The job market is undergoing a seismic shift. As we move deeper into 2026, artificial intelligence isn’t just transforming technology companies—it’s reshaping every industry from healthcare to finance, retail to manufacturing. The U.S. faces a shortage of more than 700,000 AI professionals, and roles like AI and machine learning engineers are predicted to nearly triple in the coming years.
But here’s the reality: AI won’t simply replace jobs—it will transform them. Roughly 26% of jobs posted over the past year are poised to “radically transform” due to generative AI, while 54% are likely to be “moderately” transformed. The question isn’t whether AI will affect your career, but how prepared you are to thrive in this new landscape.
This comprehensive guide explores the most critical AI skills for 2026, why they matter, and how you can develop them to future-proof your career.
Understanding the AI Job Market Landscape
The Current State of AI Employment
The demand for AI skills has been steadily rising since 2010, with this trend accelerating significantly in the last year. Over the first eight months of 2024, the share of online job postings for AI jobs reached 1.62 percent, nearly double what it was just a few years ago.
The impact varies by sector and role:
White-Collar Transformation: Jobs in higher-paying fields where college education and analytical skills are advantageous are most exposed to AI, including budget analysts, data entry keyers, tax preparers, technical writers, and web developers.
Widespread Adoption: By 2024, nearly a quarter of all occupations had some minimal demand for AI skills, with the share of industries and commuting zones showing AI demand reaching 29% and 13% respectively.
Premium Compensation: Workers with AI skills command significantly higher wages across every industry analyzed, with wage premiums averaging substantially higher for those with AI capabilities.
The Reality Check
While headlines scream about job displacement, the actual picture is more nuanced. Overall metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.
However, this doesn’t mean complacency is an option. The transformation is happening—it’s just unfolding over years rather than months, giving you time to prepare and adapt.
The Top AI Skills for 2026
1. Machine Learning & Deep Learning Mastery
Why It Matters
Machine learning (ML) and deep learning (DL) form the backbone of modern AI systems. From personalized recommendations to autonomous vehicles, these technologies power the innovations reshaping our world.
Key Competencies
- Supervised, Unsupervised, and Reinforcement Learning: Understanding different learning paradigms and when to apply each
- Neural Networks: Architectures optimized for speed and efficiency
- Frameworks: Proficiency in TensorFlow, PyTorch, and emerging lightweight alternatives
- Model Deployment: Real-world implementation of ML/DL models at scale
- Performance Optimization: Fine-tuning models for specific business applications
Real-World Applications
Retail giants use ML to hyper-personalize shopping experiences, while healthcare companies leverage DL to detect diseases earlier than human doctors. Financial institutions employ these technologies for fraud detection and risk management.
Who Needs This
- Data Scientists
- Machine Learning Engineers
- AI Researchers
- Applied Scientists
- Quantitative Analysts
2. Natural Language Processing (NLP) & Generative AI
Why It Matters
With generative AI becoming mainstream, NLP has evolved from a niche specialization to a critical business capability. Companies don’t just want AI that “reads”—they want AI that understands, generates, and reasons.
Key Competencies
- Large Language Models (LLMs): Understanding transformer architectures and how LLMs work
- Conversational AI: Building chatbots and virtual assistants for customer service
- Text Generation: Creating content at scale while maintaining quality
- Sentiment Analysis: Extracting insights from unstructured text data
- Multi-Modal AI: Combining text with images, audio, and video
Real-World Applications
From customer support automation to content creation, document summarization to code generation, NLP skills are in demand across virtually every sector.
Who Needs This
- NLP Engineers
- AI Application Developers
- Content Strategists
- Customer Experience Designers
- Product Managers
3. Prompt Engineering: The Art of AI Communication
Why It Matters
Prompt engineers require the highest proportion of Machine Learning & AI skills (22.8%) compared to machine learning engineers (21.6%) and data scientists (16.0%), though their focus is specifically on language models rather than general machine learning algorithms.
Despite initial hype, prompt engineering has evolved into a complementary skill embedded across multiple roles rather than always being a standalone position.
