Week 1: AI Methodology, Opportunities, and Challenges

[MUSIC] Hello and welcome to our course where you
will learn about the evolving role of the AI product manager. My name is Daniel Yeomans and
I will be your instructor for this course. I have played various roles in my
career including product manager, project manager, scrum master,
and product owner. Currently, I am a college professor,
corporate trainer, and a subject matter expert
at Skill-Up Technologies. A common prioritization system I learned
many years ago was the nice to have, should have, and must have method. AI has been a nice to have for many years. However, ChatGPT was released
in November of 2022. Since that time, more and
more companies and product managers have embraced AI as
a tool to leverage when planning or developing new products or
actually including AI as a feature to embed in their new and
innovative products. Like it or not, AI is now at least
a should have for every product manager. I do not possess a crystal ball, but
I believe I can confidently predict AI will soon become a must have for
every product manager. It is a tool that will become
essential to ensure new products are viable in a highly competitive and
changing market. This course is part of the IBM AI Product
Manager Professional Certificate and the Generative AI for
Product Manager Specialization. It is designed for new or seasoned product managers hoping
to stay ahead of the AI curve. Learners should be familiar with
the product management lifecycle and product lifecycle. Completing the IBM Product Manager
Professional Certificate is optional. Module one will introduce you to
the impact of AI on product management. We will share new skills a product
manager must develop to be effective and identify the AI team and
the key roles that they play. Following that, you will learn about
the current AI methods used in product management and the benefits and
challenges that they present. In module two, you will learn about the
various stages of AI product development and how they integrate into the overall
product management lifecycle. You will also explore AI tools and examine
use cases demonstrating their value. Additionally, you will learn about
the effective commercialization of AI and identify current and future trends
impacting AI product managers. In the last module,
you will complete a final project and take a final assessment to test
your understanding of key concepts. There’s a lot to cover here. To
get the most from this course, make sure to go through the concepts and
videos, review the readings, complete the hands-on labs, and check your
understanding with the graded quizzes. If you have any trouble
with the course material, please don’t hesitate to contact
us in the discussion forum. So welcome to the course. We look forward to sharing how AI
can enhance your product concepts, address your customer needs, and provide
your firm with a competitive advantage.

Welcome to the Role of
the AI Product Manager. After watching this video, you’ll be able to: summarize the role of
the AI product manager, describe the AI product
manager’s persona, and identify key skills required to be an
AI product manager. Artificial intelligence, AI, is emerging as a must-have skill set in product management. An increasing number of
companies are using AI to drive significant advancements
in developing new products and services. We now live in a world
where self-driving cars make our lives easier and
improve vehicle safety. AI-driven computer
vision systems interpret visual data and are used
for facial recognition, object detection,
and medical imaging. Millions of people now use
ChatGPT daily to assist them, both personally and
professionally. AI is here to stay, and the AI product manager
will lead the way. An AI product manager is a
leader who should possess an understanding of basic
product management concepts, processes, and tools. They are often at the forefront
of innovation and must drive end-to-end development
of a new AI product. They normally have an
adequate technical background and understand how AI
systems are planned, developed, launched,
and delivered. The AI product manager must form and manage the
AI development team. This includes selecting the right team members
to effectively build the AI product and
ensure return on investment, ROI, for the company. This requires understanding
how to form and manage cross-functional
teams to ensure success. The AI product manager must
understand how AI can enhance the customer
experience and manage product development to meet
corporate needs and values. They work with internal
and external stakeholders to make informed decisions
about product development, feature prioritization, product enhancements, and trade-offs. The AI product manager must
also assess risks and develop mitigation plans to ensure product development
does not go off track. Personas are crafted
to represent different user segments
or customer types. They provide insights
into users needs, motivations, pain
points, and behaviors. Next, let’s explore an AI
product manager’s persona. The typical AI product manager earned a Bachelor
of Science degree. They normally have
experience working as a product manager or
in a related role. Basic understanding of
business administration, computer science,
and machine learning are normally qualifications
they possess. Next, an AI product
manager should possess a curiosity to explore
new technologies and a desire to develop
solutions that can be applied to address real-world
opportunities or problems. They should be empathetic
and can analyze data, understand patterns, and
make data driven decisions. An AI product manager must also be a good
communicator who can thrive in a cross-functional
environment and effectively build
a diverse team. The AI product manager believes AI has the power
to revolutionize industries through
the development of new products or refinement of existing products in a
matter that delights customers and enhances the
corporate bottom line. The greatest reward for an AI product manager is to see how new products and services have a tangible and positive
impact on customers lives. Balancing trade-offs and managing expectations
are key challenges. The product manager must plan for their minimum
viable product, MVP, that achieves an opportunity or addresses
the customer problem. In addition, the
AI product manager must deliver what they promise, avoid over-promising
and under-delivering. Lastly, let’s identify
key skills and abilities that are required
to be an AI product manager. You should understand the
basic concepts of driving AI. These include systems
such as machine learning, neural networks, natural
language processing, and other relevant techniques. In addition, basic data
analysis skills are necessary to be able to communicate and work with
