AI Readiness: How and Where to Start

Embarking on an artificial intelligence (AI) journey can be like setting sail in uncharted waters for many businesses. The term 'AI Readiness' has become a buzzword in the industry, yet its meaning often eludes even the most enthusiastic adopters. In essence, AI Readiness refers to the preparedness of a company to integrate AI technology in a way that is both effective and adds tangible value to its operations. Far from being a simple tech upgrade, AI Readiness encompasses a comprehensive evaluation, ranging from data infrastructure and technical expertise to cultural adaptation. For organizations aiming to remain competitive in a rapidly advancing digital landscape, discerning their AI Readiness is not only strategic but also imperative for sustainable growth. Understanding where your organization stands is the first crucial step in harnessing the power of AI to its fullest potential.

Why is AI Readiness critical for your business? The onset of AI heralds a massive transformation in how businesses operate, strategize, and innovate. Embarking on the AI journey unprepared can result in costly missteps, whereas a clear readiness assessment can pave the way for a successful and purposeful AI integration.

Assessing Your AI Readiness

To determine your starting line in the AI race, you must first take stock of your current position. Assessing your AI readiness involves a multidimensional approach, examining the technological, data, cultural, and strategic facets of your organization. It's about asking the right questions and objectively evaluating the answers.

Understanding the Basics of AI in Business

AI has transitioned from science fiction to business reality, and it is revolutionizing the way decisions are made, efficiencies are improved, and customer experiences are crafted. However, before delving into its intricacies, it's crucial to demystify AI for the corporate context. AI encompasses various technologies such as machine learning, natural language processing, robotics, and more — each with its application and prerequisites for success.

Key Components of AI Readiness

• Data Infrastructure: At the heart of AI is data. A robust data infrastructure that can capture, process, and analyze data is essential. This includes having the right tools for data storage, mining, and analysis that are scalable and secure.
• Technical Expertise: Possessing an in-house team with the technical skills to develop, deploy, and manage AI solutions is transformative. If such resources are not available, identifying the right AI partners or vendors becomes equally significant.
• Organizational Culture: AI adoption is not just a technological change but a cultural one. Cultivating a culture that embraces innovation, continuous learning, and experimentation is necessary for AI to thrive within an organization.

The AI Readiness Scorecard

An effective way to formalize the assessment process is through an AI Readiness Scorecard. Such a scorecard evaluates multiple indicators of readiness across various dimensions of the business, providing a quantifiable measure of where an organization stands. Central to this is the alignment with business goals and objectives, technical infrastructure, data governance policies, and workforce capabilities.

Assessing your company's AI readiness is a strategic exercise that preempts the integration of AI technologies into your core operations. To truly understand where you stand, a comprehensive analysis that examines your technical infrastructure, data maturity, workforce skill sets, and organizational culture is necessary.

An AI Readiness Scorecard can help you analyze these dimensions and provide a benchmark against industry standards. Such an evaluation often brings to light the strengths to capitalize on and the gaps that need closing. For instance, you may discover that while your workforce is eager and adaptable, your data management systems need a significant overhaul to meet the demands of AI applications.

The assessment phase should not be rushed. It is essential to take the time to ensure that all aspects of AI readiness are thoroughly evaluated. The insights generated from this phase will affect how you plan your AI roadmap, what resources you allocate, and the timelines you set for AI project rollouts.

The AI Readiness Scorecard should include:

• Technology Assessment
• Data Quality and Accessibility Evaluation
• Talent and Expertise Analysis
• Cultural Readiness Check
• Value Realization Potential

Each of these components should be scored based on current capabilities and benchmarked against best practices in the industry.

In the context of technological assessment, it's not just about having the latest tools, but also about the integration and interoperability of systems that can support AI processes. Let's delve deeper into the technical prerequisites for AI integration:

• Data Management: Evaluate your data lifecycle management – from acquisition, storage, processing, to analysis. A strong foundation here provides the fuel for AI algorithms.
• Compute Power: Assess whether your current IT infrastructure can handle the high-performance computing requirements of AI models, including whether to use on-premises, cloud, or hybrid solutions.
• Security and Privacy: With AI hinging on data, ensuring the security and privacy compliance of your systems is non-negotiable. This extends to understanding regulations like GDPR or Canada's PIPEDA.

