Learn More
Deploying AI strategies into mid-market companies to help them hit their business goals and achieve measurable results.
“AI won’t take your job. It’s somebody using AI that will.”
"AI has evolved from a parlor trick to an essential tool that represents a paradigm shift in how we market."
"The speed that it's evolving eclipses any previous trend - it's a bullet train and it has already left the station."
THE AI STORY
A QUICK HISTORY OF AI

1950s
The Birth of AI
The term "Artificial Intelligence" is coined, Frank Rosenblatt develops the first artificial neural network.

1970s
Early Developments and Setbacks
First AI Winter - reduced funding and interest in AI research.

1990s
Resurgence and Breakthroughs
IBM's Deep Blue defeats world chess champion Garry Kasparov

2010s
Deep Learning Revolution
IBM Watson wins Jeopardy! against human champions & AlphaGo defeats world champion Go player Lee Sedol.

2020s
The Era of Generative AI
OpenAI releases GPT-3, a powerful language model​, bringing conversational AI to the mainstream
WHERE THIS IS GOING
Big Tech is going nuclear. Amazon, Microsoft, and Google are building nuclear power plants to handle the power needs of their massive AI server farms.
Data centers are expected to use 8% of U.S. energy by 2030, up from 3% today.
WHAT DOES THIS MEAN FOR BUSINESS TODAY?
AI IN ACTION
Current Trends for Revenue Teams
Personalized Customer Journeys
AI tailors marketing messages and offers based on customer behavior and preferences.
Predictive Analytics for Sales
AI models predict the likelihood of leads converting into customers.
Predictive Customer Service
By analyzing historical data, AI can predict potential customer issues before they arise.
Dynamic Content Generation
AI helps create personalized content including emails and social posts at scale.
REAL-WORLD RESULTS
Average metrics of companies successfully deploying AI¹
35%
Increased Revenue
35% increase in sales with personalized AI campaigns.
25%
Reduced CAC
Lowered acquisition costs by 25% using AI-optimized ads.
40%
Customer Retention
AI is helping retain 40% more customers by analyzing feedback and behaviors.
30%
Faster Sales Cycles
AI-powered lead scoring shortened sales cycles by 30%.
¹Research done by Forbes & Gartner
YOUR DATA IS GOLD
Are You Leveraging It Effectively?
Your company holds a wealth of valuable data, often scattered across teams, departments, and siloed systems.
Combining first-party data with AI creates unparalleled opportunities to elevate customer experiences, increase operating capacity, and accelerate growth.
By focusing on these key areas, companies can create a significant competitive advantage in today's data-driven business landscape.
CHALLENGES WITH AI ADOPTION
TOOL OVERLOAD
New AI tools and Large Language Models are being deployed and created at a rapid pace.
How do you select the tools that align with your business goals?
LACK OF TEAM TRAINING
How do you keep your teams on top of how to:
  1. Leverage AI to it's full potential.
  1. Use AI safely and responsibly?
  1. Keep pace with the latest advancements?
FOUR TYPES OF AI-INTERACTION

1

2

3

4

1

QUERYING

2

PROMPTING

3

CUSTOM AI AGENTS

4

AI + AUTOMATION
BASIC
INTERACTIONS WITH AI
#1
QUERYING
Example
Users asks AI for information similar to how they use Google.
Example - a marketing manager asks AI to lookup the latest trends in Content Marketing.
Problem: Uncertainty
Uncertainty in framing questions for AI can limit users from gaining deeper insights, leading to missed opportunities and reduced value from their data.
#2
PROMPTING
Example
Users instruct AI to complete a specific task.
Example - content creator asks AI to draft a 1,500-word blog post on the role of AI in social media marketing.
Problem: AI Prompting lacks specificity
Most individuals lack the understanding of how to effectively prompt generative AI.
The minimum effective framework for prompts should include role, goal, and context and are always more than a sentence or two.
SOPHISTICATED
INTERACTIONS WITH AI
#3
CUSTOM AI AGENTS
Building Role-Based AI Agents
Custom AI models tailored for specific tasks, like a copywriting AI agent that can predict writing assignments and what is expected of it.
Example: Training a Copywriter AI Agent
An AI copywriter trained to master a company's brand, Ideal Customer Profile, tone, customer communication style, industry compliance, approach to writing, and collaboration with human counterparts.
#4
AI Automation
Combining AI tools with automation to streamline complex workflows. Example: A financial firm automates the loan approval process using AI agents, from application to approval.
Example

Trigger 1
A new lead fills out a form on your website.

AI Agent 1
Custom AI Agent reviews the lead and researches their company and sends info to CRM system

Trigger 2
CRM+AI system scores the lead. If the lead qualifies, CRM sends instructions to a trained sales rep AI Agent.

AI Agent 2
AI writes a personalized email for the lead, sends emails draft to the human sales rep to proof and send.
HOW STAGEX HELPS COMPANIES

1

Identifying AI Opportunities
Mapping existing workflows helps pinpoint areas where AI can add value in research, creation, and enablement stages

2

Pilot Project Selection
Prioritizing mature AI use cases relevant to the organization, considering team readiness, and aligning with brand values are essential.

3

Training & Up-skilling
Investing in training programs that emphasize the role of AI in enhancing creativity, rather than replacing it, is crucial.

4

Brand Consistency with AI
Off-the-shelf AI often produces generic results. Custom models trained on brand data ensure outputs reflect brand identity.

5

Responsible AI Guidelines
Companies need to establish clear guidelines regarding the ethical and legal use of AI, considering factors like privacy, data security, and bias.

6

Measuring Success
Defining and tracking KPIs like turnaround times, creativity enhancement, cost reduction, scalability, and quality improvement is essential for evaluating AI transformation progress.

7

Continuous Learning
Fostering an environment of experimentation and learning, with regular feedback loops and access to training resources, ensures ongoing adaptation and improvement.
Deploying AI strategies into mid-market companies to help them hit their business goals and achieve measurable results.