​AI Tech Circle​

AI Leadership Spotlight: The Role of the Chief AI Officer and first 90 days

Stay Ahead in AI with Weekly AI Roundup; read and listen on AITechCircle: June 01, 2024

Welcome to the weekly AI Newsletter, where I provide actionable ideas and tips to assist you in your job and business.

Before we start, share this week's updates with a friend or a colleague:

Today at a Glance:

• The Chief AI Officer’s 90-Day Plan
• AI Weekly news and updates
• Generative AI Usecase: Enhancing Employee Safety with Personalized OHS Training Using Generative AI
• Favorite Tip Of The Week & Potential of AI
• Things to Know: Introduction to AI Safety, Ethics, and Society.
• Open Tech Talk Podcast, the latest episode on Building a Successful AI SaaS Product
• AI/ML/GenAI courses to attend
• AI Events & Tech Tools to try out
• Datasets for your next project

I am working with several of my university friends, customers, and colleagues to shepherd AI efforts, primarily Generative AI, which will give me some first-hand experience with this technology and how it is being pursued.

Last week, I had a very interesting meeting with one of the newly appointed Chief AI Officers (CAIO) in Dubai. We discussed the best ways to introduce AI in the organization and where to start.

This discussion sparked the idea for this article. If someone is entrusted with this role or responsibility to oversee the enterprise AI initiative, how should he/she be taking it up? I have just realized that if I need to start this, what could my actions be?

I need to follow a playbook or introduce a playbook in the organization. This playbook can play an integral part in bringing AI/Gen AI to the organization, which is aligned with the business objectives and gives benefits.

Let’s build this playbook publicly so we can all use and benefit from it. Please feel free to share your thoughts and ideas to enrich it. First of all, let's have some background on the role of CAIO.

As the focus is on AI and to capitalize on and stay ahead of this curve, Governments and Organizations are not leaving any stone unturned to target this. Organizational leaders are embarking on a journey with dedicated efforts to get realistic benefits and adopt them in the public and private sectors.

The Dubai Government has announced that every public sector organization should have a Chief AI Officer (CAIO) designated to lead the AI initiatives. The US Government has also announced that chief AI officers (CAIOs) will be assigned to all agencies. Other statistics from LinkedIn show that the number of CAIOs has tripled over the last 5 years.

Chief AI officer’s responsibilities commonly will target these areas:

Now, we can consider a 90-day or 100-day plan for this role as a famous approach recommended by various business coaches. The concept of a 90-day plan for any new role was popularized by Michael D. Watkins in his book “The First 90 Days: Proven Strategies for Getting Up to Speed Faster and Smarter.” Watkins is an expert in leadership transitions and introduced this framework to help leaders and professionals effectively navigate the critical first three months in a new position.

Typically, this follows 3 phases,

• Day 1-30: Learning and Assessment
• Day 31-60: Strategic Planning and Relationship Building
• Day 61-90: Execution and Adjustment

Now, what I did, I took this framework and thought of myself in this role and how, as a CAIO, what could be my first 90 days :)

And here it looks like...

To support your journey, last week, we had a detailed write-up on how to develop an AI Strategy for an organization or what the components of the AI strategy are. You can download the complete slide and read about it from here.

It's exciting to continue this ride and have different phases gathered to introduce AI/Gen AI in a sample organization in the healthcare industry.

You can download the slide deck at the AI Tech Circle site.

Share your feedback and comments, and let's build and learn together. If you have been to the role of chief AI officer, let's connect and improve this framework for the wider AI community.

Weekly News & Updates...

Last week's AI breakthroughs marked another leap forward in the tech revolution.

1. Text to Sound Effects from ElevenLabs: This new AI Audio model generates sound effects, short instrumental tracks, soundscapes, and various character voices, all from a text prompt. Link to try out
2. Huawei unveils Arabic LLM. It is a 100 billion parameter model based on Huawei’s self-developed Pangu and trained with local data to cover the local culture, history, and knowledge customs of the Arab world. Link​
3. Elon Musk’s xAI raises $6 billion from Sequoia, Andreessen, and Saudi royals as it hails ‘significant strides’ in AI research. Link​

The Cloud: the backbone of the AI revolution

• Oracle Plans to Open Two Public Cloud Regions in Morocco link​
• IT trends show customers need computing power to take advantage of AI link​

Gen AI Use Case of the Week:

Generative AI use cases in the HealthCare industry:

Simplifying Claims Submission - Medical Coding with LLMs Using Generative AI.

Business Challenges:

1. Complexity of Medical Codes: Medical coding involves complex and numerous codes, making it difficult for human coders to be accurate and efficient.
2. High Error Rates: Manual coding errors can lead to claim rejections, payment delays, and potential compliance issues.
3. Time-Consuming Process: Coding and submitting claims manually is time-consuming and resource-intensive.
4. Regulatory Compliance: Ensuring adherence to constantly changing medical coding standards and regulations.