Next, in terms of data quality and accessibility, the readiness scorecard should highlight the state of your datasets. High-quality, well-curated, and accessible data sets are prerequisites for any successful AI project. Issues like data silos, inconsistent data formats, and poor data governance can impede AI efficiency and accuracy.

As for talent and expertise, a gap analysis can help you identify the need for upskilling existing staff or recruiting new talent proficient in AI and machine learning. Consider the balance of technical skills and the ability to translate AI outcomes into actionable business insights.

The cultural readiness check will gauge how well-prepared your organization is to embrace the changes AI will bring. It involves leadership buy-in, a willingness to experiment and learn from failures, and the flexibility to adapt business processes.

Focusing on value realization will highlight the potential impact of AI on your business processes and customer experience. It's an exercise in understanding not just if you can adopt AI, but if you should, based on the expected return on investment.

Setting Goals for AI Adoption

As much as AI presents numerous possibilities, it’s crucial to ground your adoption strategy in clear, achievable objectives. To harness AI's potential, one must not only focus on the technological appeal but prioritize alignment with business goals to drive value. This stage is about defining what AI success looks like for your organization.

Short-Term vs. Long-Term AI Goals

Setting both short-term and long-term goals is critical for maintaining momentum and vision. In the short term, focus on enabling quick wins that demonstrate the value of AI with measurable outcomes, such as process optimization or customer service enhancements. For the long-term, think about transformational change: How can AI contribute to game-changing innovation in your products or services?

Goals should be SMART - Specific, Measurable, Achievable, Relevant, and Time-bound. This ensures that your AI initiatives remain focused and capable of evaluation. For instance, having a goal to "reduce customer response times by 30% within six months using AI-powered chatbots" is a SMART goal that addresses a specific operational efficiency.

Aligning AI Goals with Business Objectives

AI adoption should never occur in a vacuum; it needs to be a strategic lever for achieving broader business aims. Whether it's improving customer satisfaction, cutting operational costs, or innovating product offerings, every AI initiative should map back to your enterprise's strategic objectives.

Aligning AI goals with business objectives requires communication across departments and leadership levels. This holistic approach ensures enterprise-wide buy-in and synergizes efforts across different verticals, ensuring that AI becomes a shared tool for success rather than a siloed project.

Key takeaway: Integrating AI without a clear connection to core business objectives is akin to steering a ship without a compass—directionless and potentially disastrous.

Defining Success Metrics for AI Initiatives

Setting goals without the means to measure success can lead to misguided efforts and misallocated resources. It’s imperative that for each AI goal, corresponding performance indicators are defined. This could range from quantitative metrics, such as improvement in production yield or uptick in sales, to qualitative ones, such as customer satisfaction scores.

It is equally important to establish a timeline for evaluating these metrics and a framework for regular review. Success in AI is not just about hitting a target but ensuring that the target remains relevant and contributes to continuous improvements.

Remember: The beauty of AI lies in its ability to learn and adapt. Your metrics should incorporate the capacity for evolution, ensuring your AI initiatives grow in sophistication and contribution over time.

This section equips businesses with actionable advice on setting goals for AI adoption that are directly tied to real-world outcomes and strategic objectives. A scorecard or visual dashboard can be beneficial tools for monitoring progress and ensuring your AI goals align with your business plans.

Roadblocks and How to Overcome Them

Any transformative journey comes with its set of challenges, and the path to AI integration is no different. Anticipating roadblocks and formulating strategies to circumvent them will safeguard your AI initiatives and keep your goals within reach.

Identifying Common Challenges in AI Adoption

The journey to AI-readiness often reveals constraints that can hinder progress. These challenges typically include insufficient data quality, a lack of in-house AI talent, budgetary restrictions, and a resistance to change within the organization. A proactive approach—where potential issues are identified early and plans are devised to address them—can hugely mitigate the risks associated with these roadblocks.

Strategies for Overcoming Technical Obstacles

Tackling the technical obstacles requires a strategic approach:

• Infrastructure Audit: Conduct an exhaustive review of current IT assets to understand your capacity to support AI initiatives.
• Incremental Upgrades: Rather than a complete overhaul, consider incremental infrastructure enhancements to support AI functionalities.
• Data Hygiene: Implement robust data management practices to ensure high-quality, AI-ready data.
• Cloud Solutions: Leverage cloud computing resources to access scalable AI processing power and storage solutions.
• Partnerships: Collaborate with technology partners to fill in expertise and infrastructure gaps.

