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.
