After decades of development, in 2022, AI systems achieved a new level of popularity with the emergence of Generative AI, which is capable of producing high-quality images, texts, and speech from text-based prompts. OpenAI's ChatGPT product captured the imaginations of consumers and business alike, and seemed poised to change everything. In this webinar, we will be exploring the fundamentals of AI's impact on content marketing, what (if anything) has actually changed, and how to harness AI as a strategic advantage in your content process. To watch the recording of the webinar, visit: https://my.demio.com/recording/J7GlZKRv

1. Content In The Age Of AI:
The Fundamentals Of Content In A Post-GPT World

2. What We’ll Cover
● What is AI?
○ How does it work?
○ What is it good at?
○ What is it bad at?
● Integrating AI into content
creation
○ AI as team
○ AI as team member
○ Building AI-assisted
workflows
○ Pitfalls and problems
● Examples and use cases

3. WHAT IS AI?

4. What Is ChatGPT?
● A (very) Large Language
Model (LLM)

5. What Is ChatGPT?
● A (very) Large Language
Model (LLM)
● A neural network built on the
Transformer architecture
Inputs
Input
Embedding
Positional
Encoding
Multi-Head
Attention
Add & Norm
Feed
Forward
Add & Norm
Nx
Outputs
(shifted right)
Output
Embedding
Positional
Encoding
Masked
Multi-Head
Attention
Add & Norm
Feed
Forward
Add & Norm
Nx
Multi-Head
Attention
Add & Norm
Linear
Softmax
Output
Probabilities

6. What Is ChatGPT?
● A (very) Large Language
Model (LLM)
● A neural network built on the
Transformer architecture
● A language prediction engine
There
is
may
was
are
seem
be
were
lots
many
be
lots of
&
many
many
concerns
people
reasons
onlookers
people
&
t - 1 t t + 1 t + 2

7. What Is ChatGPT?
● A (very) Large Language
Model (LLM)
● A neural network built on the
Transformer architecture
● A language prediction engine
● A context-less,
understanding-less word
generation black box
Or a cute unknowable horror from beyond space and time that is
incomprehensible and cares nothing for humanity!

8. What Is ChatGPT?
In short, ChatGPT and the other
similar language models guess what
a language-based output should
sound like based on a corresponding
input and a large training set of
language-based data.
At S&G, we think of it as a
technology that will simultaneously
change everything, and not very
much at all.

9. What Is ChatGPT
Good At?
Generative language AIs are good at broad, strictly
language-oriented tasks that focus specifically on
understanding the composition of language.
● Reading and evaluating content on the
technical merits
● Approximating voice and tone
● Mimicking written styles
● Transforming language into data
● Summarizing, excerpting, and outlining
● Responding to ideas in a natural-sounding
way
● Evaluating the similarity of two ideas
● Providing something that sounds like the
correct answer

10. What Is ChatGPT
Not Good At?
Generative language AIs are bad at deep responses.
Anything that requires outside context, an
understanding of the world, or a reliance on facts and
figures. It’s also not great at math, and it struggles
with consistency and novelty.
● Reading and understanding content on a
human level
● Accurately answering fact-based
questions — hallucinations / mirages
● Understanding context
● Maintaining long conversations or
extended trains of thought
● Understanding consequences or
implications; logical reasoning
● Creating relevance outside of the prompt
● Knowing anything past the training cut-off
● Providing the actual correct answer

11. We see a person do something, and we know
what else they can do, and we can make a
judgement quickly. But our models for
generalizing from a performance to a
competence don’t apply to AI systems.
-Rodney Brooks
Zorpette, Glenn. ”Just Calm Down About GPT-4 Already.” IEEE Spectrum 17 May 2023

12. Current Ideal AI Use Cases
Self-contained Generative
Repetitive
● Context-complete
● Limited scope
● Short and focused
● Creative
● Unverifiable
● Synthetic
● Time-intensive
● Programmatic
● Uniform

13. Freespeech in nutshell
● Transcribe: state of the art
models, 10+ languages,
time-stamps, easy to review
and edit.
● Translate: best-in-class
commercial translation APIs.
● Voice-over/dub: accurate
speech-rate, multiple voices,
emotions, fast iteration.

14. INTEGRATING AI INTO CONTENT
CREATION

15. Integrating AI Into Your
Content Process
There are two schools of thought
about how to integrate AI into the
content process:
● AI As Team: AI completely
replaces an entire content
creation team, or at least a
large part of one.
● AI As Team Member: AI
replaces limited sections
of teams, or augments
existing teams.

16. AI As Team
The most obvious solution is
replacing an entire content team
with ChatGPT or similar, and
outsourcing the large and
complete task of content creation
to the AI.
This is often where many people
begin and end their AI content
journey.