AI Solution Description:

Using LLMs for medical coding can simplify the claims submission process by automating the extraction and classification of medical codes from clinical documents. Here’s how it works:

1. Data Ingestion: The LLM ingests clinical notes, patient records, and other relevant documents.
2. Natural Language Processing: The LLM processes the text to understand the context and extract relevant medical terms and codes.
3. Automated Coding: The model then maps these terms to the appropriate medical codes (e.g., ICD-10, CPT) and generates the necessary coding for claims submission
4. Claim Submission: The coded data is automatically populated into the claims submission system and is ready for review and submission to insurers.

Expected Impact/Business Outcome:

1. Revenue: Faster claims processing leads to quicker reimbursements and improved cash flow. Accurate coding reduces claim rejections and the associated costs of re-submission
2. User Experience: High coding accuracy reduces the burden on human coders and improves job satisfaction. Quicker claims processing enhances the experience for patients and healthcare providers.
3. Operations: Automation reduces staff workload, allowing them to focus on more critical tasks. The solution can quickly scale to handle increasing claims volumes without additional human resources.
4. Process: Ensures consistent and standardized coding practices across the organization. Keeps up with the latest coding standards and regulations, reducing compliance risks
5. Cost: Reduces labor costs associated with manual coding and re-submission of rejected claims.

Required Data Sources:

1. Clinical Documents: Patient records, clinical notes, and treatment summaries.
2. Coding Standards: Access the latest ICD-10, CPT, and medical coding databases.
3. Historical Claims Data: Previous claims data will be used to train and fine-tune the LLM for better accuracy.
4. HIS/MIS Systems

Strategic Fit and Impact Rating:

• Strategic Fit:

High Implementing LLMs for medical coding aligns with the strategic goals of improving operational efficiency, reducing costs, and enhancing revenue cycle management.

• Impact Rating: High

The solution significantly impacts revenue, user experience, operations, and compliance, making it a precious investment for healthcare organizations.

Favorite Tip Of The Week:

Here's my favorite resource of the week.

• What We Learned from a Year of Building with LLMs (Part I). OReilly has published this article, which is very informative and detailed to help you understand the best practices from the year of work. Link​

Potential of AI

• Generative AI Agents Developer Contest: Top Tips for Getting Started link​

Things to Know

'Introduction to AI Safety, Ethics, and Society' book Y course is developed by Dan Hendrycks, Director of the Center for AI Safety. It introduces AI safety and ethics to interested students, practitioners, and others.

• Technical challenges in building safe AI, including opaqueness, proxy gaming, and adversarial attacks, and their impact on managing AI risks.
• Societal-scale risks from advanced AI include malicious use, accidents, rogue AI, and the role of AI racing dynamics and organizational risks.
• Safety of sociotechnical systems, the relevance of safety engineering and complex systems theory, and managing tail events and black swans.
• Collective action problems and challenges in building cooperative AI systems.
• AI governance, including safety standards, international treaties, and trade-offs between centralized and decentralized access to advanced AI.

The Opportunity...

Podcast:

• This week's Open Tech Talks episode 135 is "Building a Successful AI SaaS Product with Denzil Eden." She is the founder of Smarty ai

Apple | Spotify | Google Podcast | Youtube

Courses to attend:

• ​Building Agentic RAG with LlamaIndex: In this course, created in collaboration between Deep Learning AI and LlamaIndex and taught by co-founder and CEO Jerry Liu, you will learn to build agents that can intelligently navigate, summarize, and compare information across multiple research papers from arXiv. Additionally, you’ll gain skills in debugging these agents to ensure effective guidance of their actions.

Events:

• ​The AI Retreat 2024, June 13, Dubai, UAE
• ​GITEX GLOBAL, Oct 14-18, 2024, Dubai, UAE

Tech and Tools...

• ​LeRobot: It provides models, datasets, and tools for real-world robotics in PyTorch.
• ​MLflow: It is to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Data Sets...

• ​Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs.

Other Technology News

Want to stay on the cutting edge?

Here's what else is happening in Information Technology you should know about:

• Apple Plans AI-Based Siri Overhaul to Control Individual App Functions as reported by Bloomberg​

AI First Community to Learn & Share...

Have a question or need some assistance with your AI project, or maybe you want to be part of the thriving community to learn AI together,

​Click here to join the AI Tech Circle - It's your Community on Discord​

Download 100+ Gen AI use cases:

That's it!

As always, thanks for reading.

Hit reply and let me know what you found most helpful this week - I'd love to hear from you!

Until next week,

Kashif Manzoor