Key Competencies
- Effective Prompt Design: Crafting instructions that generate accurate, relevant outputs
- Chain-of-Thought Prompting: Breaking down complex tasks into logical sequences
- Context Management: Providing appropriate information for optimal results
- Prompt Testing: Validating and iterating on prompt effectiveness
- Multi-Modal Prompting: Combining text, images, and other data types
Specialized Areas
- Prompt Security: Mitigating risks from manipulated AI outputs
- Prompt Automation: Building systems for scalable prompt generation
- Domain-Specific Prompting: Tailoring approaches for specialized industries
Real-World Applications
Every company using ChatGPT, Claude, Gemini, or other LLMs needs professionals who can extract maximum value from these tools. From marketing teams creating content to legal departments drafting documents, prompt engineering skills amplify productivity.
Who Needs This
- AI Product Managers
- Content Creators
- Marketing Professionals
- Business Analysts
- Operations Managers
4. Data Engineering & Management
Why It Matters
AI without data is like a car without fuel. As the world generates more than 180 zettabytes of data by 2026, companies need professionals who can collect, process, and manage massive datasets with precision.
Key Competencies
- Data Pipeline Design: Building robust systems for data flow
- Real-Time Processing: Handling streaming data at scale
- Tools Mastery: Apache Spark, Databricks, Kafka, and similar platforms
- Data Governance: Ensuring compliance with regulations like GDPR
- Data Quality: Maintaining accuracy and reliability of datasets
- Cloud Data Platforms: AWS, Azure, and Google Cloud data services
Real-World Applications
Industries like finance and logistics rely on seamless data flows to make instant decisions. E-commerce companies need real-time analytics to personalize experiences. Healthcare organizations require secure, compliant data management for patient records.
Who Needs This
- Data Engineers
- Data Architects
- MLOps Engineers
- Analytics Engineers
- Platform Engineers
5. MLOps & Model Deployment
Why It Matters
Having a great model isn’t enough—it must be deployed, monitored, and updated seamlessly. MLOps is the backbone of AI in production, ensuring models are scalable, reliable, and continuously improving.
Key Competencies
- Containerization: Docker and Kubernetes for model packaging
- CI/CD Pipelines: Automated testing and deployment workflows
- Model Monitoring: Tracking performance and detecting drift
- Version Control: Managing model iterations and reproducibility
- Infrastructure as Code: Terraform and similar tools
- API Development: Building interfaces for model access (FastAPI, Flask)
Real-World Applications
Every AI product needs MLOps to move from prototype to production. Whether it’s a recommendation engine, fraud detection system, or chatbot, MLOps engineers ensure reliability and scalability.
Who Needs This
- MLOps Engineers
- DevOps Engineers
- Site Reliability Engineers
- Platform Engineers
- Production ML Engineers
6. AI Ethics & Responsible AI
Why It Matters
AI ethics, responsible AI, and AI literacy are expected to rise dramatically in importance, while traditional skills like basic programming and content creation may become less relevant. More than 100,000 professionals with expertise in AI ethics and governance are now requested annually, with concentration highest in the financial and information sectors.
Key Competencies
- Bias Detection & Mitigation: Identifying and reducing algorithmic bias
- Explainability: Ensuring AI decisions can be understood and justified
- Privacy Protection: Implementing data protection standards
- Regulatory Compliance: Understanding GDPR, CCPA, AI Act, and similar regulations
- Ethical Frameworks: Developing principles for responsible AI development
- Stakeholder Engagement: Communicating ethical considerations to diverse audiences
Real-World Applications
Financial institutions need to ensure lending algorithms don’t discriminate. Healthcare organizations must explain AI-driven diagnoses. Tech companies must protect user privacy while delivering personalized experiences.
Who Needs This
- AI Ethics Specialists
- Compliance Officers
- Risk Managers
- Policy Advisors
- Product Managers
- Legal Counsel
7. Cloud Computing & Infrastructure
Why It Matters
The global cloud computing market is projected to surpass $1 trillion by 2028, with AWS, Azure, and Google Cloud leading the charge. Cloud platforms provide the computational power necessary for training and deploying AI models at scale.
Key Competencies
- Cloud Platforms: Deep expertise in AWS, Azure, or Google Cloud
- Serverless Architecture: Building cost-effective, scalable solutions
- Hybrid & Multi-Cloud: Managing complex infrastructure strategies
- GPU/TPU Management: Optimizing compute resources for AI workloads
- Cost Optimization: Balancing performance with budget constraints
- Security: Implementing cloud security best practices
Real-World Applications
Organizations are adopting hybrid and multi-cloud strategies, creating sharp demand for professionals who can manage complex infrastructures and automate deployments at scale.
