoring a custom GPT to your specific needs, you can perform specialized tasks even more efficiently – whether that means analyzing documents, managing unique datasets, or interpreting instructions. And to go one step further, the true potential of the GPT API is unlocked when you fine-tune it and integrate it into other platforms like Marketo for email personalization at scale, persona classification, and more.

Price:

The pricing structure for GPT API usage takes a “pay for what you use” approach. There are several different rates based on which model you’re using, and how many tokens you use. All the pricing information is outlined by OpenAI here.

 

3. Claude 3.5 Sonnet

What it is:

Claude 3.5 Sonnet is the latest model developed by Anthropic. It’s touted as their most intelligent one yet, operating at twice the speed of their previous Claude 3 Opus model with improved reasoning, knowledge, coding proficiency, and ability to understand complex, nuanced instructions. It also includes a new “Artifacts” feature: a dynamic workspace where users can see, edit, and build upon Claude’s creations in real time.

Use case:

This model excels at producing on-brand drafts for blogs, newsletters, articles, social media posts, and so on with a more natural, relatable tone than its predecessors. It’s ideal if you’re a marketer who needs to create polished content quickly, maintaining a consistent brand voice throughout. The “Artifacts” feature also allows for real-time collaboration, making it easier for teams to work together on content and marketing projects.

Price:

Free – $0 per person/month
Pro – $20 per person/month
Team – $25 per person/month

 

4. Gemini 1.5 Pro

What it is:

Google’s Gemini 1.5 Pro is similar to the above LLMs, but it sets itself apart through its large context window that allows it to process up to 1,000,000 tokens at once.

Use case:

A context window of this size makes Gemini 1.5 Pro ideal for handling extensive material like large datasets or comprehensive reports. It’s great for extracting valuable insights from market research data or reviewing and summarizing comprehensive industry reports.

Price:

Gemini Advanced Subscription – $20/month

 

Specialized Writing Tools

These tools focus more narrowly on using generative AI to help with writing tasks – whether you’re writing emails, blog articles, social media ads, product descriptions, or anything else. Their interface and feature set are tailored to writing assistance above all else.

 

5. Jasper

What it is:

Jasper is a generative AI tool that stands out by taking a highly tailored approach to content creation. It excels at learning and maintaining your brand’s unique style and voice.

Use case:

Jasper is great for generating personalized ad copy for different audience segments, long-form blog posts for inbound marketing strategies, and more. You can create a custom style guide and upload brand voice reference documents to a knowledge base, all of which powers output that is consistently on-brand. This is a great supplemental tool for any kind of copywriting and content writing that requires careful voice and tone accuracy.

Price:

Creator – $49 per seat/month
Pro – $69 per seat/month
Business – Custom Pricing

 

6. Writer

What it is:

Writer is a generative AI platform that puts an emphasis on enterprise security, privacy, and compliance requirements. For example, it offers robust visibility and access controls, it won’t use your data to train its models, and it adheres to global privacy laws and security standards.

Use case:

Like other generative AI tools on this list, it will boost content creation efficiency and creativity, offering a consistent flow of blog posts, social media content, marketing emails, and more. This is a really great option for marketers at larger organizations who want a generative AI platform that prioritizes security and privacy above all else.

Price:

Team – $18 per user/month
Enterprise – Custom Pricing

 

7. Persado

What it is:

Persado’s AI-powered platform creates personalized digital communications, using data-driven insights to enhance customer engagement. It features their “Motivation AI knowledge base” and is trained on enterprise language to optimize messages for higher conversions across multiple channels.

Use case:

Using Persado, you can refine messaging strategies for email marketing, social media ads, landing pages, SMS campaigns, and more by leveraging data-driven insights and past campaign performance. This is especially useful for marketers striving for consistent, impactful, and tailored messages that resonate with different customer segments.

Price:

Custom Pricing Only

 

8. Agorapulse

What it is:

Agorapulse has a great AI writing assistant integrated directly into their existing platform that can enhance social media copy effectiveness to improve engagement.

Use case:

Agorapulse works best when you input your own copy, then ask it to optimize and fine-tune it into a new version that prioritizes engagement. You can also use it to generate fresh content ideas and suggest the best times to post for maximum reach. And full integration within their platform allows users to seamlessly improve their copy, analyze post-performance, and then schedule and manage future posts in one place. This is a great option for marketers looking to focus on maximizing social media content engagement.

