Digital leaders must be able to explain how AI can impact the business and contribute to measurable outcomes, such as a percentage increase in revenue.
- With only 35% of IT and non-IT executives believing that their organizations currently have the required digital leadership skills, the opportunities for digital management education are growing exponentially.
- The success rate of today’s digital initiatives resembles the traditionally low success rate of IT projects. We continue to make the same mistakes.
Considered by many as the next industrial revolution, Artificial Intelligence (AI) will be more transformative in a shorter period of time than stone tools, controlled use of fire, the wheel, clothing, agriculture, alphabets, printing, vaccines, incandescent light, telephones, the steam engine, flight, antibiotics, television, computers, the internet, fusion energy, etc.
Like other new technologies, it is likely that we will overestimate the impact that AI will have in 3 years but underestimate the impact that we will experience in 10 years.
One thing is certain and that is that AI is (and will continue to) providing a profound competitive force. With competitive opportunities, and cross-organization workflow, and compliance requirements occurring across and within every industry (e.g., Pharma, Financial Services, Healthcare, Government, Retail, Manufacturing, Petro, Hospitality, Education), no one is immune from the benefits (and challenges/considerations) provided by AI technologies. Naturally, having accurate, accessible, secure data is fundamental to a successful AI initiative, thus providing additional topics addressed in this program.
10 Popular Examples of AI Use Cases
- Service process operation optimization
- New AI-based products & services
- Customer service analytics
- Customer segmentation
- AI-based product research & enhancements
- Customer acquisition and lead generation
- Contact center automation
- Product feature optimization
- Risk modeling and analytics
- Predictive service and intervention
Since generative AI hit the scene in 2022, it has swiftly permeated enterprise solutions from leading vendors. From tools that let organizations blend their data and processes with large language models, to AI features within major productivity suites, the technology's reach across business functions has expanded rapidly.
With AI's advancements, distinguishing between a chatbot and a human has become increasingly difficult. The Turing Test has long served as the benchmark for evaluating computers' ability to mimic human intelligence.
A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.
AI is on the top of every emerging technology and investment list, and has become an essential, disruptive, and universal game-changing solution.
AI requires a team with a diverse skillset that goes beyond just technical expertise. Data scientists and engineers are crucial for building the models, but without a solid understanding of business needs on board as well, these models’ risk being irrelevant. A real dream team for AI success will blend technical prowess with business acumen. The lineup should include domain experts who understand the specific business challenges that the AI is designed to address, as well as change management specialists who can navigate the cultural transformation that often necessary to fuel broad AI adoption. Don't forget the importance of so-called softer skills such as communication and collaboration as well. A team that can effectively communicate the value proposition of AI to stakeholders and foster a collaborative environment where data scientists and business leaders work seamlessly together is essential for achieving long-term success.
While the full promise of AI is still uncertain, its early impact on the workplace can’t be ignored. It’s clear that AI will make its mark on every industry in the coming years, and it’s already creating a shift in demand for skills employers are looking for. AI has also sparked renewed interest in long-held IT skills, while creating entirely new roles and skills companies will need to adopt to successfully embrace AI.
The rise of AI in the workplace has created demand for new and emerging roles in IT and beyond. Chief among these are roles such as prompt engineers, AI compliance specialists, and AI product managers.
Other emerging roles include AI data annotators, legal professionals specializing in AI regulation, AI ethics advisors, and content moderators to track potential disinformation around AI.
Organizations are also seeking more established IT skills such as predictive analytics, natural language processing, deep learning, and machine learning. In addition to these skills, there is also an uptick in demand for skills around large language models, ChatGPT, and similar generative AI bots.
AI has also created a demand for new C-suite roles focused purely on leveraging generative AI throughout all aspects of business—from internal ways of working to external AI-powered product solutions for customers.
This has sparked conversations around ethics, compliance, and governance issues, with many companies taking a cautious approach to adopting AI technologies and IT leaders debating the best path forward. AI can and should be harnessed for the betterment of society, but it must be done responsibly and with robust governance frameworks in place. In addition to the "usual" considerations when introducing new technologies, addressing the security, moral, and ethical challenges inherent with AI initiatives is receiving significant attention around the globe. Considerations including political misrepresentation, distinguishing a human from a machine (e.g., emotion, envy, anguish, joy, philosophy), legal (is it the machine, programmer, designer, etc. to be held accountable).
AI models are programmed by people based on available data. But which people and what data? We need to avoid the errors and biases that could undermine the validity and reliability of AI systems.
