So what are Large Language Models again?

Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of textual data from books, articles, websites, and other sources to develop a deep understanding of language, grammar, context, and nuances. The term "large" refers to the size of the model, which is determined by the number of parameters (essentially the adjustable components in the model) it has—often running into billions or even trillions.

Large Language Models (LLMs) have seen rapid development, with various organizations contributing to their evolution. Here's a snapshot of some notable Large Language Models Available today:

1. Model: GPT-4.5

Developer: OpenAI

Release Date: February 2025

Price: $2.50-$10.00/MTok in 2025

2. Model: Claude 3.7

Developer: Anthropic

Release Date: February 2025

Price: $3.00-$15.00/MTok in 2025

3. Model: Gemini 2.0

Developer: Google DeepMind

Release Date: February 2025

Pricing not disclosed in 2025

4. Model: Llama 3.1

Developer: Meta AI

Release Date: July 2024

Price: $0.90/MTok in 2025

5. Model: DeepSeek

Developer: DeepSeek, Chinese AI startup

Release Date: January 2025.

Price: The R1 model is free for research and experimentation, with commercial usage subject to licensing terms.

6. Manus

Developer: Butterfly Effect, a Chinese startup

Release Date: March 2025.

Price: Currently, access is restricted to invite codes, and pricing details have not been disclosed.

Inspired by https://explodingtopics.com/blog/list-of-llms

Future-Proof Your Company’s AI Strategy: How a Strong Data Foundation Can Set You Up for Sustainable Innovation

1. Sustainable Innovation Requires Strong Data Foundations:

- Sustainable innovation involves creating an ecosystem that allows emerging technology to thrive, with a focus on establishing a robust data foundation.

2. Aligning Data and AI Programs:

- Data and AI efforts should be integrated from the start to enhance efficiency, reduce costs, and streamline operations. Companies that do this see better financial and operational outcomes.

3. Key Questions for Preparing AI-Ready Data:

- Companies must ask questions such as:

- How does their data align with business outcomes?

- What roadblocks may arise?

- How can they optimize data for greater efficiency?

4. Best Practices for a Strong Data Foundation:

- Ensure data quality and governance using tools like AI-powered catalogs.

- Strengthen data security and compliance with global regulations.

- Explore strategic partnerships to enhance analytics and AI capabilities.

- Foster a data-driven culture by promoting AI literacy, cross-team collaboration, and democratizing data access.

5. Impact of a Solid Data Foundation:

- Businesses with strong data foundations are better positioned to scale AI innovations, improve operational efficiencies, and achieve greater financial impact.

Summarized from: Unite.AI

Manus AI takes centre stage in China’s growing AI scene

• Manus AI Introduction: A Chinese startup, Butterfly Effect, has developed an AI system called Manus, which is gaining attention for its autonomous task execution capabilities.

• Functionality: Manus can perform complex tasks autonomously, such as generating reports, automating workflows, and writing code, without constant user input. It integrates multiple AI models, including Anthropic's Claude 3.5 Sonnet and Alibaba's Qwen models.

• Performance and Reception: Manus has outperformed competitors like OpenAI’s Deep Research in benchmark tests. Early adopters and influencers have praised its efficiency and versatility, but critics question its reliance on existing AI models.

• Concerns: The platform is currently available through an invite-only beta, leading to server limitations and exclusivity frustrations. Some users are skeptical about Manus's long-term scalability and originality.

• Content Regulation: Manus has been noted for providing uncensored responses on sensitive topics, raising questions about its content control in a highly regulated environment like China.

• Future Outlook: Manus is seen as a potential game-changer in human-machine collaboration, but its scalability and broader adoption remain uncertain.

Summarized from: https://tribune.com.pk/Manus AI takes centre stage in China’s growing AI scene

List of AI-powered data analysis and visualization tools available in the market as of 2025

1.Microsoft Power BI

Developer: Microsoft

Capabilities: AI-powered features like Quick Insights (automated insights generation) and Q&A (natural language queries). Integration with Azure AI and Machine Learning for advanced analytics. Custom visualizations and real- time dashboards. Automated data preparation and cleaning using Dataflows .

Cost : Power BI Pro: $10/user/month. Power BI Premium: $20/user/month or $4,995/capacity/month (for larger organizations).