Who Needs This
- Cloud Architects
- Cloud Engineers
- Infrastructure Engineers
- DevOps Specialists
- Solutions Architects
8. Computer Vision
Why It Matters
Computer vision applications have gone mainstream, from manufacturing quality control to security systems, augmented reality to medical imaging.
Key Competencies
- Image Processing: Preprocessing and augmentation techniques
- Object Detection & Recognition: Identifying and classifying visual elements
- Image Segmentation: Pixel-level understanding of images
- Video Analysis: Processing temporal data for action recognition
- 3D Vision: Depth perception and spatial understanding
- Edge Deployment: Running vision models on devices with limited resources
Real-World Applications
Autonomous vehicles, facial recognition systems, medical imaging analysis, retail inventory management, and AR/VR experiences all rely on computer vision.
Who Needs This
- Computer Vision Engineers
- Robotics Engineers
- Research Scientists
- AR/VR Developers
- Quality Assurance Engineers
9. Domain-Specific AI Expertise
Why It Matters
Generic AI models don’t cut it when domain nuance is required. Vertical AI is growing fast, with industries demanding professionals who understand both AI and their specific sector.
Key Competencies
- Industry Knowledge: Deep understanding of sector-specific challenges
- Regulatory Awareness: Knowing industry-specific compliance requirements
- Use Case Identification: Finding AI opportunities within domain constraints
- Model Customization: Fine-tuning AI for specialized applications
- Stakeholder Translation: Bridging technical and business contexts
High-Demand Sectors
- Healthcare: Medical imaging, drug discovery, personalized medicine
- Finance: Risk modeling, fraud detection, algorithmic trading
- Legal: Contract analysis, legal research, compliance monitoring
- Manufacturing: Predictive maintenance, quality control, supply chain optimization
- Retail: Demand forecasting, customer analytics, inventory management
Who Needs This
- Industry-Specific AI Consultants
- Applied Scientists
- Product Specialists
- Business Analysts with AI skills
- Technical Subject Matter Experts
10. Adaptability & Continuous Learning
Why It Matters
The most valuable AI skill of 2026 may not be a single technical skill—it’s adaptability. AI evolves so quickly that today’s cutting-edge tools may become outdated in just months.
Key Competencies
- Learning Agility: Quickly mastering new tools and frameworks
- Research Literacy: Understanding and implementing academic papers
- Experimentation Mindset: Trying new approaches without fear of failure
- Community Engagement: Participating in AI communities and open source
- Cross-Disciplinary Thinking: Connecting AI with other fields like cybersecurity, blockchain, or robotics
How to Develop This
- Follow AI research and thought leaders
- Experiment with open-source frameworks and tools
- Attend conferences and meetups
- Contribute to open-source projects
- Take on projects outside your comfort zone
Who Needs This
Everyone working in or around AI needs this skill. The field moves too fast for anyone to succeed without continuous learning.
Essential Complementary Skills
While technical AI skills are crucial, success in 2026 requires a broader skill set:
Communication & Collaboration
Employers increasingly value engineers who can translate technical systems into business, design, and ethical contexts. If you can talk to a data privacy lawyer, a UX researcher, and a DevOps engineer in the same day, you’re indispensable.
Key Abilities
- Explaining complex AI concepts to non-technical stakeholders
- Writing clear documentation
- Presenting findings and recommendations
- Working effectively in cross-functional teams
- Managing stakeholder expectations
Business Acumen
Understanding how AI creates business value separates good technologists from great ones.
Key Abilities
- ROI calculation and cost-benefit analysis
- Product thinking and user experience
- Strategic planning and prioritization
- Market analysis and competitive intelligence
- Change management
Creative Problem-Solving
AI tools can automate routine tasks, but creativity remains uniquely human.