17. AI As Team: Pros
+
● Simple
● Obvious
● Biggest headcount impact
● Least cost/planning overhead
● Immediate impact
● Appearance of scalability
“AI As Team” is naturally one of the first
things many organizations try.
Without experience with AI’s capabilities, it’s
easy to assume it can handle the entire
content process, while being infinitely
scalable and significantly reducing
headcount.
The AI As Team approach is simple, quick,
and requires the lowest commitment of
resources and planning.

18. AI As Team: Cons
-● Creative limits
● Accuracy issues
● No context
● Outdated information
● Limited emotional intelligence
● Derivative and undifferentiated
● No strategic vision
● Less headcount reduction than initially appears
● Not nearly as scalable as initially appears
● Uses AI for things it isn’t great at
In practice, however, many of the benefits
turn out to either be overstated or entirely a
mirage.
Scaling full-team replacement with AI
quickly becomes untenable, as the
shortfalls of current systems begin to show
and require an increasing amount of human
labor to overcome.
Doing AI cheap and fast results in few
long-term savings, low-quality results, and
early abandonment — giving up a strategic
advantage because of low-quality tactical
execution.

19. AI As Team Member
AI As Team Member is more
difficult to implement, and requires
not only a solid understanding of a
given AI’s capabilities, but also the
full system that it will fit into.
This means understanding not just
the broad strokes, but the
individual components that go into
producing each piece.

20. AI As Team Member: Cons
-
● Higher upfront costs, in time and resources
● Increased complexity
● Fewer immediate benefits
● Less obvious benefits
● Difficult to implement
● Longer time to ROI
“AI As Team Member” is a more mature
implementation that requires more planning
and higher upfront costs.
It also doesn’t result in the immediate
wow-factor of saying, “This was entirely
written by AI,” or the immediate case for
headcount reductions.
These factors can slow adoption, but can
also result in higher long-term commitment
and stronger overall lift.

21. AI As Team Member: Pros
+
● Tailored to specific business needs
● Increased scalability within and across teams
● Long-term growth in:
○ Efficiency
○ Savings
○ Strategic competencies
● Plays to AI’s strengths
● Differentiation from competitors
● Less disruption
“AI As Team Member” works so well not only
because it plays to the strengths of AI, but
because it fits into a broader strategic
framework — rather than just being an
ad-hoc tactical implementation.
Building use case-specific AI tools
familiarizes companies with their own
processes, AI’s capabilities, and helps build
out AI competencies in-house that can be
transferred to other use cases.
It also ultimately results in higher-quality,
differentiated work.

22. Building AI-Assisted Workflows: Begin With The Process
Content
Audit
Gap
Analysis
SEO
Research
Competitor
Analysis
Resource
Audit
Strategic
Objectives
Brainstorming
Sessions
Social
Listening
Internal
Listening
Editorial
Calendar
Stakeholder
Review
SEO
Alignment
Marketing
Review
Outline and
Expansion
Initial
Drafting
Final
Drafting
Strategic
Editing
Line
Editing
SME
Editing
SEO
Editing
Team-level
Approval
Department
Approval
Compliance
Approval
Stakeholder
Approval
Distribution
Plan
Social
Copy
Pitch
Copy
Owned
Posting
Planning Creation Utilization
Ideation Validation
Identification Drafting Editing Approval Distribution
Start with a map of the complete process and all of its major subtasks/nodes to get a clear idea of what you can outsource to AI.

23. Building AI-Assisted Workflows: Understand Opportunities
Identify parts of the broader process that fit the strengths of language AIs: self-contained, generative, and repetitive. Think beyond obvious, broad implementations.

24. Building AI-Assisted Workflows: Clearly
Define Inputs and Outputs
● Identify constraints: is it
output, input, both?
● Begin with the constraining
value and work from that
● Connect inputs and outputs
with rough process outlines
— don’t worry about the
details yet
● The big question to answer:
Can your inputs lead to
desired outputs?
Competitor Website
Competitor Analysis
INPUT
OUTPUT
PROCESS
Collect Content
Organize
Content
Process
Content
Extract Relevant
Information
Do Some
Analysis
Maybe html, or just a
url, or saved images?
Existing human-readable
content analysis template
with design.
???

25. Building AI-Assisted Workflows: Test Before
You Build
● Experiment with
“human-initiated” AI
workflows before attempting
any automation
● Create consistent prompts,
and verify that they act as
expected across a variety of
inputs
● Get input from every
stakeholder — at least a
sanity check

26. Pitfalls and Problems
● AI is the wrong tool for the job, or
picking the wrong AI
● Using the wrong prompt
● Inconsistent results, especially with
edge cases
● Trying to do too much/too little
● Technical difficulties
● Duplicating effort
● AI strategy rather than strategic AI
● Measuring results
● Low-quality results

27. Examples, Use Cases, And Best
Practices

28. Freespeech
Accurate speech transcription, translation and voiceover
for creators, international organisations, and educators.