Price:

Standard – $49 per month/user (Billed annually)
Professional – $79 per month/user (Billed annually)
Advanced – $119 per month/user (Billed annually)
Custom Pricing Available

 

Visual Content Creation Tools

These tools use AI to assist in the creation of visual and audio content. They focus primarily on AI-powered video generation and image generation, with some tools offering video and audio editing assistance, transcription generation, and other specialized features.

 

9. Celtra

What it is:

Celtra is a creative automation platform that leverages AI to help marketers and designers efficiently create, manage, and optimize digital advertising content at scale.

Use case:

Celtra is a pretty awesome tool for any marketer who needs to produce visually appealing work but also wants to automate several variations for A/B testing and optimization at scale. Use it to produce localized ads for different markets, using data-driven insights to continuously enhance campaign performance.

Price:

Custom Pricing Only

 

10. OpusClip

What it is:

OpusClip is a generative AI tool designed to repurpose long videos into short, engaging clips optimized for social media platforms.

Use case:

As a marketer, OpusClip is a game-changer for producing short-form, engaging video clips for social media ads, website content, short video tutorials for customer education, and more. It uses advanced AI to identify the most compelling moments in your long-form videos, then rearranges them into short clips that are more likely to go viral. With the growing demand for versatile video content in marketing, this is a must-have tool for your content creation team.

Price:

Free – $0/month
Starter – $15/month
Pro – $29/month
Enterprise – Custom Pricing

 

11. Descript

What it is:

Descript is an AI-powered platform that streamlines the audio and video editing and creation process. Unlike more traditional tools, it allows you to make changes to audio and video by editing the transcript text directly.

Use case:

Marketers can use Descript to create video transcripts, add captions, remove filler words, add green screen effects, enhance voices, clean up background noise, and more. We think it’s a crucial part of any marketing team’s content creation toolbox – particularly when it comes to producing and editing video content and audio podcasts more efficiently.

Price:

Free – $0 per month
Hobbyist – $12 per person/month (Billed annually)
Creator – $24 per person/month (Billed annually)
Business – $40 per person/month (Billed annually)
Enterprise – Custom Pricing

 

12. Runway

What it is:

Runway is currently one of the leading AI-powered video generation tools out there right now. It leverages advanced AI algorithms to generate custom video content based on your text instructions and inputs.

Use case:

Runway is a great resource for marketers to create appealing visual content for a wide range of use cases. Use it to quickly create short, engaging videos to supplement your social media ads, generate b-roll footage to use in longer video advertisements, make your educational and training content more engaging, and much more.

Price:

Basic – $0 per month
Standard – $12 per person/month (Billed annually)
Pro – $28 per person/month (Billed annually)
Unlimited – $76 per person/month (Billed annually)
Enterprise – Custom Pricing

Sora (Honorable Mention – Unreleased)

Since it’s quite similar to Runway, we wanted to take a second here to mention OpenAI’s up-and-coming AI video generation tool, Sora. We’ll learn more about it when it’s released to the public (hopefully later in 2024), but for now, it is definitely one to look out for. We’re confident that OpenAI’s entry into this space will be a strong one that marketers can utilize to produce impactful visual content.

 

13. Midjourney

What it is:

Midjourney is an AI-powered image generation tool that creates visual assets based on user descriptions and instructions. The images it produces largely depend on the submitted text prompt, but it is capable of a range of images, from photo-realistic to highly stylized or abstract.

Use case:

Similar to DALL·E 3, which is accessible through ChatGPT Plus, marketers can use Midjourney to generate images for social media content, blog articles, infographics, product mock-ups, brand imagery, email headers, mood boards and storyboards for brand and product aesthetics, and more. It’s fast, straightforward, and produces some of the best results out of any image generation tool.

Price:

*Basic – $10/month
*Standard – $30/month
*Pro – $60/month
*Mega – $120/month
Enterprise – Custom Pricing

*Annual plans available for a lower monthly rate

 

14. HeyGen

What it is:

HeyGen is an AI-powered video creation platform that specializes in deep fakes and synthetic avatars. It allows users to generate videos using customizable AI avatars and voices from text scripts or audio files.

Use case:

HeyGen is useful for creating immersive content that enhances your digital marketing efforts. For example, developing personalized video messages for marketing campaigns, producing interactive digital content for sales outreach, product overview videos for your website, and more are all enabled here. It’s a cutting-edge tool we think plenty of marketers will find useful.