the experts effectively. The AI product manager also defines the product concept
and product vision. They establish and manage
cross-functional teams, set schedules, and develop workflows to plan and
develop a product. The AI product manager
effectively communicates highly technical AI
solutions in a manner that is understood by non-
technical stakeholders. They must be able to effectively communicate
and collaborate with a diverse product development
team to include roles, responsibilities, tasks,
milestones, and decisions. As an AI product manager, you should understand
the strategic initiatives of the firm, be able to translate
AI-driven products in a manner that tracks with the
firm’s mission and vision, and evaluate the
financial feasibility of AI-driven product concepts. Finally, you should
gain proficiency in a variety of AI tools
to streamline tasks, improve idea generation,
enhance customer experiences, and assess organizational
readiness. In this video, you learned that AI product managers lead the development of
new AI products, possessing a mix of technical
and managerial skills. They form and manage
AI development teams, ensuring ROI and success through
cross-functional collaboration. AI product managers assess risks and ensure
projects stay on track. AI product managers
typically have a technical background and curiosity for new technologies. Effective
communication, empathy, and data driven decision
making are essential skills. Balancing trade-offs and
managing expectations are key challenges and
technical skills required include understanding
AI concepts, defining product vision, effective communication,
strategic alignment, and proficiency in AI tools.

Welcome to The Value Proposition of AI in Product Management. After watching this video, you’ll be able to define the value AI provides
in product management, identify areas not
impacted by AI. AI plays an increasingly critical role in
product management. The product manager’s job is
both critical and complex. Product managers
are embracing AI to improve product development
and functionality. They must understand AI’s value proposition
in product management. AI’s value proposition in product management includes
data-based decision-making, productivity, personalization,
trend analysis, customer feedback, and
disruptive innovation. AI allows a product
manager to access and analyze large amounts of data that a human
analyst might miss. The product manager must
make data-based decisions that impact the firm’s financial
stability and customers. A poor decision might cost affirm millions of dollars or result in negative
customer feedback. During their daily job, a product manager must
accomplish several tasks, sometimes with
differing priorities. AI can automate or
streamline data processing, customer segmentation, or
product testing tasks. This frees the product
manager to focus on high-priority tasks
requiring expertise. The product manager is the
voice of the customer. This task often includes a personalized approach
for multiple customers. This is a time-consuming and often overwhelming
requirement. AI can tailor solutions to a large customer population and address individual
customer needs. The product manager must be
able to analyze trends in a rapidly changing market to make product decisions quickly. AI models can help rapidly identify evolving
customer behavior, buying patterns, and emerging
customer pain points. Trend analysis enables
the product manager to be proactive rather than reactive
and planning roadmaps, identifying and
prioritizing features, providing pricing
recommendations and more. The market is in a
constant state of change. Customer feedback is
critical to enable firms to assess their product viability throughout the
product lifecycle. AI can monitor product growth efficiently and identify issues, gaps, or trends that
drive database solutions. AI can also recommend solutions
to maintain or improve profitability
during maturity and guide product
retirement decisions. In essence, AI can detect
problems and opportunities quicker and more
effectively than a human reviewing
feedback manually. A disruptive innovation disrupts
and eventually displaces entrenched products
and existing markets by creating new market
and value network. AI enables disruptive
innovation by fostering agility,
creativity, and adaptability. It can uncover insights,
identify patterns, and predict trends that were
previously inaccessible. AI empowers organizations to develop innovative
products and services. While product managers
need to understand AI’s value proposition
and product management, they also need to identify
areas that AI does not impact. These areas include
human judgment, ethics, collaboration,
data, and trust. Let’s learn more about them. AI is a data-driven tool
accomplished by machines. AI models do not possess human
intuition and creativity. AI is unable to perform
strategic thinking. Humans will always need to make subjective
decisions that matter. These decisions require empathy
and human understanding. AI algorithms can inadvertently introduce unwanted biases
or ethical issues. Human guidance is necessary to ensure AI systems produce fair, transparent results
and comply with legal and regulatory guidelines. Implementing a successful
AI product requires close collaboration with diverse stakeholders and
development team members. Communication is as much
an art as a science. AI models will never replace a human’s
ability to communicate effectively and adjust
their communication styles to meet the situational
needs of stakeholders. AI systems require high-quality labeled data sets
to train effectively. They rely on this data to learn and make predictions
or recommendations. While AI can analyze and
process large volumes of data, it depends on humans or other sources to collect and curate the data
needed for training. Therefore, autonomously
generating data remains a task outside of
the scope of AI systems. Product managers must
understand the importance of data collection and management
in AI driven initiatives. AI is new and often a mystery to many
uninformed stakeholders. Adopting new AI products requires human intervention,
understanding, and the ability to
guide customers through an adoption process that
is often confusing. This human touch cannot be
replaced by a computer. In this video, you
learned that AI plays an increasingly critical role in product management. Product managers must understand AI’s value proposition
in product management. AI’s value proposition and product management includes
data-based decision-making, productivity, personalization,
trend analysis, customer feedback, and
disruptive innovation. Areas that AI does not impact
include human judgment, ethics, collaboration,
data, and trust.