2.Tableau

Developer : Salesforce

Capabilities : Advanced data visualization and interactive dashboards. AI- driven insights via Tableau Einstein (e.g., trend analysis, forecasting, and anomaly detection). Natural language processing (NLP) for querying data using plain language. Integration with multiple data sources (cloud, on-premise, and hybrid).

Cost: Tableau Creator: $70/user/month (includes Tableau Desktop, Prep, and a Creator license for Tableau Server/Cloud). Tableau Explorer: $42/user/month (for viewing and interacting with dashboards). Tableau Viewer: $15/user/month (for viewing only).

3. Looker

Developer : Google Cloud

Capabilities : Real time data exploration and visualization. Embedded analytics for integrating insights into applications.

AI-powered predictive analytics and anomaly detection. Integration with Google BigQuery and other cloud data sources.

Cost : Custom pricing based on usage and deployment (contact Google Cloud for a quote).

4. Qlik Sense

Developer : Qlik

Capabilities : Associative data modeling for exploring data relationships. AI driven insights via Qlik Insight Bot (natural language interaction). Automated data preparation and predictive analytics. Real time dashboards and visualizations.

Cost : Qlik Sense Business: $30/user/month. Qlik Sense Enterprise: Custom pricing (contact Qlik for details).

5. ThoughtSpot

Developer: ThoughtSpot

Capabilities: AI-powered search-driven analytics (users can ask questions in natural language). Automated insights and anomaly detection. Integration with cloud data warehouses like Snowflake, BigQuery, and Redshift. Real-time collaboration and sharing of insights.

Cost: Custom pricing based on deployment and usage (contact ThoughtSpot for a quote).

6. Sisense

Developer: Sisense

Capabilities: AI-driven analytics for predictive modeling and forecasting. Natural language processing for querying data. Embedded analytics for integrating insights into applications. Real-time dashboards and interactive visualizations.

Cost: Custom pricing based on deployment and usage (contact Sisense for a quote).

7. Alteryx

Developer: Alteryx

Capabilities: AI-powered data preparation, blending, and advanced analytics. Predictive analytics and machine learning capabilities. Automated insights and reporting. Integration with Tableau, Power BI, and other visualization tools.

Cost: Alteryx Designer: $5,195/user/year. Alteryx Server: Custom pricing (contact Alteryx for details).

8. Domo

Developer: Domo

Capabilities: AI-driven insights and anomaly detection. Real-time dashboards and visualizations. Natural language queries for data exploration. Integration with cloud data sources and APIs.

Cost: Custom pricing based on usage and deployment (contact Domo for a quote).

9. Zoho Analytics

Developer: Zoho Corporation

Capabilities: AI-powered assistant Zia for natural language queries and insights. Automated data preparation and blending. Interactive dashboards and visualizations. Integration with Zoho apps and third-party tools.

Cost: Basic Plan: $24/month (for 2 users). Standard Plan: $48/month (for 5 users).Premium Plan: $115/month (for 15 users). $455/month (for 50 users).

10. Databricks

Developer: Databricks

Capabilities: Unified analytics platform for data engineering, machine learning, and visualization. AI-powered insights and predictive analytics. Integration with Apache Spark for large-scale data processing. Collaborative workspace for data teams.

Cost: Databricks SQL: $200/DBU (Databricks Unit) per month. Databricks Machine Learning: $300/DBU per month. Custom pricing for enterprise plans.

11. SAS Visual Analytics

Developer: SAS Institute

Capabilities: AI-driven analytics for forecasting and decision-making. Interactive dashboards and visualizations. Natural language processing for querying data. Integration with SAS Viya for advanced analytics.

Cost: Custom pricing based on deployment and usage (contact SAS for a quote).

12. Einstein Analytics (Tableau CRM)

Developer: Salesforce

Capabilities: AI-powered analytics for Salesforce data. Predictive analytics and automated insights. Natural language queries and interactive dashboards. Integration with Salesforce CRM and other data sources.

Cost: Included in Salesforce CRM licenses (additional costs may apply for advanced features).

13. Polymer

Developer: Polymer

Capabilities: AI-powered data transformation and visualization. Automated insights and interactive dashboards. Integration with Google Sheets, BigQuery, and other data sources.

Cost: Free Plan: Limited features. Pro Plan: $20/user/month. Team Plan: $50/user/month.

14. Tellius

Developer: Tellius

Capabilities: AI-driven insights and natural language queries. Automated data preparation and predictive analytics. Interactive dashboards and visualizations.