Price:

Free – $0/month
*Creator – $29/month
*Team – $149/month
Enterprise – Custom Pricing

*Annual plans are available for a lower monthly rate. Price will also increase based on credits needed.

 

Productivity Enhancement Tools

These tools are designed with productivity enhancements in mind. Many of them feature “AI Agents” or “Copilot” chatbot helpers that can receive instructions and automate basic, repetitive tasks, freeing you up to focus on higher-impact, creative tasks.

 

15. Zapier

What it is:

Zapier is an automation tool with an experimental AI workspace that connects commonly used apps like Gmail, Slack, and over 2,000 more. It allows you to automate repetitive tasks without coding or relying on developers to build the integration.

Use case:

You can teach Zapier’s bots to automate repetitive marketing tasks such as data synchronization between CRM and email marketing platforms. Or you can set up automated workflows, allowing Zapier to trigger social media posts based on blog publication. There are limitless possibilities here when it comes to streamlining marketing tasks and processes – and if you don’t know where to start, Zapier makes a large selection of user-created templates available.

Price:

Basic – $0/month
Premium – $20/month
Advanced – $100/month

Add-on services can be purchased for more complex and comprehensive workflow automations.

 

16. Google Workspace & Microsoft Copilot

What it is:

We’ve grouped these together because they both feature existing tools by Google and Microsoft that have been enhanced by AI to boost productivity.

If you use Google’s suite of productivity apps such as Google Docs and Sheets, you can take advantage of AI-driven (through Gemini) suggestions and automations directly in these tools.

Similarly, Microsoft has integrated “Microsoft Copilot”, a generative AI chatbot, into their entire Microsoft suite to enhance productivity and functionality. This includes everything from Word to Excel to Teams. Windows users will even find Microsoft Copilot on their desktop to streamline everyday tasks and processes.

Use case:

Use these AI integrations to draft, edit, and proofread text in Google Docs or Microsoft Word, as well as generate content based on prompts, suggest improvements, correct grammatical errors, and even enhance the tone and style of writing. AI can also help create presentations in Google Slides or Microsoft PowerPoint by suggesting layout designs and generating speaker notes – or by organizing and tracking data in Google Sheets or Microsoft Excel.

Price:

Microsoft Copilot
Microsoft Copilot Free – $0/month
Microsoft Copilot Pro – $20 per user/month

Gemini for Google Workspace
Requires an existing Google Workspace plan with one of these add-ons:
Gemini Business – $20 per user/month (Billed annually)
Gemini Enterprise – $30 per user/month (Billed annually)

 

17. HyperWrite AI Agents

What it is:

HyperWrite is an interesting AI tool that lets you create custom workflows for “AI Agents” who perform tasks autonomously for you. You can record the task once, then let the AI Agent take over and perform it as many times as you need.

Use case:

HyperWrite is definitely capable of assisting with routine email drafts, reports, and content outlines, but this new AI Agents feature is what sets it apart from other tools right now. You can set up Agents to automate personalized follow-ups to leads and customers, generate regular reports based on market research and competitive analysis, draft and schedule social media posts and content, and much more. For now, simple tasks are quite reliable, while more complex tasks are going to require more instructions and experimentation – but this feature continues to improve.

Price:

Premium – $16 per user/month (Billed annually)
*Ultra – $29 per user/month (Billed annually)

*As of July 2024, the Ultra tier is required to access Agent features.

 

Strategy Enhancement Tools

While similar to the previous category, these tools focus less on task automation and more on providing insights and information that can aid strategic decision-making and optimize campaigns. They can perform market analysis, competitive analysis, content strategy optimization and scheduling, sales strategy optimization, customer behavior insights, and more.

 

18. Perplexity

What it is:

As an AI chatbot-driven research platform, Perplexity functions as a hybrid between a search engine and an AI chatbot, combining features of both to deliver a seamless user experience.

Use case:

Marketers can use Perplexity to provide in-depth market analysis and insights that aid their strategic planning. It’s great for conducting comprehensive competitor analysis, identifying market trends, gathering consumer insights, or identifying potential partnership opportunities by analyzing industry networks. We like to think of it as an awesome research assistant.

Price:

Standard – $0/month
Professional – $20/month

 

19. MarketMuse

What it is:

MarketMuse is a content optimization platform that uses AI and machine learning to assist in the creation of high-quality, relevant content.