Welcome to A Day in the Life
of an AI Product Manager. After watching this video, you’ll be able to summarize an AI product manager’s
key tasks and identify the essential skills required to perform those tasks. An AI artificial
intelligence product manager must have extensive
knowledge in strategizing, acting as the voice
of the customer, and communicating
and collaborating effectively with a diverse team. They should understand
AI technology, apply it to product
requirement development, and stay on top of
emerging trends in AI. Let’s explore some tasks an AI product manager
performs daily. The AI product manager
must work closely with senior executives to ensure the product vision aligns
with corporate strategy. They must show management how the proposed product concepts add value to the firm
and its customers. The AI product manager is
the voice of the customer. They must be able to determine
customer pain points and develop viable
requirements through user stories that
divine customer roles, needs, and the value
of satisfying them. The AI product manager must then be able to define
the requirements for the engineering and
development teams to find potential solutions that may
include AI capabilities. An AI product
development project requires support from engineers, data analysts,
researchers, designers, marketing, and many partners. The AI product
manager must unite all stakeholders and
create a team environment. Next, the AI product manager is responsible for creating
and planning AI products. This process involves identifying AI product
opportunities, developing product concepts, defining market and
product requirements, and creating preliminary
and final business cases. The final business case outlines the product values
and key financials, guiding the development
team towards launching and delivering
the AI product. The AI product manager must also be able to work
closely with engineers and data scientists to
determine which AI tools and models are most appropriate to apply to the product concept. This selection requires
a working knowledge of AI tools and models that enable accurate product performance predictions and recommendations. Next, the manager
oversees scope, schedule, and
budget limitations. They must identify the
minimum viable product (MVP) that addresses customer
pain points effectively. Prioritizing features is
important for delivering maximum value with minimal
resources and time. Negotiation skills are vital
in achieving this balance. The manager is responsible for establishing key
performance indicators (KPIs) to measure the success
of AI product development. They must monitor progress, analyze internal and
external feedback, and make necessary adjustments
to ensure success. Additionally, they should seek out opportunities
for improvement. For a product
concept to succeed, it must align with a
viable market opportunity. The AI product manager must stay informed about
market trends, closely monitor
competitors, and identify potential market
segments that could benefit from an AI
powered product. Lastly, the world of AI
is changing rapidly. AI product managers
must devote part of their days to learning
what is new and changing. This devotion may include
reviewing research papers, attending conferences,
networking with experts, and completing AI
educational courses. In this video, you learned that AI product managers
excel in strategy, customer representation, and team collaboration with a deep understanding
of AI tech and trends. They ensure product visions
match corporate goals, demonstrating value to
executives and customers. They identify pain points, craft user stories, and effectively communicate requirements to
engineering teams. They foster teamwork and knowledge sharing among
various stakeholders. Leading product creation involves identifying
opportunities, developing concepts, and
creating business cases. They manage scope,
prioritize features for maximum value, establish KPIs, monitor progress and adjust
strategies for success, and they dedicate
time to staying updated through research,
conferences, and networking.