Cost: Custom pricing based on deployment and usage (contact Tellius for a quote).

15. Mode Analytics

Developer: Mode

Capabilities: AI powered insights and predictive analytics. Interactive dashboards and visualizations. Integration with SQL, Python, and R for advanced analytics.

Cost: Starter Plan: Free (limited features). Pro Plan: $25/user/month. Enterprise Plan: Custom pricing.

16. Google Cloud AI Platform

Developer: Google

Capabilities: Machine learning model training, data analysis, predictive analytics, and integration with Google Cloud services.

Costs: Google Cloud AI Platform pricing varies based on usage and resources. It offers a pay-as-you-go model with no upfront costs.

17. SAS Viya

Developer: SAS Institute

Capabilities: Advanced analytics, machine learning, data visualization, and integration with various data sources.

Costs: SAS Viya pricing is customized based on the organization's needs and usage. It typically involves a subscription model.

18. RapidMiner

Developer: Altair Engineering

Capabilities: End-to-end data science, machine learning, data preparation, and model deployment.

Costs: RapidMiner offers a free version with limited features. The Studio Professional plan costs $2,500 per user per year, and the Studio Enterprise plan pricing is available upon request.

19. KNIME

Developer: KNIME AG

Capabilities: Open-source data pipelining, machine learning, data visualization, and integration with various data sources.

Costs: KNIME offers a free open-source version. The commercial version pricing is available upon request.

20. Talend

Developer: Talend, Inc.

Capabilities: Data integration, data quality, and data preparation.

Costs: Talend offers a free open-source version. The commercial version pricing is available upon request.

21. IBM Watson Analytics

Developer: IBM

Capabilities: AI-driven insights, natural language processing, predictive analytics, and data visualization.

Costs: IBM Watson Analytics offers a free tier with limited features. The Plus plan costs $30 per user per month, and the Professional plan costs $80 per user per month.

22. Amazon SageMaker

Developer: Amazon Web Services (AWS)

Capabilities: Amazon SageMaker is a cloud-based platform for building and deploying AI models. It offers a range of tools for data preparation, visualization, and analysis.

Pricing is based on usage.

23. DataRobot

Developer: DataRobot

Capabilities: DataRobot is a platform that automates the building and deploying of machine learning models. It offers a range of tools for data preparation, visualization, Pricing is custom and available upon request.

24. Akkio

Developer: Akkio

Capabilities: Akkio is a no-code AI analysis platform for predictive analytics and forecasting

Cost: Paid plans starts at $19 per month.

25. H2O.ai

Developer: H2O.ai

Capabilities: H2O.ai is an open-source, in-memory machine-learning platform that enables users to develop and deploy ML models for performing predictive analytics.

Cost: H2O.ai offers a free trial. Pricing is custom and available upon request.

26.Julius AI

Developer: Caesar Labs

Advanced Analysis: Perform complex analyses like forecasting, regression, and statistical modeling. Data Cleaning: Effortlessly prepare and clean your data for analysis. Report Generation: Create polished reports summarizing your findings. Problem Solving: Scan and solve math, physics, and chemistry problems.

Cost: Julius AI offers a free plan with limited usage. Paid plans are available for users who need more features and usage. They also offer discounts for students and academics. Lite $29/mo Standard $65/mo Team $76/mo

These tools cater to a wide range of users, from small businesses to large enterprises, and offer varying levels of AI-powered capabilities for data analysis and visualization. Pricing often depends on the scale of deployment, number of users, and specific features required. Many vendors offer free trials or demos to help users evaluate the tools before committing. There could be other minor tools available in the market unmentioned here at this time.

How Agentic AI is Democratizing Data Expertise

Agentic AI refers to advanced artificial intelligence systems that act autonomously, proactively assisting users in analyzing data, generating insights, and even making recommendations. Unlike traditional AI tools that require extensive user input and technical know-how, agentic AI is designed to be intuitive, conversational, and user-friendly. It bridges the gap between complex data systems and everyday users, enabling them to harness the power of data without needing to write a single line of code.

One of the most significant impacts of agentic AI is its ability to democratize data expertise. In the past, data analysis was often siloed within specialized teams, creating bottlenecks and slowing down decision-making processes. With agentic AI, employees across departments—from marketing to finance to operations—can access and interpret data in real time. For example, a marketing manager can use an AI-powered tool to analyze customer behavior trends, while a sales representative can generate personalized sales forecasts without relying on a data analyst.