Use case:

MarketMuse can help content creators, marketers, and SEO professionals develop more effective content strategies. It’s great for conducting content audits, identifying content gaps, and ultimately optimizing content to rank higher on search engines. MarketMuse also has content planning features that help you develop a content calendar based on strategic keyword research, providing a roadmap for creation and distribution.

Price:

Free – $0/month
Standard – $149/month
Team – $399/month
Premium – Custom Pricing

 

20. Gong

What it is:

Gong.io is a revenue intelligence platform that uses artificial intelligence to analyze customer interactions across multiple channels, such as phone calls, emails, and web conferences.

Use case:

Gong leverages AI to analyze sales and revenue data, helping you make informed decisions that optimize your overall sales strategy. More specifically, it can improve sales pitch effectiveness by analyzing call recordings to provide actionable insights, track sales trends, forecast future performance, highlight and prioritize relevant opportunities, and more. All of this helps ensure your sales strategies align with what your customers are really looking for.

Price:

Custom Pricing Only

 

21. Intellimize

What it is:

Intellimize is an excellent tool that uses AI for website personalization and conversion rate optimization, offering features like AI-generated landing pages, an AI Content Studio, and integrations with other tools in your tech stack.

Use case:

Intellimize can help marketers test different website layouts to determine the most effective designs for user engagement. It can also help test and optimize call-to-action placements based on user behavior, which enhances the likelihood of conversions. And the AI Content studio ensures that consistent, relevant content is delivered to your audience for a more tailored and impactful user experience.

Price:

Custom Pricing Only

 

22. Hume

What it is:

Hume is a tool with the unique ability of using AI to interpret customer emotions. It’s trained on millions of human interactions, allowing it to measure nuanced vocal modulations, guiding language, and speech generation, as well as interpret both vocal and facial expressions.

Use case:

Marketers can make good use of this tool to help improve customer retention and satisfaction by monitoring sentiment analysis on social media, improving customer support interactions, personalizing customer experiences, and more. It can also be used to guide campaign effectiveness by analyzing the emotional reactions of target audiences to various content and advertisements.

Price:

Pay-as-you-go pricing model. Further details here.

 

Major Marketing & Sales Platforms

To round out the list, this section is reserved for pre-existing marketing and sales platforms that many marketers will already be using on a regular basis. We’ve included them because of their extensive AI integrations and additional AI-powered features, which significantly enhance productivity and efficiency for users. We’ll keep this part focused exclusively on what those AI integrations look like, as the platforms themselves will likely be familiar to you – and pricing will vary drastically based on your overall platform plan outside of AI-specific features.

 

23. HubSpot

HubSpot has a growing list of AI features and tools that it continues to integrate into its platform. Some of these include their AI Email Writer, AI Blog Writer, Content Remix, Chatbot Builder, and several more. Aside from these evolving options, they’ve created ChatSpot – HubSpot’s own AI-powered companion that can generate real-time insights from in-depth company research, comprehensive keyword rankings, and more.

 

24. Salesforce

Similar to HubSpot, Salesforce has integrated their own AI-powered assistant which they’ve titled Einstein Copilot. Powered by their Einstein 1 platform, it’s essentially a chatbot integrated directly into your Salesforce that can answer questions about prospects, sales data, opportunities, and so on. Outside of Einstein Copilot, Salesforce has integrated several other AI-powered features too such as generative AI for email writing, call summaries, and much more.

 

25. Adobe

Last but not least, we want to make sure we draw your attention to the AI-powered features Adobe has been integrating across all their offerings. From Adobe Firefly 3 for text-to-image generation, to Adobe Sensei GenAI for content and copy generation, to the integrated AI Assistant – all these features continue to accelerate productivity and creativity for marketers.

And if you’re an Adobe Marketo user, Adobe Dynamic Chat is now integrated with generative AI, allowing you to train it on sales, marketing, and product knowledge so customers and prospects can receive on-brand conversations and support.

We know that it’s tough for most marketers to stay up to date with the latest AI developments. Especially with how frequently new tools are being released. But once you sift through the noise and integrate a few key tools into your workflow, the resulting productivity and creativity increases make all the difference.

We encourage you to experiment with the tools from this list that are most relevant to the work you’re doing. Take some time to learn how they work and continuously refine how you use them.

And if you need some help, don’t hesitate to reach out to us here!

We’re continuously experimenting with different ways that AI can make us all the best marketers possible.