Three Laws of Robotics (often shortened to The Three Laws or Asimov's Laws) introduced in his 1942 short story "Runaround" (included in the 1950 collection I, Robot) are influencing AI deployment:
First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.
Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
Added Zeroth Law: A robot may not harm humanity, or by inaction, allow humanity to come to harm.
Luftman’s Addendum: Define universal AI governance, regulation, compliance and accountability internationally to ensure the above and to easily verify truth and to combat misinformation, disinformation, and lies.
Having technical skills alone does not address the pervasive persistent IT-business alignment conundrum demanding IT and non-IT organizations working in harmony to identify opportunities for leveraging AI, and the newer concern to address the AI security, moral, and ethical challenges!
As part of an independent 4-course Certificate, or an all-inclusive Deploying Analytics Certificate (Big Data, Business Intelligence, Knowledge Management), or Technical Training Certificate, candidates will learn how to harness these different AI technologies to meet specific business needs/objectives while identifying innovative ways to reach new customers, maximize efficiency/effectiveness, and drive profitable growth.
All Aspects of the Data-AI Value Chain Must Be Considered
GIIMs AI courses prepare candidates for careers supporting this evolving field, including the driving forces behind industry specific opportunities and considerations. In these AI courses, participants will understand the various technical, management, legal, and ethical considerations for selecting a technology/platform, and effectively applying the technology in real-world applications. While there is clearly a growing need for domain and organizational knowledge associated with AI, as it’s vital to have a deep understanding of organizational needs to determine which AI technologies will be best suited to a given application, much of the discussion around AI in the workplace has been about the jobs it could replace.
The GIIM courses below focus on closing the technical and management, leadership, business, interpersonal, and industry skills that are essential. The
courses enable candidates to expand on their previous education and experience in math, statistics, data, and programming, to organize, analyze, and visualize data to uncover hidden solutions that challenge traditional business assumptions to produce entirely new operating and strategic AI models. Once completed, candidates will be be in a position to take advantage of the increasing opportunities in a wide range of AI, data science, and advanced analytics roles, that go well beyond just the technical skills typically focused on.
Required: Deploying AI Technologies
*
Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. While AI having a relatively long history, it is now emerging as an essential technology across every industry. Artificial intelligence (AI) is an academic term that has been seized upon by the media, marketing departments and commentators as shorthand, and to add narrative spice. The now-dominant AI term includes physical and software robots and tools including ‘robotic process automation’, ‘cognitive automation’ and ‘artificial intelligence’.
The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, these courses are for you. This foundational technical course will enable candidates to understand the essential concepts for implementing AI initiatives. Upon completion of this course candidates will be competent in Machine Learning concepts, AI Techniques, Cognitive Computing, and Deep Learning techniques using Python (the open-source software/programming library designed to conduct research and build solutions in machine learning and deep neural network structure; alternative programming languages/products will also be covered).
The focus of this course will be on assimilating the concepts of Machine Learning and Deep Learning with relevant industry specific algorithms, to build artificial neural networks and traverse layers of data abstraction, and to understand the power of data in the candidates’ new role as a Technical AI professional. The concepts of Neural Networks, Artificial Neural Networks, Natural Language Processing and working with libraries like NLTK, MatPlotlib, TFlearn, Keras &Tensorflow, along with current and emerging industry projects, will also be covered. Specific and generic industry examples and emerging applications and AI technologies, and approaches for deploying AI will be the emphasized throughout this course.
At the end of this course candidates will be prepared to engage in the technical management and development responsibilities necessary to effectively and efficiently implement AI initiatives.
Select at least 3 courses from the following:
1.
Managing AI Initiatives
*
While having a relatively long history, Artificial Intelligence (AI) is still actively evolving to where it is now emerging as an essential technology across every industry. The purpose of this course is to prepare IT and non-IT managers for creating effective AI strategies and plans that leverage AI and Cognitive Computing for competitive advantage.
Artificial intelligence (AI) is an academic term that has been seized upon by the media, marketing departments and commentators as shorthand, and to add narrative spice. The now-dominant AI term includes physical and software robots and tools including ‘robotic process automation’, ‘cognitive automation’ and ‘artificial intelligence’. The focus of this course will be on learning the essentials of modern AI, leading industry current and future business application initiatives, and deriving AI deployment strategies, business cases, and plans, as well as considerations for organizational structure, sourcing, and governance processes.
Through an engaging mix of understanding the current and emerging AI and Cognitive Computing technologies, business insights, industry examples, and their impact on the business, the learning journey will bring into sharp focus the reality of contemporary AI and Cognitive Computing and how they can be harnessed to support representative cross industry as well as industry specific (e.g., Finance, Retail, Healthcare) applications.
