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A Blueprint for CMO Success in the High-Stakes AI Marketplace
And how to prevent PR disasters & prove multimillion-dollar value with your initiatives.
Welcome!
Whether you’re just starting your AI journey, or you adopted the tech early and now you’re a wily veteran,
this e-book can help. It will provide the insights and tools you need to implement a new program or
maintain and refine an existing one. Our only goal is to ensure you stay competitive in what has become
a cutting-edge and evolving landscape.
We’ll cover:
•
The current state of AI
•
AI’s real-world impacts
•
Scaling your business with AI
•
Common AI pitfalls to avoid
•
The importance of privacy & security
•
How we can help you get started
Your journey to harnessing AI’s full potential begins now. Let's get started!
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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CHAPTER 1: The State of AI
AI’s Real-World Impact
There was a time when artificial intelligence (AI) and
machine learning (ML) were futuristic concepts
In retail, for example, AI enhances CX through personalized
incorporated into science fiction fairytales. It’s safe to
recommendations and streamlined supply chain operations.
say that time has passed. The AI race began in 1950
Healthcare benefits from AI-driven diagnostics and patient
with Alan Turing’s seminal paper, “Computing
care management systems, while financial services leverage
Machinery and Intelligence.” Today, it has grown into a
AI for fraud detection, risk assessment, and personalized
$300 billion industry that has reshaped business and
banking experiences. Manufacturing industries use AI for
redefined marketplaces, indicating radical implications
predictive maintenance and optimizing production processes.
for marketers.
While AI’s business impact is vast, we’re also seeing evidence of transformation across traditional marketing channels.
Forward-thinking marketing leaders see the AI
revolution as an opportunity to enhance customer
experiences (CX), optimize marketing strategies, and
drive growth. But they need to act now and work
Google and Yahoo are optimizing inboxes to respond to the
efficiently. As the world grapples with the sheer depth
influx of AI-written emails and forcing email marketers to
and breadth of AI applications, this technology embeds
elevate their strategy from “spray and pray” tactics. The
itself deeper into industries, upending and transforming
sophisticated AI algorithms personalize inboxes based on
traditional business operations.
open, click-through, and unsubscribe rates in a way that
emphasizes more desirable email content and moves less
impactful email sends to junk. Similarly, Google rolled out its
Gemini AI responses to the search engine result page (SERP)
in May. Marketers should closely monitor their organic search
traffic and optimize content marketing strategies accordingly.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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The Way Forward
Many CMOs recognize the necessity of developing an AI strategy
and work to avoid falling prey to the “shiny, new object” fallacy. The
allure of AI’s potential can be overwhelming, leading to uncertainty
about where to start. Without a clear strategy, marketers risk
implementing AI haphazardly, resulting in missed opportunities and
suboptimal outcomes.
Whether you’re just starting your AI journey or looking to refine your existing strategy, this e-book will provide valuable guidance to help you stay competitive in the rapidly evolving AI landscape.
Successfully implementing AI starts with knowing where to begin.
The process can be complex and potentially costly, but partnering
with experts who have experience and a proven track record in AI-
optimized CX, AI data foundations, powerful data analytics, and
robust security and compliance measures can significantly ease the
journey. A good partner can provide the necessary guidance and
support to navigate the intricacies of AI, ensuring that your
implementation is effective and efficient.
In the following chapters, we’ll explore real-world examples, best
practices, and expert insights to equip you with the knowledge and
tools necessary to harness the power of AI effectively.
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CHAPTER 2: Understanding AI in a Business Context
Generative vs. Predictive AI
Generative AI refers to algorithms that create new data instances
resembling a given dataset. These algorithms learn patterns from input
data and generate output that’s similar but not identical to the input.
Generative AI is powerful in creative fields where new and unique content
is valued. It generates ideas, designs, or solutions that were not explicitly
part of its training data. While AI-generated content is not technically
unique or novel — it’s basically the world’s most efficient copycat! —
copyright laws have failed to keep pace with technology. These laws are
evolving, and the U.S. government uses a sliding scale to determine what
constitutes an infringement. Still, the most common applications of
generative AI include:
•
Text: Tools like GPT-4 can generate text from a given prompt,
making them useful for content creation, translation, and
conversational interactions.
•
Images: Models like GANs (Generative Adversarial Networks)
can produce realistic images from text descriptions.
•
Music & art: AI can compose music or create visual art that
mimics specific styles or themes.