Do you find yourself questioning the value of AI?

You’re definitely not alone.

If you aren’t excited about AI today, you haven’t yet encountered a use case that is meaningful to you.

There’s a good chance you’ll shift to a believer at some point this year.

Here are three charts that tell a story of where we are today and where we’re going in 2024.
 

The Gartner Hype Cycle

The Gartner Hype Cycle presents the typical journey a new innovation takes to reach acceptance and adoption.

An innovation is released. Expectations are inflated. People naturally become skeptical and become disillusioned with the idea.

We have seen this time and time again with AI: “AI is going to change the world!” “AI is going to take our jobs!”

The truth is, change doesn’t happen overnight. And to the individual, it’s not clear that it will ever meet the hype. So skepticism remains.

Think about flip phones, blackberries, and then iPhones. That transformation didn’t happen in a year. It took some time. Microsoft was so confident about the Windows Phone 7 that they staged a mock funeral for the iPhone in 2010..

In 2024, I think we will start to see a shift. As demonstrable benefits increase, the individual or organization climbs up the slope of enlightenment. They see the technology for what it’s truly worth to them. These days, it’s pretty hard to argue that the iPhone form factor has not impacted mobile telephony.

If you’re not seeing AI as a game changer, then there isn’t a use case that has impressed you yet.

If the AI advocates are true and improvements come exponentially, then this year we should start to see a rapid climb up the slope of enlightenment.

This means more and more use cases with demonstrable benefits are on the horizon.
 

AI Adoption Model

Enter chart number two, the AI Adoption Model. This chart was something that Liza Adams and I discussed last summer as a spin off of a LinkedIn post she had made.

This chart demonstrates a risk/benefit tradeoff that will serve as an impediment to adoption in the short term.

Changing the status quo when the risks, or perception of risks, outweigh the benefits is a difficult choice for organizations to contend with.

What if you started making decisions for your organization based on GPT 3.5? Or made a massive investment in a product, just to have a new Custom GPT make it irrelevant?

With the clarity of hindsight, we can see that many of these bad decisions were made. Although it’s somewhat arguable that these risks were predictable even a year ago.

So as new innovations are released and the use case benefits become clear to the organization, changing the status quo and adopting new tools and processes becomes a much easier decision.

Risks like using proprietary data in training models have been seen as a major hurdle. New offerings, however, are addressing these issues head-on. For example, this is front and center in the new “Teams” option for ChatGPT.

In the end, there really isn’t a choice at all.

AI adoption will actively or passively take place. Passive adoption will happen because AI will be baked into existing tools that organizations have already adopted.
 

Use Case and Impact

This final chart was presented by Scott Brinker at a conference I attended last November.

This is another look at the evolution of AI. While Scott included “AI + No Code Tools” in this image, it could just as easily be labeled “AI” instead.

This chart shows that current AI tools have had a low impact on the organization. Things like writing emails, summarizing meetings, creating digital art for social – these aren’t transforming organizations overnight.

But the growth curve is steep, if not exponential. Have you ever seen the original DALL·E image creation? It was abstract if we’re being generous.

However, if we believe that improvements will continue – possibly exponentially – then we’ll eventually have tools that have a medium-level impact on the organization. This could be an AI agent that performs tasks that double the output of an employee, for example.

If you’ve seen some of the text-to-video generation tools, you’ll know that they’re going to transform the creative space. Kanye West just released a music video that is all AI generated.

Change is starting.

History has shown us that with every major innovation, some organizations (and individuals) adapt and some organizations don’t. I used to go to Blockbuster every weekend. I used to have a portable Toshiba CD player with headphones that didn’t skip!

AI is going to bring about changes at a rate we haven’t experienced before.

And if you’re not yet convinced, it’s because you haven’t seen a use case that is meaningful to you.

I’ll bet you a coffee that sometime in 2024 you’ll see something that changes your mind.

HubSpot is coming to the rescue!

But first ask yourself this question:

Does your team have clear, structured guidelines or principles on AI usage?

The overwhelming majority of people will likely still answer “No.”

The conversation around AI safety and transparency at work, however, is starting to gain more traction in our industry.

At RP, we continue to push the importance of discussions like these – which is why we released two things over the past few weeks:

1. A template on AI guidelines and principles.
2. Our very own custom GPT called MOPs AI Advisor.

Both of these resources were designed to help members of the MOPs community (and entire organizations) implement a system of transparency, accountability, and safety when it comes to AI integration in the workplace.