Welcome to Building
the AI “Dream Team.” After watching this video, you’ll be able to identify
the optimal team members for an AI product and describe critical actions essential
to build the team. Identifying and building
the AI team is one of the most critical roles an AI product manager
must perform. Successful implementation
of an AI product requires a diverse
set of managers, engineers, and developers, all working together to
achieve a complex goal. Let’s first identify the
optimal or dream team members. The AI product manager is at
the center of the team and leads the entire product
management lifecycle from conception to launch. With the help of a diverse team, the AI product manager
creates the product concept, identifies requirements,
develop solutions, and oversees the launch. Subject matter experts, SMEs, are team members who provide accurate
industry insights, market trends, and
regulatory guidance. The AI product manager
must collaborate with experts who can assist in
identifying requirements, evaluating technical
options, planning solutions, launching the product,
and ensuring excellence. Next we have the AI engineers. They translate requirements into AI-supported designs
and end products. They can identify
user-friendly interfaces that ensure the preferred
customer experience. Business analysts are the link between customers
and technology. They support the AI
product manager by conducting needs assessments,
engaging stakeholders, analyzing product
feasibility, ensuring traceability of features and functions
during development, and assisting in product
testing and evaluation. They play a crucial
role in ensuring that the final product aligns with
the initial requirements. Data Engineers, scientists, and architects are the backbone
of the AI dream team. They utilize requirements and solutions to build
models, pipelines, and designs delivering the necessary product
functionality and features as planned. They leverage AI systems
to ensure product quality, accuracy, reliability,
and performance. System architects design the
overall system architecture. They collaborate to evaluate and recommend optimal ways to
leverage the power of AI. Their work ensures that
the product is scalable, secure, and performs
as advertised. Next we have DevOps engineers. They recommend
automated tools to streamline the development
process for AI models. They ensure these models
are deployed consistently across multiple environments to meet performance
specifications. Lastly, product owners work with Scrum masters and
the development team to verify user stories, develop solution criteria, prioritize the product backlog, and develop product
increments leading to the launch of the final product using an iterative approach. Now let’s explore the
necessary actions needed to develop the individual members into high-performing teams. The Tuckman Ladder is a commonly used team
development model that outlines four stages
of team development. These stages in order
of occurrence are form, storm, norm, and perform. The AI product manager must understand this model
and ensure the team as a whole reaches the
norm and perform stages and remains there for
the duration of the project. Let’s explore each stage. The team is brought
together in the form stage, the AI product manager
defines the product needs, shares customer pain
points being addressed, and assigns the roles and responsibilities of
each team member. The product manager then
defines the requirements and shows how AI will enhance
development efforts accurately, briefly, and concisely.
During the storm stage, conflicts are promptly addressed by the AI product manager. They clarify product
goals and address individual team member’s questions to foster
collaboration, the manager ensures
each team member understands their role in achieving success
and appreciates the value of all roles involved. Additionally, they develop
a communications plan to facilitate focused and frequent communication within the team. In the norm stage,
productivity is high, but the team still
needs support. The AI product manager needs to participate in all daily
stand-up meetings, sprint demos, and
retrospectives. They should work closely
with all stakeholders, celebrate small wins, and detect issues before they
become major blockers. Try to find ways that allow team members to achieve results, grow their relationships,
and empower them to think of ways to
go from good to great. Lastly, delegation is key
in the perform stage. The AI product manager trusts the team, but
verifies progress. They create an environment
where the team is empowered to find new and improved ways to add value and stay informed. The team is now self-
organized and able to determine what needs to be accomplished with
minimal guidance. The AI product manager ensures that their
journey stays on track. In this video, you learned that the AI Product Manager leads
the team comprising SMEs, AI engineers, business analysts, data engineers,
system architects, DevOps engineers,
and product owners. The AI product manager
defines product needs, shares customer pain points, assigns roles, and clarifies requirements to enhance
development efforts with AI. There are four stages in
the Tuckman Ladder model, form, storm, norm, and perform. The form stage involves bringing the team together and
defining requirements. The storm stage
addresses conflicts, clarifies goals,
fosters collaboration, and develops a
communications plan. In the norm stage, the AI product manager
supports the team, celebrates wins and
addresses issues early. The AI product manager delegates
tasks, trusts the team, encourages innovation,
stays informed, and keeps the project on
track in the perform stage.

[MUSIC] Welcome to Communicating
AI to Stakeholders. After watching this video, you’ll be able to explain the consequences
of stakeholders not understanding AI. Discuss how to overcome resistance to
AI through effective communications. AI is a new technology that is
revolutionizing product management. However, many stakeholders lack
a fundamental understanding of AI, which raises several critical consequences,
let’s have a look at these consequences. Firstly, there’s a risk of misaligned
expectations. Product management stakeholders may have unrealistic
expectations regarding AI capabilities and outcomes, leading to dissatisfaction
with the final product. Secondly, it may lead to ineffective
decision-making as stakeholders might overlook valuable AI-driven insights or fail to capitalize on AI’s
potential effectively. Also, stakeholders may not understand AI
related features or functionalities and struggle to communicate requirements or
priorities to the development team. Ultimately, a lack of understanding
about AI may result in delays. As stakeholders and development teams
spend more time clarifying requirements or addressing misunderstandings,
it may result in cost overruns and project failure. It’s crucial to communicate
AI effectively and mitigate the risk of
stakeholder resistance. Some of the best AI
communication practices for overcoming resistance through effective