Change Management and AI Adoption

Managing the human side of AI adoption can often be an organization's biggest hurdle. Embracing change management principles is critical to ensure that your workforce is on board with the AI transformation:

• Stakeholder Engagement: Engage with key stakeholders from the outset to understand their concerns and get their buy-in.
• Communication Plan: A transparent communication plan should address the benefits of AI, the impact on jobs, and the support available for upskilling.
• Training and Development: Invest in training programs to build AI competency within your workforce.
• Celebrate Milestones: Recognize and celebrate when teams achieve AI-related goals to drive positive reinforcement.

An effective change management strategy acknowledges the inevitable human resistance to new technologies and provides a clear path for incorporating AI into daily work life, ensuring that the transition is as smooth as possible for all employees.

Crafting Your AI Strategy

With a clear understanding of your AI readiness and goals, now comes the strategic planning phase. Crafting an AI strategy is essential because it serves as the blueprint that guides all future AI-related activities. The strategy must be adaptable, measurable, and closely aligned with business objectives.

Importance of a Tailored AI Strategy

A one-size-fits-all approach doesn't work in the realm of AI. Each business has its unique challenges, opportunities, strengths, and weaknesses. A tailored AI strategy takes into consideration these factors and provides a customized plan that leverages AI technologies most effectively for the distinct circumstances of your company.

Building a Cross-Functional AI Team

A successful AI strategy relies on the collective efforts of a diverse team of individuals who bring different skills and perspectives to the table:

• Identify key roles such as data scientists, AI engineers, business analysts, and project managers.
• Encourage collaboration between the AI team and other departments to ensure AI initiatives align with broader company goals.
• Promote a culture of continuous learning within the team, staying up-to-date with the latest AI advancements and methodologies.

Role	Description	Key Responsibilities
AI Project Manager	Oversees the AI project from conception to deployment, ensuring it meets business goals and objectives.	- Project planning and management - Coordination among team members - Stakeholder communication
Data Scientist	Analyzes and interprets complex data to help companies make decisions based on data insights.	- Data mining and analysis - Building predictive models - Data visualization
Machine Learning Engineer	Designs, builds, and deploys machine learning models to solve specific problems.	- Developing machine learning algorithms - Model training and evaluation - Integration of ML models into production systems
AI Researcher	Focuses on developing new AI techniques and improving existing algorithms.	- Conducting AI research - Publishing findings - Keeping up with AI advancements
Data Engineer	Prepares the “big data” infrastructure for analysis and optimization by the data scientists.	- Data collection and storage - Data preprocessing and cleaning - Managing data pipelines
AI/ML Operations (MLOps) Engineer	Focuses on the automation and production of machine learning lifecycles.	- Deployment of ML models - Model monitoring and maintenance - Automation of machine learning workflows
Data Analyst	Processes and performs statistical analysis of large datasets.	- Data cleaning and preparation - Exploratory data analysis - Reporting and data visualization
Software Developer	Develops software to support AI algorithms or to use AI models in applications.	- Developing APIs for model deployment - Integrating AI models into existing software - Building AI-powered applications
UX/UI Designer	Designs user interfaces and experiences for AI-driven applications.	- Designing intuitive and user-friendly interfaces - Conducting user research and testing - Collaborating with developers to implement designs
Ethical AI Specialist	Ensures AI systems are developed and deployed in a manner that adheres to ethical standards.	- Conducting ethical reviews of AI projects - Developing guidelines for ethical AI use - Advising on bias mitigation strategies
Legal and Compliance Officer	Oversees legal aspects related to AI development and deployment, including data use and privacy.	- Ensuring compliance with data protection laws - Drafting and reviewing contracts - Advising on intellectual property rights
Business Analyst	Bridges the gap between IT and the business using data analytics to assess processes and determine requirements.	- Identifying business needs - Requirements analysis and management - Communicating between stakeholders and the AI team

Collaborating with AI Vendors and Partners

For many companies, especially those with limited in-house expertise, collaborating with external AI vendors and partners can be beneficial. These partnerships bring specialized skills and experiences that can accelerate your AI initiatives and help avoid common pitfalls experienced by others.

