The Heart of Connection: Unleashing the Power of Human Relationships in a Digital World

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All Human

All Technology

Technology v. Human Touch

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Technology Hype

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In 1970 Marvin Minsky said, “machines will be capable of doing any work a human could do in…?”

1. 3-8 years
2. 10-20 years

C. 20-30 years

 co-founder of the Massachusetts Institute of Technology's AI laboratory

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August 2025 MIT Paper “The State of AI Business in 2025” states what percent of GenAI make it to production?

1. 5%
2. 15%

C. 36%

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August 2025 MIT Paper “The State of AI Business in 2025” states what percent of companies get 0% ROI on GenAI pilots?

1. 5%
2. 25%

C. 95%

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Technology can now win art shows for cash prizes?

A. True

B. False

C. What is art?

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Top 3 Contestants

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Quick AI Training Overview – Street Safety

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1980s Approach – Expert Systems

If is_car_going_too_fast(car_distance, car_speed):

Print (“Don’t cross the street”)

Else:

Print (“It’s safe to cross”)

Car Distance

Car Speed

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What if it’s a different sidewalk?

What if its raining?

What if the driver is texting?

What if I can’t see the driver texting?

What if the driver is texting and listening to Taylor Swift?

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Training Data From Past CCTV Cameras

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Multilayer Perceptron

Closest Car

Input Layer

Speed

Hidden Layer

H1

H2

Output Layer

H1

Backpropagation Helps Train Model

10 feet

100 mph

Live!

No

+0.01

+0.01

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How AI Learns - Multilayer Perceptron

Credit to: https://medium.com/the-feynman-journal/what-makes-backpropagation-so-elegant-657f3afbbd

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Flawnson Tong – Medium

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Image Recognition Models- 100-10,000 nodes

Accuracy < 100%

Humans Recognize Objects After 2 times ~100 accuracy

Where AI Struggles

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Being Human Area 1: �Replacement

Where Technology can replace humans

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Complexity Drives Out Technology

You work all by yourself in a shop that where your assistant is an AI. To make a new paint your AI suggests the following chemicals to be used,

​

which do you pick?:

​

A. Acetaldehyde 

B. Potassium cyanide

C. Trichlorophenoxyacetic acid 

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Questions to Ask When Analyzing Tech Disruption Promises

1) Is there a simpler version of this technology?

2) Is the general population using a simpler version?

3) Are their legal repercussions if something goes wrong?

4) Are there any social adoption obstacles?

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Case Study Claims

1) Is there a simpler version of this technology?

2) Is the general population using a simpler version?

3) Are their legal repercussions if something goes wrong?

4) Are there any social adoption obstacles?

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Case Study Customer Service Chatbots

1) Is there a simpler version of this technology?

2) Is the general population using a simpler version?

3) Are their legal repercussions if something goes wrong?

4) Are there any social adoption obstacles?

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Case Study: Tech In the World of Insurance

Complexity Requires Trust

Can the result be correctly verified by a non-technical person?

Would you like a schedule or blanket waiver of subrogation?

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Where Technology Can Replace Humans

1. Where AI makes 50% less errors than humans performing the same task
2. A non-technical person can understand the AI output

3. Where legal repercussions are not catastrophic

• Cars
• Images
• Writing
• Revenue projections
• P/L statement

• Line profitability
• Training materials
• Creating tests
• Recruitment positive indicators

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Interviews, surveys, and analysis of 300 public implementations in “The State of AI Business in 2025” *

• Limited disruption: Only 2 of 8 major sectors show meaningful structural change
• Enterprise paradox: Big firms lead in pilot volume but lag in scale-up
• Investment bias: Budgets favor visible, top-line functions over high-ROI back office
• Implementation advantage: External partnerships see twice the success rate of internal builds

Four Patterns Where GenAI Implementations Fail:

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Each industry was scored from 0 to 5 based on five observable indicators.

1. Market share volatility among top incumbents (22’ to 25’)
2. Revenue growth of AI-native firms founded after 2020
3. Emergence of new AI-driven business models
4. Changes in user behavior attributable to GenAI
5. Frequency of executive org changes attributed to AI tooling

Which Industries Are Showing Structural Change

These scores represent normalized averages across five dimensions, triangulated from public indicators and interview-derived assessments. Alternative weighting schemes were tested to confirm consistency of industry rankings**

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• 70% funds go to sales and marketing functions of AI budget allocation in survey.
• Back office usually has higher ROI, but smaller allocation

Fund Allocation:

Survey based on 153 senior leaders

that currently use AI in their firms

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Benefits of AI

Front-office wins:

• Lead qualification speed: 40% faster
• Customer retention: 10% improvement through AI-powered follow-ups and messaging

​

Back-office wins:

• BPO elimination: $2-10M annually in customer service and document processing
• Agency spend reduction: 30% decrease in external creative and content costs
• Risk checks for financial services: $1M saved annually on outsourced risk management

Interviews, surveys, and analysis of 300 public implementations in “The State of AI Business in 2025” *

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First Neuralink Patient – Noland Arbaugh

- July 2024

Using AI To Help Humans Communicate With Only Their Minds

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Example of GenAI in Backoffice Use: Text Summarization

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How Successful Firms Benefit – ROI Paradox

• Most implementations don't drive headcount reduction
• Organizations experience workforce impacts in customer support, software engineering, and administrative functions. (ROI Paradox)
• Measurable savings from reduced BPO spending and external agency use, particularly in back-office operations.
• Improved customer retention and sales conversion through automated outreach and intelligent follow-up systems.