This shift is not just about convenience; it’s about fostering a culture of data-driven decision-making. When employees have easy access to actionable insights, they can respond more quickly to challenges and opportunities. Agentic AI also reduces the risk of misinterpretation by providing clear, context-aware explanations of data, ensuring that even non-experts can understand and act on the information.

Moreover, agentic AI is leveling the playing field for smaller businesses. In the past, only large enterprises with deep pockets could afford to build and maintain sophisticated data analytics teams. Now, with AI-driven tools becoming more accessible and affordable, small and medium-sized businesses can compete on the same footing. They can leverage agentic AI to optimize operations, improve customer experiences, and identify new growth opportunities—all without needing to hire a team of data scientists.

In conclusion, agentic AI is revolutionizing the way we interact with data, making it easier than ever for everyone to become a data expert. By breaking down barriers and empowering users at all levels, this technology is driving innovation, improving efficiency, and fostering a more inclusive data-driven culture. As we continue to embrace agentic AI, the possibilities for growth and transformation are limitless—and the future of data expertise is brighter than ever.

Insights and POV inspired by CIO.

Unlocking the Power of AI for Social Good: 20 Grants for NGOs

Artificial Intelligence (AI) has emerged as a transformative force across various sectors, including healthcare, education, and environmental conservation. Its potential to analyze vast amounts of data, identify patterns, and automate processes makes it an invaluable tool for non-governmental organizations (NGOs) striving to address complex social issues. The concept of “AI for Social Good” encapsulates the use of AI technologies to enhance social welfare, improve community resilience, and drive sustainable development. As NGOs increasingly adopt AI solutions, they can leverage these technologies to optimize their operations, enhance decision-making, and ultimately amplify their impact.

The Importance of Grants for NGOs Using AI

Grants play a crucial role in supporting NGOs that are integrating AI into their operations. Beyond financial support, grants validate the work of NGOs and signal to other potential funders that an organization is engaged in meaningful and innovative efforts. Additionally, grants often come with resources such as training, mentorship, and networking opportunities, which can empower NGOs to build their capacity in AI and sustain their projects over time. By serving as a catalyst for innovation, grants enable organizations to experiment with new ideas and approaches that can lead to transformative change.

Top 5 Grants for NGOs Using AI

1. The Google.org AI for Social Good Program: This initiative provides funding and support to organizations leveraging AI to tackle pressing social issues. Google.org offers grants ranging from $500,000 to $2 million for projects that demonstrate innovative uses of AI in areas such as disaster response, healthcare, and education.

2. The IBM Watson AI XPRIZE: This global competition encourages teams to develop AI solutions that address humanity’s grand challenges. While not a traditional grant program, finalists receive funding and mentorship opportunities to help bring their projects to fruition.

3. The Microsoft AI for Earth Program: This grant program focuses on environmental sustainability and supports projects that utilize AI to address climate change, biodiversity loss, and water scarcity. Selected organizations receive funding as well as access to Microsoft’s cloud computing resources.

4. The Omidyar Network: This philanthropic investment firm funds initiatives that leverage technology for social impact. They are particularly interested in projects that use AI to promote transparency, accountability, and civic engagement.

5. The Data.org Data Impact Challenge: This initiative supports organizations using data science and AI to drive social change. Grants are awarded based on the potential impact of the proposed project and its ability to leverage data effectively.

How to Apply for AI Grants

Applying for AI grants requires careful planning and preparation. NGOs should begin by thoroughly researching potential funding opportunities that align with their mission and project goals. Once suitable grants are identified, organizations must develop a compelling proposal that clearly outlines their project’s objectives, methodology, expected outcomes, and budget. It is essential to articulate how the proposed use of AI will enhance the project’s effectiveness and contribute to social good.

In addition to crafting a strong proposal, NGOs should pay attention to the application process itself. Many grant-making organizations have specific guidelines regarding submission formats, deadlines, and required documentation. Adhering to these guidelines is crucial for ensuring that applications are considered. Furthermore, engaging with funders early in the process can provide valuable insights into their priorities and expectations, increasing the likelihood of success.

Success Stories of NGOs Using AI Grants

Using AI to Combat Homelessness: DataKind’s work has demonstrated the potential of AI in addressing complex social issues. By analyzing data on homelessness, DataKind has been able to identify trends and patterns that can inform policy decisions and resource allocation. This approach has shown promise in reducing homelessness and improving the lives of vulnerable populations.