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Conversely, predictive AI makes predictions about future
Generative and predictive (also referred to as
events based on historical data. These models analyze
traditional) AI serve different purposes, but both are
past data to find trends or patterns that inform strategic
needed to advance an emerging program. For creative
planning and resource allocation. The most common
industries, generative AI can provoke thoughts and drive
applications include:
innovation; for data-driven or operations-focused
industries, predictive AI can help with planning and
•
Forecasting: Used in finance to manage risk, in
decision-making. But, for many industries — healthcare,
meteorology for weather forecasting, and in supply chain
fashion, tech, to name a few — combining both types of
management for demand forecasting.
AI can turn cutting-edge consumer behavior trends into
•
Risk assessment: In healthcare, it can predict disease
innovative new customer offerings.
outbreaks or patient readmissions. In finance, it assesses
credit risk and fraud detection.
•
Customer insights: In business, predictive models analyze
customer behavior and predict future buying patterns,
aiding in personalized marketing and sales strategies.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Using AI to Scale Your Business
Artificial Intelligence and Experimentation have a symbiotic relationship. AI is changing what’s possible from an Experimentation
standpoint, giving your product managers, UX strategists, and data scientists more time to focus on novel innovation and
experimentation ideas instead of maintaining run-of-the-mill CRO tests. Experimentation techniques allow business leaders to test
and measure their AI products, techniques, and strategies to ensure quicker paths to a return on investment. The Artificial
Intelligence + Experimentation continuum includes the following modules that layer on one another to create a profitable program:
•
A/B Testing is the most basic form of experimentation, but it is often the most impactful. These experiments can lead to true innovation and design thinking, and every program should get the basics of A/B Testing down before they try to add AI into the mix. Even once simple experiments are automated, big bets will still get high-touch, product manager interaction. Think: Amazon One-Click Checkout. Segmentation allows for more targeted experiences through data-driven user personas and consumer behavior analysis. Once you master macro-level A/B Testing, it’s time to apply that formula against various user segments. Additional investments are often required during this phase of experimentation such as connecting a testing tool to a CRM or CDP or conducting advanced analytic techniques such as cluster analyses. Multi-channel experiments let you test out strategic user journeys and cross-channel consistency. However, it requires tracking investments to understand your customer journeys fully, and ensure consistency across multiple channels, particularly as cookie best-practices evolve. For both segmentation and multi-channel approaches, loyalty programs that encourage multi-channel sign-ins and data opt-ins can be helpful mechanisms to acquire and connect data. Machine learning measurement applies automation to the previously mentioned methods to increase experimentation velocity and unlock 1-to-1 personalization, but it requires investment in a larger infrastructure. This strategy can help capitalize on ROI with a multi-armed bandit deployment. Generative AI incrementality allows your multi-armed bandit and operationalized measurement machine to test experiences at scale. It’s best to stick to small changes that don’t introduce too much risk—e.g., button color changes—and define clear success metrics. Even with heavy automation, it’s important to keep a human in the loop to avoid adverse outcomes and monitor the algorithms for overall learnings and assess when it’s time to retrain and models.
•
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CHAPTER 3: Common Pitfalls in AI Programs
Understanding Data Sources & Training/Maintaining AI Models
When building an AI program, it’s essential to understand the primary data types and the roles required for effective
deployment. Companies new to AI typically deal with structured data sets, which are organized in a predefined manner,
often in spreadsheet programs such as Excel or relational databases such as SQL Server. Examples of structured data include
customer records, transaction histories, and inventory logs—all of which can be easily searched, analyzed, and organized.
But, to leverage generative AI models effectively, companies need to be comfortable with unstructured data, which does not
have a predefined format. Unstructured data includes text documents, images, videos, social media posts, and other content
that is not easily searchable or analyzable without specialized tools. This data requires different storage and processing
approaches to train AI models effectively.
Operationalizing these models takes skills and expertise, and often, several people to build and maintain them. Understanding
the nuances of each role will help you build a capable AI team that is prepared to manage model training and
implementation. The three roles needed include:
Business Expert
Engineer
Statistician or Data Scientist
This person understands your
Responsible for the AI model’s coding,
This role involves understanding the
company, the AI strategy, and
operationalization, and maintenance.
underlying mathematics and fine-
generally, how the AI model works.