Focusing on key AI and Cognitive Computing technologies, such as machine learning, natural language processing and deep learning, this course will help candidates understand the implications of these new technologies for business strategies, as well as the economic and society issues they address. The course will also examine how artificial intelligence and cognitive computing will complement and strengthen the workforce rather than just eliminate jobs. Additionally, the course will emphasize how the collective intelligence of people and computers can solve business problems that not long ago were considered impossible.
Upon completion of this course IT and non-IT candidates will be prepared to deliver an organizational AI strategy that addresses specific technology management and organizational aspects for ensuring successful deployment of AI.
2. Building the Data/Analytics Organization *
This course addresses the organizational elements of the Data and Business Analytics (including cognitive computing and robotics process automation) functions by focusing on the management, structural/reporting, and human resource/skills considerations of data and business analytics. Topics such as determining where the group(s) should report, how they are assessed/measured, the necessary skills and how to source them, key data/analytics/cognitive computing processes, data governance, how to lead data-driven innovation in products and services, IT and non-IT roles, and customer and competitor alignment, all driven by the demand to improve the quality and speed of business decisions, minimize the risks/challenges for implementing them, and how to leverage data as a strategic asset. By concentrating on IT’s data, analytics, and cognitive computing responsibilities, in essence this course puts the candidate in the role of the CAO/CDO (Chief Analytics Officer/Chief Data Officer) as they define the vision, strategies, missions, and build the management processes and organization/skills necessary to deploy these data driven initiatives. The course focuses on the important organizational structure in terms of separate or combined organizations, and placement within the overall enterprise and IT organizational structures. This course is geared for managers and consultants engaged in building and growing this organization, including CIOs and non-IT executives to help prepare the enterprise to leverage their investment in Big Data/BA.
3. Analytics, Applications & Techniques *
This course will focus on providing candidates with a well-grounded understanding and appreciation of the contemporary methods, tools and techniques used to make analytics an integral part of managerial decision making. It will concentrate on the approaches for realizing the hidden knowledge in corporate databases and will help participants make near-real time intelligent business and operation decisions. The course will introduce various types of analytics including: reporting/visualization, predictive/data mining, decision-making/prescriptive analytics, pattern recognition, and forecasting. Methodological and practical aspects of knowledge discovery algorithms will also be covered including: data preprocessing, k-nearest neighborhood algorithm, machine learning (e.g. decision trees, artificial neural networks), predictive modeling, cognitive computing, clustering and market segmentation, association rule mining techniques, and time series forecasting. The focus of this course is on understanding the potential of these analytical techniques in various organizational settings.
4. Knowledge & Discovery Approaches *
This course follows the Analytics Applications and Techniques Course, and will focus on the hands-on application of data mining, text mining, cognitive computing, artificial intelligence, and big data products/tools/software in solving real world business and operational problems. A variety of popular knowledge discovery software products (both professional/industrial and free/open source) will be used to demonstrate a wide range of interesting application scenarios. This course will provide participants with an in-depth understanding of the trade-offs that exist in identifying, designing and implementing knowledge discovery projects. It concentrates on building hands-on skills to apply appropriate techniques to discover hidden knowledge in corporate and external databases (both structured and unstructured) to help managers make near-real time intelligent strategic and operational business decisions. The main goal of this course is to provide candidates with not only a well-grounded understanding and appreciation of the methods and methodologies but also help candidates develop hands-on experiences in applying them to real world problems and data sets.
5. Leveraging IT Resources: Information & Resource Management
This course takes a comprehensive information and resource perspective of business strategy by addressing the strategic, tactical, and operational roles and responsibilities across the business for managing blockchain as a strategic business asset.
While the alignment of business and IT is the primary focus, emphasis is placed on the current/emerging issues/opportunities in creating and coordinating the significant initiatives necessary to ensure IT’s contribution to the success of the organization; in essence as IT is shaping global markets and impacting the enterprise, how must IT reshape itself. This is done by examining important considerations such as governance, demonstrating value, IT processes, IT organizational structure, HR & sourcing, managing emerging technologies, the integrated roles of IT, and IT-business strategy. By concentrating on ITs strategic responsibilities, this course puts the candidate in the role of an IT leader as they build a business strategy that is enabled/driven by IT. It lays the groundwork for understanding how IT must evolve to remain relevant in a world where profound changes in business, economics, environment, and technology have become the norm.