Key Abilities
- Identifying novel applications for AI
- Designing innovative solutions
- Thinking outside conventional frameworks
- Combining disparate ideas
- Challenging assumptions
Soft Skills That Matter
- Critical Thinking: Evaluating AI outputs and making sound judgments
- Emotional Intelligence: Understanding human needs and concerns
- Leadership: Guiding teams through AI transformation
- Resilience: Adapting to rapid change and setbacks
- Curiosity: Asking questions and exploring possibilities
Industry-Specific Skill Demands
Technology & Software
Priority Skills: Machine Learning, MLOps, Prompt Engineering, Cloud Computing
Tech companies need full-stack AI capabilities—from research to deployment to scaling.
Finance & Banking
Priority Skills: Risk Modeling, AI Ethics, Regulatory Compliance, Fraud Detection
The financial sector shows the highest concentration of demand for AI ethics and governance expertise.
Healthcare & Life Sciences
Priority Skills: Medical AI, Computer Vision, Regulatory Knowledge, Data Privacy
Healthcare demands specialized expertise in medical imaging, drug discovery, and patient data protection.
Retail & E-Commerce
Priority Skills: Recommendation Systems, NLP, Customer Analytics, Supply Chain Optimization
Retail leverages AI for personalization, inventory management, and customer experience enhancement.
Manufacturing & Industrial
Priority Skills: Computer Vision, Predictive Maintenance, IoT Integration, Process Optimization
Manufacturing uses AI for quality control, predictive maintenance, and supply chain efficiency.
Legal & Professional Services
Priority Skills: NLP, Document Analysis, Compliance, AI Ethics
AI integration into traditional services like consulting and legal services is rapidly increasing, driving demand for skilled talent.
How to Develop These Skills
1. Choose Your Focus Area
Identify the field that excites you most—AI, cloud, cybersecurity, data, or development—and commit to building depth there.
Don’t try to master everything at once. Start with 2-3 skills that align with your career goals and current role.
2. Earn Relevant Certifications
Popular Certifications
- Cloud: AWS Solutions Architect, Microsoft Azure Administrator, Google Cloud Professional
- Security: CISSP, CEH (Certified Ethical Hacker)
- Data: Google Data Analytics Certificate, Databricks Certification
- AI/ML: TensorFlow Developer Certificate, AWS Machine Learning Specialty
- Ethics: Certified Ethical Emerging Technologist (CEET)
Credentials validate your expertise and open doors with employers.
3. Build Hands-On Experience
Employers value practical application. Create real-world projects—deploy an app in the cloud, analyze public datasets, or participate in open-source security challenges—that demonstrate your skills in action.
Project Ideas
- Build a chatbot using LLM APIs
- Create a recommendation system with real data
- Develop a computer vision application
- Contribute to open-source AI projects
- Participate in Kaggle competitions
- Build a personal AI portfolio website
4. Leverage Online Learning Platforms
Recommended Resources
- Coursera: Stanford ML courses, DeepLearning.AI specializations
- edX: MIT and Harvard AI programs
- Udacity: AI nanodegrees
- DataCamp: Data science and ML tracks
- Fast.ai: Practical deep learning courses
- Hugging Face: NLP and transformer courses
5. Stay Industry-Connected
Join professional communities, attend meetups, and follow thought leaders to keep pace with evolving technologies.
Ways to Connect
- Attend AI conferences (NeurIPS, ICML, CVPR)
- Join local AI meetups and tech events
- Participate in online communities (Reddit, Discord, LinkedIn groups)
- Follow AI researchers and practitioners on social media
- Subscribe to AI newsletters and podcasts
- Engage with academic papers and blog posts
6. Contribute to Open Source
Open source has become more strategic than ever in 2026. Contributing to repositories, building lightweight tools, or experimenting with pre-trained models gives you intuition that closed environments can’t replicate and builds reputation.