This person often supports the data
tuning the model. This person builds
Their knowledge bridges the gap
aggregation and pipeline requirements
the algorithm and ensures its
between technical implementation
to “feed” the AI model. This person
accuracy and relevance to your
and business objectives, ensuring
ensures the model is efficiently
company’s unique use case.
that the AI solutions align with your
integrated into your company’s
strategic goals.
systems and performs reliably.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Great Data Means Great Responsibility
Establishing a Culture of Best Practices
Using AI and machine learning in data analytics has significant ethical
and legal implications. As AI capabilities grow, organizations can
Implementing an AI program is challenging,
predict and uncover personal information before it becomes public.
delicate, and time - consuming. O rganizations
This capability raises fundamental questions about who can access
must build an internal culture of compliance
that data and how it’s used.
with company policies and legal requirements
to succeed. The legal landscape surrounding
An incident involving Target’s predictive analytics is a perfect example
data usage is complex and rapidly evolving.
of retail marketing gone awry. The company sought to identify pregnant
customers early on to target them with marketing materials. Target
L aws like the General Data Protection
statisticians successfully developed a model that could predict
Regulation (GDPR) in the E U and the California
pregnancies based on shopping patterns before the news was publicly
Consumer Privacy Act (CCP A ) in the U. S . set
known. However, when a teenage girl’s pregnancy was revealed to her
strict data collection, usage, and protection
family through Target’s marketing materials, it caused significant
guidelines. The U. S . government also leans on
distress and controversy.
the Consumer Online Privacy Rights Act
(COPR A ) and the SAFE DATA Act to further
Another example includes the 2018 Cambridge Analytica scandal.
protect citizens’ privacy. Finally, U.S. Code §7216
It was eventually revealed that the company used 50 million Facebook
governs the disclosure and use of tax return
profiles to build political advertising models to sway potential voters.
information, which can be used in myriad ways
A manipulation that led Facebook’s parent company, Meta, to pay
to commit fraud and / or identity theft.
$725 million to settle the case.
While these cautionary examples made headlines, companies are
successfully using AI for good. Arguably, the best AI goes unnoticed.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Of course, non-compliance with these laws means hefty fines and legal repercussions. But committing to industry best
practices goes beyond mere legal compliance. It means being transparent about data practices, ensuring AI systems are
unbiased, and prioritizing user consent and control over personal data.
Additional steps could include:
Empowering Adherence
A company’s ethical framework should ensure that all team members know the correct procedures regarding compliance and
ethical behavior.
A robust This means understanding the policies and having the courage and support to stand behind them.
support system for reporting and addressing ethical concerns fosters ethical behavior and proactively corrects deviations.
Avoiding Unethical Data Collection
Companies must avoid deceptive practices to install cookies or collect data without customer consent. Transparency in data
collection and usage is crucial to maintaining consumer trust and staying legally compliant.
Respecting Customer Preferences
Organizations can, and sometimes do, bypass user preferences to gather more complete data. Other times, honest mistakes
are made. Health platforms GoodRx and BetterHelp in 2023 unknowingly shared customer health data with third-party
advertisers and found themselves in violation of FTC regulations and HIPPA privacy rules. In their case, they didn’t know how
much customer data was being gathered and shared from their tracking pixels. Whether it’s intentional or deliberate, failing to
respect customer privacy damages consumer trust, undermines the company’s reputation, and jeopardizes revenue.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Having the Right AI Strategy is a Must
Building an AI strategy is about identifying what problems AI can solve and understanding how and when to deploy the
solutions. Two approaches have risen to the forefront in most marketing boardrooms.
The “tool-driven” approach sees AI as a technological solution to your business problem. The dialogue often goes like this: “AI is
intriguing. Everyone is using it. We cannot be left behind. So, what business problems can we solve with it?” But this approach is a
self-fulfilling fallacy because it already assumes that AI is the solution.
Instead, smart CMOs are shifting their mindsets to the “problem-driven” approach. It focuses on evaluating each business
problem individually, first asking if AI can and should be used to solve it. Only when the answer to both questions is ‘yes’ does the
conversation switch to how AI can provide the best solution.
This measured approach helps organizations avoid the shiny-object trap and ensures the technology is used meaningfully,
effectively, and ethically.
It’s also important to note that AI solutions rarely function perfectly off the shelf. These sophisticated tools need time to test,
iterate, and learn, especially when they’re applied to specific company data. Intelligent marketing leaders embrace imperfection
and plan for ample time for refinement.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Another critical aspect of building an effective AI strategy is aligning it with your company culture.
Some organizations are risk-averse; some are more inclined towards innovation. Either can successfully support an AI strategy,
but your culture will influence how the program is implemented and maintained. Considerations for marketing leaders include:
Security & Risk
Return on Investment (ROI)
Operational Approach
A risk-averse company may prioritize
Understanding the ROI timeline is
Companies need to decide if they
stringent security measures and
crucial. Wrangling unstructured data
prefer a federated model, which
thorough risk assessments before
sets and real-time models can incur
allows for rapid experimentation, or a
deploying AI solutions. Maverick
significantly higher expenses than
more centralized, controlled approach,
organizations comfortable with
traditional business intelligence. You
which focuses on precision and risk
moving fast and breaking things can
may have to invest in AI for an
mitigation.
deprioritize some components while
extended period before seeing
still adhering to best practices.
significant rewards. Not everyone has
the appetite for such a commitment.