AI in Wildlife Conservation: The World Wildlife Fund (WWF) has utilized AI technology funded by grants to combat poaching and protect endangered species. By employing machine learning algorithms to analyze data from camera traps and satellite imagery, WWF has been able to identify poaching hotspots and deploy rangers more strategically. This innovative approach has led to significant reductions in poaching incidents in targeted regions.

Challenges and Opportunities for NGOs Using AI

While the integration of AI into NGO operations presents numerous opportunities, it also comes with its share of challenges. One major hurdle is the lack of technical expertise within many organizations. Implementing AI solutions often requires specialized knowledge in data science and machine learning, which may not be readily available among NGO staff. To overcome this challenge, organizations can seek partnerships with academic institutions or tech companies that can provide training or technical assistance.

Another challenge is ensuring ethical considerations are addressed when using AI technologies. NGOs must be vigilant about issues such as data privacy, algorithmic bias, and transparency in decision-making processes. By prioritizing ethical practices in their use of AI, organizations can build trust with stakeholders and ensure that their initiatives are both effective and responsible.

Future of AI Grants for Social Good

The future of AI grants for social good looks promising as more funders recognize the potential of technology to drive social change. As the demand for innovative solutions continues to grow, we can expect an increase in grant opportunities specifically targeting AI initiatives within the NGO sector. Additionally, as technology evolves, new funding models may emerge that allow for greater flexibility in how organizations utilize resources.

Moreover, collaboration between NGOs, tech companies, and academic institutions is likely to become more prevalent as stakeholders seek to maximize the impact of their efforts. By working together, these entities can share knowledge, resources, and best practices that enhance the effectiveness of AI-driven projects. Ultimately, the future holds great potential for NGOs leveraging AI grants to create lasting positive change in society.

Inspired by: (https://www2.fundsforngos.org/articles-listicles/20-grants-for-ngos-using-ai-for-social-good/)

Harnessing the Power of AI and Data Analytics for Social Good: A Nonprofit's Guide

Artificial intelligence (AI) and data analytics are no longer just buzzwords—they are powerful tools that can drive significant social impact. Nonprofits, often operating with limited resources, can leverage these technologies to optimize their operations, enhance outreach, and address pressing social issues. Here's how AI and data analytics are transforming the nonprofit sector and paving the way for a brighter future.

Predictive Analytics: Tackling Food Insecurity

One of the most compelling applications of AI in the nonprofit sector is predictive analytics. By analyzing vast amounts of data, AI can forecast weather patterns, seasonal crop yields, and economic conditions, helping organizations anticipate food shortages before they occur. For instance, the World Food Program uses predictive analytics to identify regions at high risk of food insecurity, allowing them to allocate aid strategically and prevent crises from escalating.

AI-Driven Poverty Mapping

AI's ability to analyze large datasets makes it an invaluable tool for identifying poverty hotspots. The Bill & Melinda Gates Foundation utilizes AI-driven predictive mapping to forecast where poverty is likely to rise. By examining economic indicators, health metrics, and infrastructure data, nonprofits can target their interventions more effectively, ensuring that resources reach the communities most in need.

Data Analytics in Education

Educational inequity is a significant challenge that many nonprofits aim to address. Organizations like Teach for All are using data analytics to enhance educational outcomes in low-income areas. By generating evidence-based insights on student performance, attendance, and engagement, educators can tailor their programs to meet the specific needs of their students, helping them reach their full potential.

AI and Social Justice

AI is also playing a crucial role in monitoring and addressing social injustices. Amnesty International, for example, uses AI-powered satellite imagery to detect patterns of violence, discrimination, and repression in real time. This technology enables nonprofit leaders to respond to emerging crises, raise awareness, and advocate for policy changes more effectively.

Geospatial AI for Climate Action

Climate change and wildlife conservation are areas where AI is making a significant impact. Geospatial AI helps organizations like The Nature Conservancy combat deforestation, monitor wildlife migration patterns, and track carbon emissions. AI-powered image analysis has even helped detect rare bird species that hadn't been seen in decades, showcasing the potential of technology to protect our planet.