7. Practice Interdisciplinary Learning
Don’t just focus on AI—explore adjacent fields:
- Ethics & Philosophy: Understanding human values and moral reasoning
- Psychology: Knowing how humans think and make decisions
- Design: Creating intuitive user experiences
- Business: Understanding organizational dynamics and market forces
- Policy & Law: Grasping regulatory frameworks and compliance
Career Paths in AI for 2026
Entry-Level Opportunities
Junior Data Analyst
- Required Skills: SQL, Python, Data Visualization, Basic Statistics
- Path: Can grow into Data Scientist or AI Analyst roles
AI/ML Intern
- Required Skills: Python, Basic ML concepts, Eagerness to learn
- Path: Full-time AI Engineer or Research positions
Prompt Engineer (Junior)
- Required Skills: Writing, Critical Thinking, Basic AI understanding
- Path: AI Product Manager or Content Strategy roles
Mid-Level Positions
Machine Learning Engineer
- Required Skills: ML algorithms, Python, TensorFlow/PyTorch, MLOps
- Salary Range: $120,000 – $180,000
- Path: Senior ML Engineer, AI Research Scientist
Data Engineer
- Required Skills: SQL, Spark, Kafka, Cloud platforms, ETL pipelines
- Salary Range: $110,000 – $160,000
- Path: Senior Data Engineer, Data Architect
AI Product Manager
- Required Skills: Product management, AI understanding, Strategy
- Salary Range: $130,000 – $200,000
- Path: Senior PM, Director of AI Products
MLOps Engineer
- Required Skills: DevOps, ML deployment, Kubernetes, CI/CD
- Salary Range: $125,000 – $175,000
- Path: Senior MLOps, Platform Architect
Senior & Leadership Roles
Senior AI Research Scientist
- Required Skills: Advanced ML, Research experience, Publications
- Salary Range: $180,000 – $300,000+
- Path: Principal Scientist, Research Director
AI Ethics Lead
- Required Skills: Ethics, Policy, Technical understanding, Leadership
- Salary Range: $150,000 – $250,000
- Path: Head of AI Ethics, Chief AI Officer
Head of AI/Director of AI
- Required Skills: Technical expertise, Strategy, Team management
- Salary Range: $200,000 – $400,000+
- Path: VP of AI, Chief AI Officer
Chief AI Officer (CAIO)
- Required Skills: Executive leadership, AI vision, Business strategy
- Salary Range: $300,000 – $600,000+
- Path: CEO, Board positions
Common Mistakes to Avoid
1. Chasing Every Trend
Focus on fundamentals that endure rather than jumping between the latest hype cycles.
2. Ignoring the Business Side
Technical skills without business understanding limit career growth. Learn to connect AI capabilities to business outcomes.
3. Neglecting Soft Skills
Communication, collaboration, and leadership matter just as much as coding ability.
4. Staying in Your Comfort Zone
The AI field evolves rapidly. Continuous learning isn’t optional—it’s essential.
5. Overlooking Ethics
As AI adoption accelerates, responsible AI development has moved from optional to mandatory. Don’t build without considering implications.
6. Building in Isolation
The next generation of AI engineering will be less about isolated model performance and more about integration. Learn to work across disciplines.
7. Focusing Only on Theory
Employers want practical skills. Build projects, contribute to open source, and demonstrate real-world capabilities.
The Reality of AI Job Displacement
What the Data Shows
While concern about AI replacing jobs is valid, the reality is more nuanced:
Limited Current Impact: Gen AI is “very likely” to fully replace just 19 job-related skills—or 0.7% of roughly 2,900 skills analyzed, including basic math, prompt engineering, and image classification.
Transformation, Not Elimination: Almost half—46%—of skills in a typical U.S. job posting are poised for hybrid transformation by gen AI, where human oversight remains essential and AI performs significant routine tasks.
Concentrated Impact: Entry-level hiring in “AI exposed jobs” has dropped 13% since large language models started proliferating, with software development, customer service, and clerical work most vulnerable.
Preparing for Change
Rather than fearing displacement, position yourself as someone who can work alongside AI:
Embrace AI Tools: Learn to use AI to enhance your productivity rather than compete against it.
Develop Human Skills: Creativity, empathy, strategic thinking, and complex problem-solving remain uniquely human.
Stay Adaptable: The ability to learn new skills quickly is your best protection.
Build AI Literacy: Understanding how AI works helps you identify opportunities and limitations.
The Future: What Comes After 2026?
Emerging Trends to Watch
Agentic AI: Agentic AI is built on the same models as gen AI but also has the ability to perform tasks on users’ behalf. This represents the next evolution beyond passive AI tools.
Adaptive Systems: Adaptive prompting, where AI systems help refine prompts themselves rather than relying solely on human tweaking, represents a major trend for the future.
Quantum AI: The convergence of quantum computing and AI could unlock unprecedented capabilities.
AI-Human Collaboration: The future isn’t AI replacing humans—it’s humans and AI working together in ways we’re just beginning to understand.