6. Managing Emerging AI Technologies
This course focuses on the current and emerging AI related tools, approaches, and related technologies (e.g., cloud, legacy services, data security/privacy, social media/networks, internet of things, mobile applications, cognitive computing, crowd-sourcing, standards), and how they can be integrated and leveraged. While technology focused, it is still focused on management considerations.
It is designed to help candidates understand the difference between the different types of AI initiatives and related technologies. By concentrating on current AI related technologies, in essence this course puts the candidate in the role of the CIO/CTO/CISO/CAO/CDO as they ensure their organization is prepared to effectively and efficiently enable/drive these blockchain initiatives.
7.
Deploying Robotics Process Automation Technologies
(also consider courses from the Business Process Management Certificate)
Robotic process automation (RPA) is a fundamental technology in the reformation of all back office and front office business processes. As organizations leverage RPA, expertise in the technical and management considerations for deploying and supporting these RPA software robots to automate tasks has become essential. The purpose of this course is to prepare IT professionals, including business analysts, business intelligence developers, data or solutions architects, and system integrators, with the current and emerging tools and practices, to ensure successful RPA deployment across the enterprise.
Appreciating how enterprises automate services using a variety of automation technologies is at the core of this courses. The array of available automation products described include scripting tools, software robots, robotic process automation, artificial intelligence, desktop automation, cognitive computing, business process management automation, and machine learning, to name a few. Understanding how these tools worked, the type of data used as input, how they processed data, and the type of results produced are fundamental. Customer would likely appreciate interacting with a robot that looked more like a person...
Recognizing the difference between Robotic Process Automation (RPA) and Cognitive Automation (CA; which people commonly call artificial intelligence/AI) and the impact they can have is essential. The realm of RPA consists of tools that automate tasks that have clearly defined rules to process structured data to produce deterministic outcomes. A ‘software robot’ is configured to process tasks the way humans do, by giving it a logon ID, password, and playbook for executing processes. RPA tools are ideally suited for automating those mindless ‘swivel chair’ chores performed by humans, like taking structured data from spreadsheets and applying some rules to update an ERP system. RPA tools ‘take the robot out of the human’, meaning that the tedious parts of a person’s job could be automated, leaving the human to do more interesting work that requires judgement and social skills. Automation Anywhere, Blue Prism, and UiPath are the top RPA providers by market share.
The realm of cognitive automation (CA) consists of more powerful software suites that automate or augment tasks that do not have clearly defined rules. We do not like to call such software ‘Artificial Intelligence’ because we believe the AI label aggrandizes what these tools do. With CA technologies, inference-based algorithms process data to produce probabilistic outcomes. A variety of tools are in the realm of CA, such as tools that analyze data based on supervised machine learning, unsupervised machine learning, and deep learning algorithms, backed by powerful computing and memory. The input data is often unstructured, such as natural language, either written or spoken. Google’s Machine Learning Kit, IPsoft’s Amelia, IBM’s Watson suite and Expert Systems’ Cogito are examples of CA tools.
Candidates will be primed to create and launch an RPA implementation plan for their organizations.
While having experience with AI tools is recognized as being fundamental, industry expertise is also considered essential in being able to have a successful career in AI. GIIM has courses in the following industries to help prepare candidates with the requisite industry expertise: Finance, Pharmaceutical, Healthcare, Manufacturing, Hospitality, Government, Telecommunications, Petroleum, Retail, Insurance, Transportation, etc.
No one can escape cyber attacks, and AI has added additional complexity. The increasing number and impact of security incidents, and difficulty in finding trusted experts in this new art of war, has driven the demand for security professionals with AI expertise to an all-time high.
As AI has become critical to the global economy, there is ongoing need, in both the private and public sectors, for qualified managers who can perceive and, with counsel, respond to the legal and regulatory environment for AI initiatives.
11. AI-Enhanced Business Communications
While effective communication stands out as a fundamental skill for a successful career, with the advent of AI, the need to understand how to apply technology to enhance all communications has become essential. The purpose of this course is to address how to leverage the dynamic intersection of business communications and AI technology to succeed. Augmented with AI-enabled tools, this course provides insights into enhancing traditional communication methods. Topics include AI-Enhanced Communication, AI-Powered Presentations and reports, Crafting a Resume and LinkedIn Profile, Tailored Messaging, Verbal Strategy Refinement, Electronic Medium Mastery, and Interpersonal Skill Development. This course focuses on improving the skills it takes to communicate effectively in today’s digital environment.