When selecting a vendor or partner, consider factors like:

• Industry expertise
• Technology stack compatibility
• A proven track record with similar business problems
• The vendor’s ability to scale with your business

Due diligence in this selection process is fundamental, as the right partner can significantly enhance your AI capabilities, while a poor choice can set you back.

Piloting AI Projects

Before a full-scale rollout, piloting AI projects is a critical strategy for testing the waters. Pilot projects allow you to:

• Test the feasibility of AI solutions in a controlled environment.
• Gather insights and data that will shape the full-scale implementation.
• Adjust your AI strategy based on real-world feedback and results.

Pilots serve as an excellent litmus test for your AI readiness and provide a pragmatic approach to refining your broader AI strategy. They offer a glimpse into how AI can transform various facets of your business, from operations to customer engagement, and set the stage for more ambitious AI endeavors.

Preparing Your Data Ecosystem

The fuel that powers AI algorithms is data. For AI systems to be effective, they require a data ecosystem that is well-designed, managed, and constantly refined. This stage is about setting up and nurturing the data environment that your AI systems will thrive on.

Data Quality and Governance

High-quality data is a non-negotiable prerequisite for any AI system. Data governance frameworks ensure that data across the organization is accurate, consistent, and securely managed:

• Establish standards and protocols for data collection, storage, and distribution.
• Regularly audit data to identify and address quality issues.
• Implement data governance tools and practices that heighten data security and privacy.

Effectively governed data not only feeds AI algorithms but also builds trust in the outputs they generate. This trust is essential for decision-makers who rely on AI-driven insights.

Data Analytics and Interpretation Skills

Raw data holds value, but the ability to analyze and interpret this data is what unlocks its true potential. Building an in-house talent pool with proficiency in data analytics and interpretation is beneficial:

• Invest in training existing staff to upskill their data analytics capabilities.
• Consider hiring data analysts and scientists who can decipher complex data sets and glean actionable insights.
• Encourage a culture of data-driven decision-making, demystifying data analytics tools, and methodologies for non-technical staff.

Honing these skills across the organization ensures that AI's contributions can be understood and acted upon effectively by all stakeholders.

Upgrading Your IT Infrastructure for AI

Legacy systems often lack the agility to handle the demands of AI applications. Therefore, upgrading your IT infrastructure is key to facilitating AI integration:

• Assess whether your existing IT environment can support the scale and complexity of AI workloads.
• Explore cloud computing options which can provide the necessary scalability and computing power for AI.
• Ensure your network can handle increased data flows without bottlenecks that could impede AI processes.

An upgraded IT infrastructure not only supports current AI requirements but also offers the flexibility to adopt future technological advancements.

The preparation of your data ecosystem is an intricate task—yet, it is undeniably rewarding. The right data environment acts as the backbone to any successful AI endeavor, redefining what's possible with technology and innovating for the future.

IT Infrastructure for A.I. Checklist:

The following checklist provides an exhaustive overview of the essential aspects you should consider when upgrading your IT infrastructure for AI applications:

Hardware Infrastructure

• Compute Power
  • GPUs for training complex models.
  • CPUs with high thread count for data preprocessing and model inference.
  • TPUs or other specialized hardware for specific AI tasks, if applicable.
• Storage Solutions
  • High-speed SSDs for fast data access and processing.
  • Scalable storage solutions (e.g., NAS, SAN) for large datasets.
  • Cloud storage options for flexibility and scalability.
• Networking
  • High-bandwidth networking for fast data transfer between nodes in a cluster.
  • Low-latency network infrastructure for real-time applications.
  • Secure network configuration to protect data in transit.

Software Infrastructure

• Operating Systems
  • Choose an OS with strong support for AI tools and libraries (e.g., Linux distributions).
• Data Management
  • Database systems optimized for the types of data you work with (relational, NoSQL, time-series, etc.).
  • Data integration and ETL tools to consolidate and prepare data from various sources.
• AI and Machine Learning Platforms
  • Machine Learning frameworks and libraries (e.g., TensorFlow, PyTorch).
  • AI development platforms for model training, testing, and deployment.
• Development Tools
  • Integrated Development Environments (IDEs) supporting AI languages (e.g., Python, R).
  • Version control systems (e.g., Git) for collaborative development.
• MLOps Tools
  • Continuous Integration/Continuous Deployment (CI/CD) tools for automated testing and deployment.
  • Model monitoring and management tools for performance tracking and version control.
  • Data versioning tools to manage and track datasets.