communication include targeted communication, focus on value,
clarity on concepts, use cases, addressing misconceptions,
managing change, and transparency. Let’s have a look at each
of these best practices. Understand the level of technical
expertise with AI concepts among your stakeholders and tailor your
communication to their knowledge level. Use language and
examples they can easily understand. Emphasize how AI can address
specific challenges or opportunities relevant to
the stakeholders’ goals and objectives. Highlight the potential benefits,
such as increased efficiency, improved decision-making, or
enhanced customer experience. Break down complex AI
concepts into simpler terms. Use analogies, metaphors, or real-world examples to illustrate how AI
works and its potential applications. Present concrete examples of how other
industries are implementing AI to demonstrate its effectiveness and
potential impact. Acknowledge and address any misconceptions
stakeholders may have about AI, such as job displacement or
ethical concerns. Provide evidence and explanation to
alleviate fears and build confidence in AI technology, the AI product manager
must be skillful at addressing change. Stakeholders may fear disruptions
to their roles, core processes, and how they do their job. The three steps of Lewin’s change model
can provide a recipe for success. The first step is unfreeze, which includes
sharing a compelling value proposition and showing how individual stakeholders
can transition their roles and responsibilities seamlessly. The second step is transition, which includes working with stakeholders
to implement the change and providing training as necessary
to adapt to the new environment. Refreeze is the last step, which includes showing how the new
AI-driven product or service is positive. Use rewards, incentives, and
encouragement to sustain the gain. Be honest about AI’s capabilities,
limitations, and potential risks. Avoid overpromising or exaggerating
AI’s capabilities to maintain trust and credibility. In this video, you learned that many stakeholders lack
a fundamental understanding of AI. The consequences include misaligned
expectations, ineffective decision-making, communication challenges,
and delayed development. It’s crucial to communicate
AI effectively and mitigate the risk of
stakeholder resistance. The best AI communication practices
include targeted communication, focus on value,
clarity on concepts, use cases, addressing misconceptions,
managing change, and transparency. [MUSIC]

Welcome to Challenges and
Opportunities for AI Product Managers. After watching this video,
you’ll be able to review high-value opportunities of AI for
the product manager, summarize critical challenges
impacting the AI product manager. AI is revolutionizing product management. It presents a number of
opportunities to product management. However, AI comes with its
own set of challenges too. The AI product manager not only needs
to utilize the opportunities that AI presents, but also address its
challenges to mitigate the risks and guarantee the success of the product. AI offers many high-value opportunities
to the product manager, including idea generation, task management,
decision-making, customer segmentation, competitive analysis, supply chain
management, and risk management. Let’s explore these opportunities. AI can generate product ideas, prototypes,
marketing collateral, designs, and more, it can identify market gaps. AI can significantly reduce the time
required to collect customer feedback and determine a product’s feasibility
with potentially optimal results. Product managers can effectively use AI to
streamline low-value tasks that require minimal skills and competencies. This streamlining allows
the product manager to focus on high-value tasks such as strategy and
product concept development. AI can access massive amounts
of data within seconds. This speedy access to data
has the potential to not only speed up the decision-making process, but
also ensure more effective decisions. AI can help validate the right
segments for products, provide database targeting
recommendations, support value propositions, and
develop a positioning statement. AI can scan multiple competitors’ data and
provide valuable insights to compare products and
determine differentiation potential. These insights help in
developing the marketing mix. The marketing mix, also called the 4Ps,
drives product-based decisions that impact product features and functionalities,
price, promotion, and places (locations). The 4P model expands to 7Ps
when services are involved. Additional focus areas include people,
processes, and physical evidence considerations. Supply chain challenges have been a key
issue firms must deal with in a post pandemic environment. AI has the power to analyze supply chains,
identify potential issues, make inventory recommendations, and more. AI can assist in identifying potential
risks that may impact a product launch. It also helps develop data-based responses
to mitigate the probability of risk occurring. AI does not come without its own risks and
challenges. A product manager must
address some key challenges, which include questionable accuracy,
reliance on prompts, lack of human touch, bias, intellectual property
(IP) issues, and building trust. Let’s discuss these challenges. While AI can analyze massive
amounts of data in seconds, its conclusions are only as
good as the data it uses. When asked,
are your responses always correct? Copilot, Microsoft’s AI capability,
replies, “As an AI language model, my responses are based on the information
available to me up to a certain point in time. While I strive for accuracy,
I can still make mistakes or provide outdated information.” It’s always a good idea to verify
critical details independently. AI models respond entirely
to the prompts you feed in. You must frame your question carefully
to attain your desired answer. If you ask the question poorly,
you receive a poor response. For example, asking an AI model to write
a story will likely not know what type of story you want, you will receive
an output that you may not desire. AI provides data outputs based
on the inputs you provide. AI models gather information and
assemble it in the manner we request. But here is a key point,
you are still talking to a machine, AI lacks the human touch, emotions,
intuition, and even a sense of humor. AI is a tool and has its limitations. Remember to consider
the human touch when needed. AI bias, also known as machine learning
bias or algorithm bias, refers to the bias that occurs due to human biases
that skewed the original training data, or AI algorithms, leading to distorted
outputs and potentially biased outcomes. Sample bias occurs when AI is trained on
data collected from a sample that does not represent the population it is intended to
represent, leading to distorted outcomes. For instance, if most of the data used to
teach an AI about what makes a vacation great comes from older generations, it
might prioritize relaxation and comfort. But if the data comes from younger people,