 

HubSpot’s 6 Steps for AI Transparency

And we’re excited to see other organizations embracing this conversation as well. The most recent example being HubSpot’s article: “The Complete Guide to AI Transparency [6 Best Practices].”

Below are the 6 steps HubSpot has come up with for creating a transparent AI policy:

Step 1: Define and align your AI goals.

Step 2: Choose the right methods for transparency.

Step 3: Prioritize transparency throughout the AI lifecycle.

Step 4: Continuous monitoring and adaptation.

Step 5: Engage a spectrum of perspectives.

Step 6: Foster a transparent organizational culture.

 

Layering In Resources

We think these steps provide a great foundation for organizations to build on.

In terms of following these steps in the real world, our own resources fit nicely as complementary tools that will expedite the process.

For example, for “Step 1: Define and align your AI goals”, our template on AI guidelines and principles comes in. When you sit down to create tangible documentation that clearly describes your organization’s AI goals, our template provides a robust starting point for you to consider.

And we’re constantly experimenting with AI in different ways.

One AI use case can drastically differ from another from a safety and transparency perspective. Which is why our MOPs AI Advisor can be a big help when it comes to “Step 2” all the way to “Step 5” of HubSpot’s best practices.

You can lean on our custom GPT as a second perspective on your experiments, ensuring you chose the right tools and consider additional privacy and safety implications you may run into. You can re-prompt the advisor to continuously monitor your experiments, adapting your strategies as needed based on its feedback.

While MOPs AI Advisor certainly isn’t designed to replace the perspectives of actual people in your organization, it can shine a light on potential viewpoints that others around the company may hold – which you can then verify through an open dialogue with those people.

 

Your Starting Point

All of these things contribute to “Step 6: Foster a transparent organizational culture.”

This happens over time, but clarity and consistency is the key.

Also, if we’ve learned anything from AI so far, it is that the situation is fluid. Things can change overnight, so it is important to understand new developments and how they impact your team.

We’re grateful to HubSpot for joining us in bringing important conversations like these to the forefront.

I am still really surprised at how unprepared most organizations are for generative AI.

A recent Salesforce survey of 14,000 people showed that most organizations have not developed AI guidelines and principles for their employees. It seems like the solution for many companies is to leave teams to fend for themselves or outright block the use of AI tools.

To help combat this issue, we shared our template on AI guidelines and principles last week that people could download and adjust based on their company needs.

And now, we’d like to continue to help our MOPs community with a new shiny tool:

Our very own custom GPT called MOPs AI Advisor.

It’s 100% free to use if you have a Chat GPT Plus or Enterprise subscription.

 

What does it do?

The custom GPT itself is trained on that same AI guidelines and principles template. It has been designed to do two things:

First, it lets you generate your own AI principles and guidelines from the ground up, tailored specifically to your company. You can use chat prompts to feed it information about the style of your organization and the amount of control you want to have over AI.

From there, it’ll draft you your very own set of AI principles and guidelines, acting as a strong foundation to build on. MOPs has an opportunity to take a leadership role if not a recommending role on this.

Second, and arguably more interesting and useful over the long run, is that it’ll allow you to input specific AI use case ideas you have and get feedback on the possible data security and privacy implications you may not have considered.

For example, one of our experiments here at RP was to use AI to generate personalized content for nurture campaigns in Marketo. Now, we can put that use case concept into MOPs AI Advisor and get helpful feedback on aspects to consider as we move forward.

 

Laying the groundwork

The creation of this custom GPT (and our template from last week) is our way of sharing our thought processes and AI best practices with the community.

By doing some of the work for you, we aim to not only make your lives a bit easier but also enable you to take some of these challenges into your own hands.

Interact with the MOPs AI Advisor and conduct some experiments of your own. We’re living in exciting times, and we can’t wait to see what you come up with.

During our AI Panel at MOps-Apalooza in November 2023, audience members were asked to raise their hand if their MOPs team had AI guidelines and principles. I was pretty surprised when hardly any hands went up.

In fact, I think the only people raising their hands were members of our RP Team. I was chatting with Paul Wilson who moderated that session with Brandee Sanders and Connor Jeffers and he was also surprised by where the community was at.

It’s clear that most of our community is using AI or has at least tried it. Now is as good a time as any to share some of the ways we are thinking about AI at RP.