Security and Compliance

• Data Security
  • Encryption tools for data at rest and in transit.
  • Access control mechanisms to ensure only authorized personnel can access sensitive data.
  • Data anonymization tools for working with sensitive information.
• Compliance
  • Tools and processes for compliance with data protection regulations (e.g., GDPR, HIPAA).
  • Regular security audits and vulnerability assessments.

Scalability and Flexibility

• Cloud Services
  • Integration with cloud services for scalable compute and storage resources.
  • Cloud-based AI and ML services for additional capabilities.
• Containerization and Virtualization
  • Containerization tools (e.g., Docker) for deploying and managing applications.
  • Virtualization solutions for efficient resource utilization.

Performance Monitoring and Optimization

• Monitoring Tools
  • Tools for monitoring system performance, resource usage, and application health.
  • AI model performance monitoring to track accuracy, inference times, etc.
• Optimization
  • Load balancing solutions for distributing workloads efficiently.
  • Auto-scaling systems to dynamically adjust resources based on demand.

Upskilling and Training Your Workforce

The advent of AI technology has ushered in a new era of workplace evolution, demanding a shift in skills and competencies. To leverage AI effectively, businesses must invest in upskilling and training their workforce. This not only ensures that employees are prepared for the changes brought about by AI but also fosters an environment of innovation and continuous improvement.

The Need for AI Literacy in the Workplace

AI literacy is fast becoming a requisite across all levels of an organization. Understanding the basics of AI, its capabilities, limitations, and ethical considerations is vital:

• Conduct workshops and seminars to introduce AI concepts and their potential impact on various roles.
• Include AI literacy as a component of the onboarding process for new employees.
• Promote a culture that values and encourages curiosity and learning about emerging technologies.

Creating a foundation of AI literacy helps demystify the technology and integrates it seamlessly into the company culture.

Developing In-House AI Talent

Growing your in-house AI talent can lead to a more agile and responsive AI strategy:

• Identify employees with the potential to be upskilled into AI-focused roles and support their development.
• Encourage continuous learning by providing access to courses, certifications, and conferences on AI and related subjects.
• Create opportunities within the organization for employees to apply their AI knowledge practically.

In-house talent development not only builds expertise but also promotes retention by allowing employees to engage with cutting-edge work and professional growth.

Leveraging Online Learning Platforms

Online learning platforms have revolutionized the way professionals upgrade their skills. They offer flexibility and a wide range of courses tailored to different proficiency levels in AI:

• Curate a list of recommended online courses from platforms such as Coursera, Udemy, or edX that employees can take advantage of.
• Consider partnerships with these platforms to create a customized curriculum that aligns with your company's AI goals.
• Encourage a knowledge-sharing environment where employees can discuss and share insights from their learning experiences.

By harnessing such resources, a company can empower its workforce with the skills needed to innovate and stay competitive in an AI-driven market.

Investing in workforce upskilling and training not only prepares your organization for AI integration but also equips your employees with future-proof skills. This commitment to professional development is an investment in your company's most valuable asset—its people.

This investment ensures your team is not only prepared to manage the AI systems that are being integrated into your business operations but also poised to innovate and drive your company forward. An educated workforce is critical to maximizing the benefits of AI and ensuring a competitive edge in the marketplace.

Moreover, it is important to understand that training should not be a one-off event. AI and related technologies are continuously evolving; as such, ongoing learning and development activities must become part of the organizational DNA, ensuring your team can keep pace with technological advancements.

Encouraging a Continuous Learning Culture

Cultivating a culture that recognizes the value of ongoing education and skill acquisition is quintessential in the AI era. Consider implementing the following:

• Set up internal learning campaigns that reward progress and certification completion.
• Host regular AI-focused innovation labs or hackathons to foster practical application of AI skills.
• Provide time and resources for employees to explore and experiment with new AI tools and methodologies.

Such initiatives empower employees to take ownership of their learning journey and contribute more significantly to AI-related projects.

Monitoring Skills Development and Utilization

As with any strategic business initiative, monitoring the impact of your training and upskilling efforts is crucial. Employ metrics to assess:

• The uptake of training opportunities by employees.
• The application of newly acquired skills to AI projects.
• The influence on productivity and innovation within teams.