it might focus on adventure and exploration. Similarly, programmatic morality bias
occurs when an AI system trained on biased or culturally influenced datasets gives an
output that may be considered politically correct or socially acceptable, often at
the expense of fairness or inclusivity. Use of AI, particularly generative AI, raises legal questions such as how
copyright laws can apply to AI or how to determine the ownership
of AI-generated data. AI has not yet reached the point where
it can ensure that you are not accessing someone’s intellectual property. Data privacy and ethical concerns remain. AI is a relatively new technology and
many people don’t understand or trust it. Integrating AI-driven talent management
solutions within existing HR systems and workflows could be challenging. To build trust, AI product managers
must ensure seamless integration with existing tools and platforms while
minimizing disruption to HR processes. In this video, you learned that
AI presents a number of opportunities to product management, but
comes with its own set of challenges. The AI product manager needs to understand
and utilize the opportunities and recognize and address its challenges. AI high-value opportunities
include idea generation, task management, decision making, customer
segmentation, competitive analysis, supply chain management,
and risk management. AI key challenges include questionable
accuracy, reliance on prompts, lack of human touch, bias,
IP issues, and building trust.

Welcome to the Return
on Investment of AI. After watching this video, you’ll be able to summarize how AI can improve the firm’s
competitive position, define expected
costs and benefits. AI product managers
typically wear two hats. The first represents the firm. Corporate management expects
the product manager to show a quantifiable return
on investment (ROI). The second represents
the customers who expect the product manager
to be the voice of the customer and deliver
product with added value. Let’s first see how AI can improve the firm’s
competitive position. The Harvard School
of Business has developed the balanced
scorecard model. This model provides
four success factors a firm must consider to remain
competitive and viable. Categories include
finance, process, employee, and customer. AI plays a huge role in helping product managers achieve the balanced
scorecard objectives. Let’s review each success factor from an AI point of view. In the context of the
balanced scorecard, finances serve as the
fundamental metric for evaluating
organizational performance. AI can drive the development
of innovative products. It can analyze
financial data and provide valuable insights
into revenue generation, cost optimization,
and risk management. It can also identify trends and detect anomalies that traditional
methods might overlook. In the area of process, AI can streamline operations, reduce resource requirements, minimize waste,
reduce the dependency on human accomplishment
of low-value tasks, and drive efficiency
and productivity. AI can enable employees
to do more with less. Employees can focus on critical
activities that provide a sense of achievement and motivate the team
to greater heights. AI-driven products present
value propositions that can improve product
functionality and features. Firms embracing AI can offer
products with more features, perform at optimal levels, are highly reliable, and
increase customer satisfaction. AI gives the firm
an advantage over competitors who cannot embrace the opportunities opened by AI. ROI encompasses a broad spectrum of organizational benefits
beyond financial gain. The AI product manager
must understand AI, quantify and qualify
the benefits or ROI of a proposed
AI product concept, and present the business case for the product effectively. Let’s now see how AI can
define expected ROI. Hard benefits or
hard ROI derived from AI can include
verified time savings, productivity increases,
and cost factors. AI can save time by
automating repetitive tasks. For example, automated
invoice processing and tracking can reduce manual analysis time on managing accounts and improve
collection performance. The AI product manager must be able to quantify
the time savings and show how AI can be
used to achieve higher-priority
organizational objectives. A product manager can use AI to show a quantifiable
increase in productivity. For example, a firm
employed five resources and spent 300 hours managing a manual accounts
receivable process. The firm also paid the employees a total of $125 per hour. AI reduced this
time to 150 hours. Thus, AI managed a savings of $18,750 monthly or
$225,000 annually. This amount is ROI. AI can drive cost savings. It can reduce dependence on labor-intensive
manual systems. AI can reduce costs by
eliminating the need for specific equipment that cost thousands of dollars
in annual maintenance. AI can also increase
revenue streams. New products may exceed the competition’s offer
and sales may soar. AI product managers can
also use soft benefits, or soft ROI, to show the economic benefits
of an AI product. Soft benefits are difficult
to quantify but still matter. They include employee
satisfaction, achievement and motivation, customer satisfaction,
and valuation. Employee satisfaction
and retention are significant benefits of AI. Statistics prove that it costs
a firm far more money to orient a new employee than to retain an existing
skilled employee. In addition, satisfied employees are often more motivated
and productive. Employee job satisfaction increases morale and engagement. Employees are often
motivated by achievement. AI allows employees to learn new skills that can
advance their careers. This motivation may encourage high levels of
employee retention. The company also benefits from heightened employee skills. AI-driven products provide
improved functionality, a positive impact on the
perception of the brand, and more customer trust. Trust leads to greater
customer satisfaction and, in all likelihood,
increased sales. Successful AI adoption
and success can increase the overall value of the company and delight shareholders. ROI is expressed
as a percentage. It is calculated by an
investments net profit or loss by its initial
cost of investment, where net profit is
the current value minus the cost of investment. Let’s illustrate this
using the accounts receivable productivity
savings example. Assuming we invested $100,000. Recall that
productivity benefits were calculated at $225,000. 225,000-100,000/100,000
= 125% ROI. In this video, you learned that AI product managers
are expected to wear two hats that show a quantifiable ROI and be
the voice of the customer. AI plays a huge role in helping product managers achieve the balanced
scorecard objectives. The balanced scorecard model provides four success
factors: finance, process, employee, and customer. AI can define expected costs and benefits regarding
hard or soft ROI. Hard ROI includes
verified time savings, productivity
increases, and cost. Soft ROI factors include
employee satisfaction, achievement and motivation, customer satisfaction,
and valuation.