Long-Term Predictions
New Job Categories: As with every technological revolution, AI will create entirely new job categories that don’t exist yet.
Increased Specialization: As the field matures, we’ll see more specialized roles emerge within AI domains.
Regulatory Evolution: Governments will continue developing AI regulations, creating demand for compliance and governance expertise.
Democratization: AI tools will become more accessible, making AI literacy essential across all professions.
Taking Action: Your 90-Day Plan
Month 1: Assessment & Foundation
Week 1-2: Self-Assessment
- Evaluate your current skills
- Identify gaps relevant to your career goals
- Research roles that interest you
- Join AI communities and forums
Week 3-4: Learning Foundation
- Enroll in a foundational AI course
- Set up development environment
- Learn Python basics if needed
- Start following AI thought leaders
Month 2: Skill Building
Week 5-6: Core Technical Skills
- Complete ML fundamentals course
- Practice with simple datasets
- Learn key frameworks (TensorFlow, PyTorch, or Hugging Face)
- Experiment with ChatGPT, Claude, or similar tools
Week 7-8: Hands-On Projects
- Build first ML project
- Create portfolio website
- Document learnings on blog or LinkedIn
- Contribute to open-source project
Month 3: Application & Growth
Week 9-10: Advanced Learning
- Take specialized course in your focus area
- Work on more complex project
- Attend AI meetup or conference
- Network with professionals in field
Week 11-12: Career Positioning
- Update resume with new skills and projects
- Create or update LinkedIn profile
- Apply for roles or pursue internal opportunities
- Set learning goals for next quarter
Final Thoughts: The Time to Act Is Now
The AI revolution isn’t coming—it’s already here. By 2030, AI could create 97 million new jobs, many requiring hybrid skill sets that combine AI with business or creative thinking.
The professionals who will thrive in 2026 and beyond aren’t necessarily the ones with the most technical expertise. They’re the ones who combine technical skills with business acumen, ethical awareness, and continuous learning. They’re the ones who see AI as a tool to amplify human capabilities rather than a threat to human relevance.
The key takeaways:
- Start now: Don’t wait for the “perfect” moment. Begin building AI skills today.
- Focus on fundamentals: Master the core concepts that will remain relevant as tools change.
- Build practical experience: Employers value demonstrated capabilities over theoretical knowledge.
- Stay curious: The field evolves rapidly. Embrace continuous learning as a lifestyle.
- Think interdisciplinary: The best opportunities lie at the intersection of AI and other domains.
- Remember the human element: Technical skills get you in the door, but soft skills drive long-term success.
- Be ethical: As AI becomes more powerful, responsible development becomes more critical.
The future belongs to those who prepare for it. Whether you’re a student choosing your path, a professional looking to transition, or a leader building your team, the AI skills you develop today will determine your opportunities tomorrow.
The question isn’t whether AI will transform your career—it’s whether you’ll lead that transformation or be left behind.
Start building your AI skills today. Your 2026 self will thank you.
Resources & Further Reading
Online Learning Platforms
- Coursera AI Specializations
- Fast.ai Practical Deep Learning
- DeepLearning.AI Courses
- Hugging Face NLP Course
- DataCamp AI Career Tracks
Communities & Forums
- r/MachineLearning (Reddit)
- AI Alignment Forum
- MLOps Community
- Hugging Face Forums
- Papers With Code
Conferences & Events
- NeurIPS (Neural Information Processing Systems)
- ICML (International Conference on Machine Learning)
- CVPR (Computer Vision and Pattern Recognition)
- AAAI (Association for Advancement of Artificial Intelligence)
- AI Summit (Various locations)
Books
- “Deep Learning” by Ian Goodfellow
- “Hands-On Machine Learning” by Aurélien Géron
- “The Hundred-Page Machine Learning Book” by Andriy Burkov
- “AI Ethics” by Mark Coeckelbergh
- “Designing Machine Learning Systems” by Chip Huyen
Tools & Platforms
- GitHub (Version control & collaboration)
- Kaggle (Datasets & competitions)
- Google Colab (Free compute)
- Weights & Biases (Experiment tracking)
- Hugging Face (NLP models & datasets)
Remember: The most valuable skill in 2026 isn’t knowing everything about AI—it’s knowing how to learn, adapt, and apply AI to solve real problems. Start your journey today.