Facilitating skills tracking allows for a clear view of your workforce's capabilities, helping identify areas where further education is necessary. This dynamic approach to talent management not only prepares your team for the challenges of AI but also propels them towards seizing new opportunities.

Ultimately, the goal of upskilling and training is not just about preparing for the adoption of AI. It's also about envisioning the future of your company with a workforce that is adaptable, technologically savvy, and primed to extract value from AI applications for years to come.

As our discussion on upskilling and training your workforce concludes, we emphasize the significance of continuous investment in employee development. The landscape of AI readiness constantly evolves, and so must your team's capabilities. Your organization's innovative edge will be sharpened not just by the technology it adopts but also by the people who wield it.

Monitoring and Iterating Your AI Implementation

Launching AI initiatives is just the beginning. The real value is unlocked when these systems are monitored, managed, and constantly refined. An iterative approach to AI implementation allows you to fine-tune your strategy and ensure that your AI solutions are delivering optimum value over time.

Key Performance Indicators for AI Systems

Key Performance Indicators (KPIs) are vital in monitoring the effectiveness of AI systems. When developing KPIs, ensure they are closely tied to both your AI goals and broader business objectives. Incorporate a mix of performance metrics, such as:

• Accuracy and quality of insights generated by AI.
• Cost savings realized through AI automation.
• Revenue increases attributable to AI-enhanced products or services.
• Customer satisfaction and engagement metrics.
• Efficiency gains in processes powered by AI.

These KPIs should be regularly reviewed to ensure they remain relevant and reflective of your AI initiative's current state.

Feedback Loops for Continuous Improvement

Feedback is the lifeblood of any AI system. Establishing feedback loops through which AI systems can be assessed and iteratively improved is crucial. This includes collecting input from end-users, analyzing system performance data, and consulting with stakeholders at every stage of the AI lifecycle.

Feedback loops help in identifying issues early, and adapting AI solutions to changing conditions, and aligning AI outcomes with user needs and expectations. This continuous improvement cycle not only enhances AI operations but also supports the ongoing evolution of your AI strategy.

All Together: Conclusion

AI readiness is not a destination but a starting line that leads to an ongoing marathon of innovation and adaptation. As you've explored the contours of AI readiness, from assessing your current state to rigorously setting goals and tackling inevitable roadblocks, your organization is now equipped with a foundational playbook to embrace AI as a transformative tool in your business arsenal. You've crafted a tailored AI strategy, prepared your data ecosystem, and invested in upskilling your workforce.

Remember, the AI landscape is ever-evolving. Success lies in your agility to adapt, willingness to iterate, and commitment to continuous learning. As you monitor and refine your AI initiatives, draw upon the insights and metrics that define your achievements and lead your industry in an AI-augmented future.

The journey of integrating AI into your business operations is a complex yet rewarding endeavor. With each step, from pilot projects to full-scale implementations, the opportunities for growth and improvement are boundless. Your readiness today paves the way for the breakthroughs of tomorrow, fortifying your business against the challenges of an increasingly digital and data-driven world.

As you stand at your AI readiness starting line, know that the race ahead is not a sprint but a marathon—one where finesse, strategy, and endurance will see you through to a future of unlimited potential.

FAQs

• How do I know if my business data is ready for AI?
  To ascertain your business data's readiness for AI, conduct an assessment focusing on data quality, accessibility, and governance. Ensure your datasets are clean, structured, and adequately protected.
• What are the first roles I should hire for building my AI team?
  Consider starting with roles such as data scientists, AI researchers, and engineers who can lay the groundwork for AI development. Also, look for business analysts who can bridge the gap between technical capabilities and business needs.
• Can small businesses also benefit from AI, and how?
  Yes, small businesses can leverage AI for a variety of applications including customer service automation, market analysis, and operational enhancements. AI can help small businesses compete more effectively by driving efficiencies and personalizing customer experiences.
• How often should our AI strategy be updated or revised?
  Your AI strategy should be reviewed regularly, at least annually, or whenever significant shifts occur in your business environment or AI technology that could impact your strategic objectives.
• Where can I find more in-depth training for my team in AI?
  In-depth AI training can be found through specialized online courses, industry workshops, conferences, and higher education programs that offer advanced studies in AI and machine learning.