Welcome to The AI Process. After watching this video, you’ll be able to
explain the AI process, define the AI process team, and its rules, discuss the product manager’s
involvement in the process. The product manager must
understand how AI works. They must use this
understanding to ensure that the critical development
team members work together toward a common goal of a
successful AI product launch. AI combines large data sets, establishes data
pipelines, creates algorithms using data patterns,
and provides outputs. AI self-corrects and learns from failures when
outputs are not adequate. The AI process
involves four steps. Data inputs, data processing,
outputs, and updates. Data is the primary
fuel that drives AI. Data professionals collect
data from various sources and develop algorithms
by categorizing data into readable formats. They establish pipelines to
ensure data flows smoothly. Note that an AI algorithm is
a set of instructions that enables a computer or machine
to perform specific tasks, analyze data, and
make decisions. Algorithms analyze
the input data. These algorithms are only as practical as the provided data. Algorithms drive outcomes. The system can now analyze data, recognize patterns,
and predict outcomes. The team uses these
outcomes to make decisions. AI can learn from its mistakes. If a data set is
considered a fail, AI systems adjust algorithms and repeat the process
to improve accuracy. The AI process team
includes data scientists, data engineers,
processing specialists, domain experts, and machine
learning engineers. All these roles are critical to the level of this process. Let’s see what this team, on a high level, must
cumulatively achieve. The team must collect
significant data, including text,
images, and sensors. It’s critical to identify the required data and a
process to access it. Once the team collects
the raw data, it must process it as it
may include omissions, inaccuracies, or
extraneous information. The team must clean,
transform, normalize the data, and then establish
data pipelines to satisfy data
feed requirements. The team then needs to
develop algorithms and models to define the features and functionality the product needs. Algorithms must be precise and are dependent on the
right data inputs. Once the team understands the business need and
the systems knowledge, it starts experimenting with different AI models
to select the best. Selecting appropriate
AI models to provide essential outputs
is the team’s next task. The team then starts configuring the selected models to perform so that the
results are accurate, without bias, and as required. What works in a
laboratory setting may not function well
in the real world, so the team must
perform alpha, beta, and other tests and analyze all the feedback to
validate the results. It is crucial to ensure that the outputs meet requirements. Model development is
usually iterative. The team may often take
two steps forward and one step backward before reaching
the intended destination. The team will need to
fine-tune models as necessary. The team needs to support
AI model deployment, gather feedback, and
update as required. Support the development
process and follow up to ensure operations meet
customer expectations. The AI process is complex and requires an efficient
product manager. An AI product manager must have skills in areas like
technical knowledge, collaboration, management
and leadership, communication, tools and techniques, and business skills. Let’s learn about
each of these skills. Product managers must gain as much technical knowledge
about AI as possible. They must network with technical and management
professionals to learn more about AI. An AI product manager
needs to understand each team member’s
role and collaborate effectively with them to
achieve optimal results. The product manager
must also work to remove any potential
development blockers. A product manager
needs to manage the product management life
cycle from end to end. This includes identifying
the market problem, developing a product strategy, defining key roles
and responsibilities, and developing a
cross-functional team. Communication is
an essential skill for any product manager. They need to define
data requirements, oversee data processing, ensure data pipelines
are in place, and evaluate AI performance. They also need to explain AI to non-technical
stakeholders. Product managers
must use the tools required to collaborate
with the team effectively, ensure the correct
information and the right amount reaches the
right team members promptly. Product managers must work diligently to improve
their business acumen. In addition, they must hone their strategic thinking,
problem-solving, decision-making,
negotiation, influencing, and conflict management skills. In this video, you learned that the AI process
involves four steps, data inputs, processing,
outputs, and updates. Data inputs include collecting
data from various sources, developing algorithms, and
establishing data pipelines. During the data processing step, algorithms analyze
the input data. During the output steps, the AI systems analyze data, recognize patterns,
and predict outcomes. Updates include
adjusting algorithms and repeating the process
to improve accuracy. The AI process team
includes data scientists, data engineers,
processing specialists, domain experts, and machine
learning engineers. AI product managers must have
AI expertise, technology, management and leadership,
communication, tools and techniques,
and business skills.