Building an AI strategy involves careful consideration of the technology’s problem-solving capabilities vs. the company’s
business goals, sufficient time for iterative testing and learning, and alignment with the company’s risk vs. reward threshold.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Privacy & Security Are Paramount to Success
Privacy and security in AI programs ensure the data’s
These examples highlight the need for companies to safeguard
integrity, confidentiality, and availability. AI models rely
their data and, if necessary, mandate new behaviors. Here are
upon vast amounts of data to function, and when that
five simple but effective ways to keep your data safe:
data is compromised, inconsistent, or untimely, the
•
Encryption: Protect data during storage and transmission
consequences can be costly and embarrassing.
to prevent unauthori z ed access .
Look no further than the Equifax data breach in 2017
•
Access control: Regulate access via the principle of least
that exposed the personal information of 147 million
privilege ( PoLP ) , ensuring employees have access only to
people, leading to a $700 million judgment and
data they need to complete their tasks .
reputational damage for the company. The 23andMe
•
Multifactor authentication (MFA): Adds an extra security
breach in 2023 is another poignant example. Bad actors
layer by requiring multiple verification types before
used “credential stuffing” to unlock and expose sensitive
granting access to sensitive data .
health, family, and genetic information for almost 7
million users. Later that same year, cyber attackers
•
Data loss prevention (DLP): Build and deploy strategies to
targeted MGM Resorts with social engineering and
detect and prevent data loss, leakage, or misuse .
ransomware, costing the company an estimated $80
•
Regular security audits: C onduct periodic assessments to
million in revenue over five days.
identify and mitigate vulnerabilities in the system.
Even more recently, Change Healthcare, a subsidiary
Embracing AI is an opportunity for brands to become market
of UnitedHealthcare, succumbed to a ransomware
leaders. By leveraging AI thoughtfully, you can unlock incredible
attack in late February 2024. The attack disrupted key
potential and drive growth—all while staying ahead of your
healthcare operations like payments to providers,
competition. And yes, AI is powerful and transformative, but it’s
eligibility checks, and prescription fulfillment for weeks.
safe and easy to navigate with the right approach. If your
Further, an estimated one-third of Americans may
company doesn’t have the capacity or expertise to build and
have exposed data as a result.
manage an effective AI program on your own, enlisting an
expert partner is highly recommended.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Remove the guesswork with our AI Center of Excellence
If you’re a marketing leader looking to leverage AI but are unsure where to start, don’t worry. We’ve got you covered.
Concord’s AI Center of Excellence (COE) roadmap program removes the guesswork from AI implementation. We work with
you to assess your people, processes, and tools to identify high-value use cases. As we build your COE, we’ll help you:
•
Gather trustworthy & reliable data: Data is what trains your AI. Inconsistent, untimely, or bad data means never
realizing true ROI.
•
Transition to a cloud-based infrastructure: Cost efficiencies in cloud-based data storage and consumption mean
AI is accessible to just about every company.
•
Implement data privacy & security best practices: Consumers are more aware than ever of how valuable their
personal data has become. So, companies must prioritize their trust over their data.
•
Build an experimentation architecture: A customized incrementality plan is necessary to ensure your AI is learning
to solve your business problems.
We can help you build a customized COE roadmap that prioritizes your company’s needs and moves at a pace your
stakeholders are comfortable with.
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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Let’s get started.
Integrating AI and machine learning into business is a transformative force reshaping industries across the globe. From
enhancing customer experiences to optimizing operations, the potential benefits of AI are vast and undeniable.
However, harnessing this potential will require thoughtful and strategic implementation.
A successful AI implementation starts with a clear understanding of where to begin. The process can be complex and costly, but we’re here. And we’re not going anywhere. We can help you avoid potential pitfalls in your journey because we’re experts, and that’s what we do.
But it’s time to act. The rapid evolution of AI means that those who
hesitate risk falling behind. Embrace the opportunity to drive growth,
become a market leader, and differentiate your product or service. We’ll
be there every step of the way.
Contact Us
Concord | concordusa.com
952-241-1090
info@concordusa.com
A Blueprint for CMO Success in the High-Stakes AI Marketplace
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