29. ● Voiceovers from AI-generated
transcripts.
● Rapid prototyping for spoken
content creation.
● Great for explainers, demos,
informational and educational
videos.
● Transcripts can be used in ChatGPT:
create an abstract or extract
highlights.
● Produce once, in one language and
expand to other languages.
Ideas for your content creation process

30. Competitor Analysis
ChatGPT can easily be harnessed to look
at competitor pages and evaluate their
use of content.
This can include things as simple as
checking keywords or as complex as
asking if there are opportunities for your
own content; for example, if a blog post
has missing information that can be
turned into an owned blog.
This is doable with zero coding now that
the GPT4 Model has access to browse
the web through the ChatGPT interface.
Example Workflow:
1. Identify top-performing competitor
content
2. Prompt: “Read the body content on
www.someurl.com/blog/article,
Summarize it for me, and identify any
opportunities to expand or provide
additional context on the topics
being covered”
3. Feed opportunities into the
content planning process to
identify if they are real and worth
pursuing

31. Derivative Works
Creating derivative works — summaries,
social posts, speech-from-text and
text-from-audio, etc. — is a prime use
case for AI; it’s tedious, does not require
any outside context, and doesn’t ask for
net new material.
While the output still needs to be
reviewed by a human, production can be
completely automated either by
manually asking through the ChatGPT
interface, or via API.
This also holds true for personalizing
content at scale when using merge
fields isn’t the best option.
Example Workflow:
1. Final draft of content produced
2. Final draft run through GPT
with prompt: “Use this content
to create a tweet, a Facebook
update, and a LinkedIn post”
3. Manually check all output to
make sure they are accurate
and fit the tone you are trying
to achieve
4. Append to final draft

32. Document Management/Discovery
AI can be a powerful connector
between content creators and other
departments.
Tools like OpenAI’s GPT can read
customer messages, interpret their
intent, and prepare response
packages that can be deployed
automatically, or by sales and
support staff.
Outsourcing intent identification frees
up XDR/support staff to engage in
higher-value activities, boosting sales
efficiency and deal velocity.
Example Workflow:
1. Lead submission comes in via
form/chat
2. Passed programmatically to LLM
to interpret intent and industry
3. Intent/industry used to search
through sales/support material
and generate response package
(e.g. relevant case studies,
brochures, capabilities decks,
support scripts, etc.)
4. XDR/support verifies materials
and sends response

33. Brainstorming / Ideation
While AI struggles to generate
high-quality content from end to end,
it can be an invaluable brainstorming
tool and sounding board.
Internally at S&G, we’ve been asking it
to expand on topic ideas, suggest
closely related topics, provide
sample outlines, and read first drafts
to identify opportunities for
improvement.
The important thing to remember is
to treat ChatGPT as an assistant, not
as a self-contained content team.
Example Workflow:
1. Identify potential topics of interest for
content creation
2. Prompt: “Suggest additional topics that
are related to, but not covered in, this list.”
3. Prompt: “Our company does X for
audience Y. Generate a sample outline for
an 800 word blog on the topic of Z”
4. Prompt: “Read the following blog post,
and identify any logic gaps or places
where additional information or context
can better convey the point.

34. Bad Use Case: Content Generation
Every marketer, business owner, and
executive has by now either gone to the
ChatGPT interface and typed in “Write
me a thought leadership post about my
industry,” or has thought about doing so.
The problem is that because of
ChatGPT’s weaknesses, the resulting
content is often incredibly weak.
Common issues include fictional
references and examples, poor audience
fit, incredibly derivative work, simplistic
and surface level understanding of the
topic, and lack of differentiation

35. Best Practices for Content Creation After AI
● Novelty is paramount. Generative AI cannot create new information, making content that
provides it immediately valuable and differentiated. Use data collection, expert input, and
unusual perspectives to outflank AI-generated approaches.
● Go beyond the prompt. Using the standard prompt interface is cheap, easy, and obvious. If you
can do it in five minutes, so can everyone else. Look for creative ways to leverage AI.
● Always check and double-check output. Generative AI, as it currently stands, is not trustworthy.
It will fabricate responses. Be thorough, and do not rely on it for anything mission-critical without
careful editing.
● Don’t make any snap decisions. Right now, we’re in the FOMO-fueled wild west. Don’t make any
drastic changes to your content program until we get more information about how things shake
out.
● “ChatGPT” is not a strategy; neither is “AI.” Unless you run an AI company, you shouldn’t be
“pivoting to AI.” It’s a tool, and it has broad applications within organizations, but it is not “what you
do” and you do not need an “AI department,” any more than you need a “Microsoft Word
Department.”

36. Learn more about S&G Content
● Check out our site: https:/
/stuntandgimmicks.com
● Get in touch: info@stuntandgimmicks.com -OR-
l.fairbanks@stuntandgimmicks.com -OR-
a.mouravskiy@stuntandgimmicks.com

37. Learn more about Freespeech
● Join Discord server: https:/
/freespeechnow.ai/discord
● Demo (90 seconds): https:/
/freespeechnow.ai/demo
● Get in touch: artyom@freespeechnow.ai