[MUSIC] Welcome to Why AI Product
Development Projects Fail. After watching this video, you’ll be
able to identify examples of AI product development projects that failed and
describe the reasons for the failure of AI product
development projects. According to a Forbes report,
approximately 95% of all products, including those incorporating AI, fail. The road to AI product success is littered
with failed product development projects. Let’s see some examples
of failed AI products. Tesla pioneered using AI to provide
an autopilot feature for their vehicles. Unfortunately, there were many
system failures and crashes. Many consumers still doubt Tesla’s
ability to deliver a safe solution. Amazon took years to develop an
AI-automated recruitment system. It was shut down within hours of its
launch when it was found that it discriminated against female candidates. General Electric, GE, developed an
AI-based technology to enhance operations, solve business problems, and
transform the generation and management of electricity worldwide. After investing billions of dollars, this
project failed to achieve these goals, and ultimately, GE was forced to
reorganize to survive as a company. A few widely reported incidents showed
that law enforcement agencies used AI-generated facial recognition systems to
arrest individuals who later turned out to be innocent. The key reasons why AI product development
projects often fail include poor strategy planning, inefficient management,
new technology, and personnel issues. Let’s look at these reasons. Inefficient strategy planning includes
poorly defined business objectives, inefficient change management,
and poor return on investment. These factors can lead to
an AI project failure. A lack of clear and well-defined
business objectives leads to failure. A product manager must identify
a specific market need or problem and describe a potential solution. All key stakeholders must agree on this
product concept to be deemed feasible before moving forward. Each AI product goes through a series
of planning and development steps and validations in its development journey. A product manager must define the product
concept and solicit feedback and input. Establishing review points and ensuring the initial product
vision remains valid or essential. Product managers must weigh the cost
of deploying an AI solution against the financial benefits and base their recommendations
on data instead of emotions. A firm will shut down an AI initiative
if it does not provide economic value. Many AI products fail due to
inadequate management and leadership. Product managers must educate
stakeholders, manage communication, and ensure that the team follows ethics. Many stakeholders are unaware of AI and
its impact, which can lead to low commitment levels
to support AI product development. AI product managers must
educate stakeholders on how AI will provide value for
the project. AI product managers must identify the team
members from diverse functions or silos, such as data scientists, engineers, and business analysts to ensure
the success of the product. They must develop an effective
communication plan across the silos to ensure all team members are on
the same page about the product vision, solution, and development plans. AI projects come with ethical challenges, such as data bias, privacy issues,
and intellectual property violations. AI product managers must implement
safeguards to overcome these challenges. AI technology is new and exciting. However, it can pose technical
challenges such as lab versus reality, data management, algorithms, and AI tools. Many AI models work well in
a lab environment, however, they may not perform well when
deployed in the real world. AI product managers must conduct
extensive beta testing before launch, enlist the support of user experience
(UX) professionals, and ensure the AI product is efficient and
error-free. Data is the foundation for AI products. AI product managers must work with the
experts to ensure that data is accurate, free of bias, and complete. Unreliable or incomplete data
will lead to product failure. Algorithms are sets of instructions
that computers follow to perform tasks like processing data and
making decisions, so the decisions are as
good as the algorithms. To make efficient decisions, AI product
managers must ensure that the algorithms are accurate, complete,
and free from bias. Some AI teams are more concerned about the
AI tools they will use than the customer’s needs they support. AI product managers must focus
on the customer’s needs and pick tools best suited to satisfy them. The need should drive the tool. AI product development project
require highly technical experts. AI product managers must
invest in internal training or hire experts from outside. Although it can increase costs and
slow down the development schedule, not investing in it may result
in lower-quality products and lost competitive advantage
in the long term. In this video, you learned that
many AI products fail often. The key reasons for AI product failure
include poor strategy planning, inefficient management,
new technology, and personnel issues. Poor strategy planning includes
poorly defined business objectives, inefficient change management,
and poor return on investment. Inefficient management includes
poor understanding of AI, ineffective communication, and
not following ethical practices. Technology challenges include lab
versus reality, poor data management, inefficient algorithms, and focus on
AI tools instead of customer’s needs. AI product development projects require
highly technical experts. [MUSIC]