We’ve put together a template that we hope will serve as a foundation for AI guidelines and principles within your MOPs team (and organization as a whole).

 

What’s in the template

The document is divided into three sections.

1. AI Use Models for Organizations:

• This outlines the merits and challenges of various approaches: open use, moderate restrictions, and high control environments.

2. General MOPs AI Guidelines:

• An eight-point consideration list. These guidelines provide a blueprint for leveraging AI effectively and responsibly in your MOPs environment.

3. Three Approaches to AI Principles:

• Based on the AI Use Models, there are three distinct frameworks. These models outlines options for organizations to adopt and tailor AI in alignment with their goals and values.

 

MOPs helping MOPs

AI is transforming the marketing landscape and understanding how to harness its potential responsibly and effectively is crucial.

We want to help the community by sharing our knowledge and experiences. Whether you’re a seasoned MOPs professional or just starting, hopefully these templates provide you food for thought.

You can download the full template document below. Nothing gated. Just a link to download. Hopefully this is helpful for you as you think about AI in your company.

Download the template

TLDR: How is AI transforming marketing operations? Some platforms are integrating AI tools directly, while others are allowing user communities to develop add-on solutions. The winners of this race will be those who integrate AI effectively, while the losers risk missing out on market shifts. We are at a crucial turning point in AI tools for B2B, and embracing AI is vital for staying competitive.

Ready. Set. Go!

It’s not a space race. It’s more of a 5000-meter race – and we’re on the first lap with 12 more to go. A couple of runners have pulled out ahead and the rest of the field is figuring out what to do.

Salesforce and HubSpot are incorporating AI assistant tools into their platforms to enhance user experience, ease the learning curve, and prevent users from seeking alternative AI solutions. Adobe has doubled down on the creative side, but we’re not sure what’s in store for platforms like Marketo.

 

“It’s clear now that AI has started to transform business.”

 

It’s clear now that AI has started to transform business. Tasks that used to require expert knowledge and hours to complete can now be done quickly and efficiently by AI.

 

Which Course?

There are two routes for platforms to take. The first is to integrate AI directly into the platform (like HubSpot), and the second is to allow user communities to develop add-on solutions or APIs to integrate AI enhancements.

 

The Winners?

So far, it’s elbows up around the first corner of the track, with HubSpot and Salesforce quickly integrating AI functionality – but it’s too early to tell who will win this race.

Whoever comes out on top will have to overcome the following key issues:

1. The power of status quo. In today’s MOPs landscape, it is very hard to disrupt the status quo. Convincing organizations to shift marketing automation platforms requires a significant cost benefit.

2. Patience. It’s reasonable to be optimistic that all platforms will eventually integrate AI into their offerings. But the real question is, will users be patient enough to wait for their current platform to add AI enhancements, or will they turn to another platform that does it first?

3. Early adoption. Platforms must communicate that those who embrace AI early on will likely be well-situated for future shifts and evolutions in how we do our work. MOPs professionals should welcome a world where repetitive, low-value tasks are eliminated – it’s very likely that AI will accelerate MOPs work for the foreseeable future.

 

The Losers?

This is even harder to predict. But it’s safe to say those who are slower to embrace AI are most likely to lose out or miss important market shifts.

 

“Those slower to embrace AI are most likely to lose out or miss important market shifts.”

 

Consider this scenario: a mid-market company has made an acquisition and is deciding between two marketing automation platforms to standardize on. Given that one platform has strong AI capabilities that increase efficiencies and lower costs to operate, and the other platform does not, it would seem like an easy choice.

What about the experts? All around, the speed at which work can be completed will increase. The losers will likely be those who are last to adopt and integrate AI into their systems and processes.

 

The Gamblers?

There are tremendous opportunities today for many to build third-party add-ons that integrate AI functionality into these platforms like Marketo.

For example, at RP we’ve created some AI content personalization add-ons that are really promising. The question is, how far do we have to go and will this feature be replaced by official platform integrations?

That’s the million-dollar question that everyone wishes they had a crystal ball to answer.

 

What are the next steps?

As we know, it’s still very early – the race has just entered the first corner.

And while it’s easy to become fatigued by the inflated expectations and relentless hype of AI, we’d be doing ourselves a disservice if we didn’t try our best to stay optimistic, open-minded, and up-to-date.

Because the reality is: we are at a crucial turning point in AI tools for B2B.

Our work is going to change, and we must change with it.