Overcoming Challenges and Embracing Opportunities

While the benefits of AI and data analytics are clear, nonprofits may face challenges in adopting these technologies. Limited budgets, lack of technical expertise, and data privacy concerns are common hurdles. However, by partnering with tech-savvy organizations, investing in training, and prioritizing data security, nonprofits can overcome these obstacles and harness the full potential of AI for social good.

In conclusion, AI and data analytics offer nonprofits unprecedented opportunities to drive social impact. By embracing these technologies, organizations can optimize their operations, enhance their outreach, and address some of the world's most pressing issues. The future of social good is bright, and AI is lighting the way.

Inspired by BizTech Magazine|How Can Nonprofits Use AI and Data Analytics for Social Good?

AI: A Game Changer for Humanitarian Aid?

The world faces increasingly complex humanitarian crises, from natural disasters to armed conflicts. In this challenging landscape, the humanitarian sector is constantly seeking innovative solutions to improve its effectiveness. Artificial intelligence (AI) is emerging as a powerful tool with the potential to revolutionize how we deliver aid and support vulnerable populations.

A recent briefing note on Artificial Intelligence and the Humanitarian Sector by ReliefWeb highlights the growing importance of AI in this field. It underscores the potential for AI to enhance various aspects of humanitarian work, but also emphasizes the need for careful consideration of ethical implications and potential risks. This post explores some key takeaways from the briefing and discusses the exciting possibilities that AI offers for the future of humanitarian action.

The Promise of AI in Humanitarian Work:

AI's ability to process vast amounts of data and identify patterns makes it uniquely suited to address many of the challenges faced by humanitarian organizations. Here are just a few examples:

• Predictive Analytics: AI can analyze historical data and real-time information to predict where and when crises are likely to occur, allowing for proactive resource allocation and faster response times. Imagine being able to anticipate displacement patterns before a conflict escalates, enabling aid organizations to preposition supplies and personnel.

• Needs Assessment: AI can analyze satellite imagery, social media data, and other sources to quickly assess the needs of affected populations. This can help humanitarians understand the scale of a disaster, identify vulnerable groups, and prioritize aid delivery.

• Logistics and Supply Chain Management: AI-powered systems can optimize logistics and supply chains, ensuring that aid reaches those who need it most, efficiently and effectively. This is crucial in disaster zones where infrastructure may be damaged or access is limited.

• Personalized Aid: AI can help personalize aid delivery by analyzing individual needs and preferences. This can ensure that people receive the specific support they require, whether it's food, shelter, medical care, or psychosocial support.

• Disinformation Detection: In times of crisis, misinformation can spread rapidly, hindering relief efforts and exacerbating the situation. AI can be used to detect and combat disinformation, ensuring that accurate information reaches affected communities.

Navigating the Challenges:

While the potential benefits of AI in the humanitarian sector are significant, it's crucial to acknowledge the challenges and potential risks:

• Ethical Considerations: AI algorithms can perpetuate biases present in the data they are trained on, potentially leading to discriminatory outcomes. It's essential to ensure that AI systems are developed and used ethically, with careful consideration of fairness, transparency, and accountability.

• Data Privacy and Security: Humanitarian data is often highly sensitive, and it's crucial to protect the privacy and security of individuals. Robust data governance frameworks are needed to ensure responsible data handling.

• Digital Divide: Access to technology and digital literacy are not evenly distributed. It's important to ensure that AI-powered solutions do not exacerbate existing inequalities and that all communities can benefit from these advancements.

• Human-Centered Approach: AI should be seen as a tool to augment, not replace, humanitarians. It's crucial to maintain a human-centered approach to humanitarian work, ensuring that technology serves the needs of affected populations.

The Future of Humanitarian Action:

AI has the potential to transform the humanitarian sector, enabling us to respond to crises more effectively and efficiently. However, realizing this potential requires a collaborative effort involving humanitarian organizations, technology developers, policymakers, and affected communities. By addressing the ethical considerations and challenges proactively, we can harness the power of AI to create a more resilient and equitable future for all. The briefing note by ReliefWeb serves as a valuable resource for understanding the complexities and opportunities presented by AI in the humanitarian sector, and it's a conversation we all need to be a part of.

Inspired by: Briefing Note on Artificial Intelligence and the Humanitarian Sector - World | ReliefWeb

Harnessing the Power of Business Analytics and Artificial Intelligence: A Roadmap to Data-Driven Success

The convergence of business analytics (BA) and artificial intelligence (AI) is revolutionizing the way organizations operate. This integration is enabling companies to extract valuable insights from massive datasets, make more informed decisions, and ultimately gain a competitive edge.