1. Exploratory Data Analysis with Python
Exploratory data analysis enables candidates to perform data health checks and gain initial insights from data, and in this course candidates will gain an understanding of python programming basics and then cover the fundamentals of data management, descriptive statistics, and data visualization using Python. Course Topics:
- Python programming essentials
- Data management in Python
- Descriptive statistics
- Data visualization with Python
2. Data Analytics with Excel and SQL
Excel and SQL are a fundamental part of a data analyst’s toolkit. A strong understanding in these tools also provides a basis for more advanced data analytics with other techniques and technologies. In this course, candidates will gain experience in collecting, processing, analyzing, and communicating with data using Excel and SQL. Course Topics:
- Excel functions
- Data Analysis with Excel
- Data management with SQL
3. Data Visualization with Power BI / Tableau
Data visualization is a powerful way to communicate meaning in data while supporting business decision-making. This course will introduce candidates to the main commercial tools used in data visualization such as Tableau, Excel, and Power BI. It will enable candidates to create a wide range of graphs, charts, and dashboards while using them appropriately in context. Candidates will also gain experience in interpreting data graphically and communicating findings effectively to a business audience. Course Topics:
- Business intelligence tools
- Generate graphs and charts
- Build dashboards
- Interpret and communicate data visually
4. Statistics for Data Analytics
Statistical inference is the process of drawing conclusions from data using statistical/mathematical techniques. This is at the core of data analytics and data science, and a strong understanding of statistics is an essential ingredient in a competent data analyst. In this course candidates will cover the fundamentals of sampling, statistical distribution, hypothesis testing, and variance analysis, while applying Python code to carry out various statistical tests that derive business solutions from their output. Course topics include:
5. Fundamentals of Predictive Modelling
Solutions to many business problems are related to successfully predicting future outcomes. This course introduces candidates to predictive modelling and provides a foundation for more advanced methods/approaches/techniques. Candidates will gain an understanding of the general approach to predictive modelling while building simple linear regression, multiple linear regression and logistic regression models in Python, to apply their insights in a range of business contexts. Course Topics:
- Predictive modelling principles
- Build regression models
- Python for predictive modelling
6. Introduction to Machine Learning
In this course candidates are introduced to fundamental concepts of machine learning, why machine learning is possible, and a range of real-world machine learning applications. Candidates will then cover common machine learning algorithms and use Python libraries to implement these techniques. Course Topics:
- Fundamental machine learning concepts
- Machine learning algorithms
- Business applications
- Machine learning with Python
The Entry Level Python program (described above) or its equivalent is a prerequisite for this Advanced Level Python program.
Advanced topics include:
a. Data Science with Python
b. Deep Learning with Python
c. Data Engineering with Python
d. Machine Learning with Python or R
e. NLP & Text Analytics with Python
f. Network Analysis with Python
ADVANCED
- IBM Cognos Technical Training
AI and machine learning are providing significant contributions to the efficiency and effectiveness of auditing (e.g., security, accounting, finance). This course, for both IT and auditing professionals, focuses on the application of AI to the essential practices employed by auditors to identify irregularities. Leveraging these AI auditing tools is enhancing the forensic analysis procedures used throughout every business and industry. Working with these tools enable auditors to select and analyze the right data to identify abnormalities. This course will prepare candidates on using Computer Aided Audit Tools (CAATs) such as ACL Robotics, Machine Learning, and Python to automate the auditing process. To visualize the results, visualization tools such as Tableau will also be covered.
As organizations accelerate their digital transformation initiatives, they are focusing their investments in leveraging emerging information technologies like Artificial Intelligence (AI) for competitive advantage. It has become essential to understand how to effectively and efficiently manage an organization’s IT resources, to reach these objectives. There are numerous strategic, tactical, and operational choices to be made about managing AI resources and it is essential to ensure that IT and non-IT executives across the organization work in harmony.
Experience has made it clear that organizations need well-conceived organizational structures, skills, processes, and decision rights to ensure that emerging technologies like AI are appropriately leveraged across the organization, especially when considering the impact that AI is having.
This course prepares executives/professionals by providing a comprehensive understanding of the fundamental decisions related to the management of AI. The course will also provide an overview of current and future relevant AI technologies and their potential impact on industry and their associated stakeholders.
The course is designed to be delivered live/synchronously (face-to-face or online) with a total of twenty (20) contact hours. While the schedule is flexible, it is usually delivered in approximately ten (10) 2-hour modules/lectures/sessions.
The AI topics include:
- Deriving IT-business AI strategies
- Considerations for types of
- organizational structure
- sourcing
- governance (i.e., decision-making and decision rights)
- roles/responsibilities
- processes
- Leverage emerging AI and related technologies
- The business value of AI
- The definition, concepts, and contexts of AI
- Enhancing business-IT alignment