Interviews, surveys, and analysis of 300 public implementations in “The State of AI Business in 2025” *

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Understanding AI Rate of Change

Spotting Change Before Its Here

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Time to Reach 50 Mil. Users

Airlines

64 Years

Automobiles

62 Years

50 Years

Telephone

Electricity

46 Years

Credit Cards

28 Years

Television

22 Years

ATMs

18 Years

Computers

14 Years

Mobile Phones

12 Years

Internet

7 Years

Facebook

4 Years

WeChat

1 Year

Pokemon Go

19 Days

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When was ChatGPT first released?

June 11, 2018

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“The future is already here, it’s just not evenly distributed” �– William Gibson

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Fringe

AI Innovation Adoption Curve

Adoption Curve

Symbolic AI,

Expert Systems – 1970s

Deep Learning

Machine Learning – 2000s

Generative Models – Markov Models – 1980s

2014 Generative Adversarial Networks (GANs)**

2023

Agent-to-Agent (A2A)

​

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Polymarket Examples

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AI In Customer Service

Do we prefer human or technology connection?

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https://cyberpsychology.eu/article/view/4285

Heart of Connection

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Discussion Prompt: 5-minute Conversation

“You are planning the ideal party. What kind of party would it be? Where would it be held? Who would you invite?

2- 4 Conversation Prompts

Post-Conversation: Individually Ranked Convo For Emotional Connection

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1. Face to Face

Room A

Room B

2. Video

3. Audio

4. IM

Measuring: Smiles, Laughter, Nods, Gestures, Self Reporting Bonding

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Human Connection Results

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How Emotions (Emoting ) Expressed Digitally

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Deepfakes Undermine Genuine Virtual Emotion

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Chatbot -> Triaging

Different Technology Applications for Human Connection

Newsletters -> AI prompts for general formatting, giphy creation

Social Media -> AI prompts for posting, authenticity

Videos -> AI Prompts for writing, capturing people who you serve

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Chatbot Engagement Levels

• Level 1: Triaged knowledge base
• After hours scheduling
• Level 2: Customized Data Solutions
• Can look up account information
• Level 3: Transactional Chatbots
• Bank of America (Erica)

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Chatbot Engagement Levels - Examples

• Level 1: Triaged knowledge base
• Suggests going to their carrier (does not specify which)
• Suggests going to portal where they can generate it
• Level 2: Customized Data Solutions
• Looks up account information
• Suggests going to their specific carrier or portal
• Could potentially create certificate
• Level 3: Transactional Chatbots
• Can handle if an endorsement is needed
• What if the wrong endorsement is added?

I need evidence of property insurance

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Chatbot Engagement Levels - Examples

• Level 1: Triaged knowledge base
• Suggests going to their carrier (does not specify which)
• Suggests going to portal where they can file it
• Level 2: Customized Data Solutions
• Can look up account information and give them link to file claim
• With carrier, needs a lookup table
• No lookup table, uses internal
• Level 3: Transactional Chatbots
• Unnecessary here

I need to file a claim

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​

Launched a Level 1 chatbot on its website and mobile app to:

​

• High containment rates (~70%) and deflected basic queries from call centers.
• Rolled out a Level 2 chatbot with advanced triage capabilities:
• Collect first notice of loss (FNOL) details
• Pre-screen claims for fraud indicators
• Route complex claims to specialized teams

​

Level 2 Breakdown: Inaccurate Claims Triage

• Minor property damage was flagged as severe
• Auto accident claims were wrongly flagged as potentially fraudulent due to inconsistent wording
• Claims were delayed or sent for unnecessary investigation
• Some high-severity claims were routed to low-priority queues

​

Case Study – InsurEco Chat Level 1=>2

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Level 1:

• Appointment times, prescription refills, and insurance FAQs
• High satisfaction score and reduced call volume by 20%

​

Level 2

• Incorrect Medical Advice- suggested home remedies for symptoms later diagnosed as serious conditions (e.g., advising "rest and fluids" for chest pain)
• A patient delayed going to the ER, resulting in a near-fatal heart event

• Data Privacy Violations- Level 2 bot collected sensitive health data (pregnancy status, medication names) without updated consent or clear privacy disclosures
• Breach of HIPAA guidelines
• MediHelp was fined and forced to conduct a full data audit

Case Study – Medihelp – Chat Level 1=>2

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Platforms to Help Create Triaging Level 1

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Connecting Area 5: Certainty

Technology’s Certain Dependence on the Human Touch

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What do AIs and Berkeley Hippies Have in Common?

They Both Hallucinate a Lot

3-5%

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Hallucination - Generative AI models produce responses that don’t match reality

To mitigate hallucinations, generative AI tools paired with fact-checking systems that leave no chatbot unsupervised.

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Examples of Hallucinations

50 reporters laid off due to increased use of

generative AI for Microsoft News’s articles,

public embarrassment, weakened trust

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Are companies financially responsible if a chatbot says incorrect information?

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Mechanical Turk Paradox

1770

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Three Mile Reopening

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Technology Curve

Hype Cycle

Peak of Inflated Expectations

Trough of Disillusionment

Slope of Enlightenment

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Moving Forward – Building an AI

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First Pilot

Value Curve With AI

Time

ROI

Production

ROI

Break

even

18 months

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Question You Need to Ask

Is this problem best solved by AI?

or

Do I Want to solve this problem with AI?

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Helpful Resources and Slides:

Helpful Resources and Slides

Get In Touch:

Contact:

Email: jeff@jeffjbutler.com

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The Power of Human Connection

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Helpful Resources and Slides

The Heart of Connection: Unleashing the Power of Human Relationships in a Digital World

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