Understanding Business Analytics (BA)

Business analytics encompasses various techniques, including descriptive, predictive, and prescriptive analytics. These approaches help organizations uncover patterns and trends in historical data, forecast future outcomes, and recommend optimal actions. Techniques such as data mining, statistical analysis, and data visualization play a crucial role in transforming raw data into actionable insights.

Artificial Intelligence (AI) at a Glance

AI, particularly through machine learning and deep learning, simulates human intelligence to recognize patterns, make predictions, and automate tasks. Machine learning algorithms enable computers to learn from data without explicit programming, while deep learning models can handle complex data structures, such as images and text, with remarkable accuracy.

The Synergy of BA and AI

When combined, BA and AI create a powerful framework for data-driven success. AI enhances BA by automating data preprocessing, feature selection, and model optimization. This allows organizations to gain real-time insights, personalized recommendations, and optimized operations. The integration of AI-driven analytics can lead to significant improvements in areas such as customer analytics, supply chain optimization, risk management, process automation, and predictive maintenance.

Applications Across Industries

1. Customer Analytics and Personalized Marketing: AI-powered analytics can analyze customer behavior, preferences, and sentiments to deliver personalized recommendations and improve targeting strategies.

2. Supply Chain Optimization and Demand Forecasting: AI and BA can predict demand patterns, optimize inventory levels, and streamline logistics operations, leading to cost savings and improved efficiency.

3. Risk Management and Fraud Detection: By analyzing historical data and real-time information, organizations can identify potential risks and detect fraudulent activities.

4. Process Automation and Intelligent Systems: AI can automate repetitive tasks and develop intelligent systems that enhance operational efficiency.

5. Predictive Maintenance and Asset Optimization: AI-driven analytics can predict equipment failures and optimize maintenance schedules, reducing downtime and extending asset lifecycles.

Overcoming Challenges

Despite the potential benefits, integrating BA and AI comes with challenges. Data quality and privacy concerns, ethical considerations, and organizational readiness are significant hurdles. Addressing these issues requires robust data governance frameworks, strategies for mitigating biases in AI algorithms, and investments in talent acquisition and upskilling.

Future Trends

Looking ahead, advancements in explainable AI, augmented analytics, and edge computing will continue to shape the integration of BA and AI. Ethical considerations and responsible AI practices will be critical to ensure fair and transparent decision-making. The collaboration between humans and AI will enhance decision-making, fostering a more data-driven culture within organizations.

In conclusion, the integration of business analytics and artificial intelligence offers unprecedented opportunities for organizations to harness the power of data, drive innovation, and achieve data-driven success. By overcoming challenges and embracing emerging trends, businesses can unlock new growth opportunities and maintain a competitive edge in the digital age.

Blog inspired by this research paper: https://www.researchgate.net/publication/375681699

BMW I Ventures Invests $4.3 Million In Athenic AI To Enhance Data Analysis Accessibility

BMW i Ventures has invested $4.3 million in Athenic AI, a company specializing in data analysis and business intelligence. This investment aims to enhance data analysis accessibility by simplifying data analytics through a self-service system powered by large language models1. Athenic AI's platform allows users to ask questions in natural language and receive answers quickly through tables, graphs, or reports. The platform integrates data from various sources, democratizing access to critical insights and accelerating decision-making across teams.

The company focuses on four main areas:

1. Empowering users with instant data analysis.

2. Breaking down barriers for non-technical users.

3. Enabling seamless integration of insights into daily workflows.

4. Tailoring insights to individual needs.

This investment is expected to reduce workflow time from weeks to minutes and help organizations uncover actionable insights. Penske Media Corporation (PMC) has already seen benefits from using Athenic AI, allowing non-technical users to analyze data more directly and quickly1.

BMW i Ventures is an independent venture capital firm under BMW Group, investing in startups shaping the future of mobility and sustainability. Athenic AI aims to empower individuals and enterprises to understand their data better using advanced Generative AI technology1.

Summarized from: DriveSpark: Read more at: https://www.drivespark.com/four-wheelers/2025/bmw-i-ventures-invests-in-athenic-ai-data-analysis-011-64811.html?30012025225159

Building Reliable AI: Start with Data Prep, Governance, and Training

Artificial intelligence (AI) promises transformative business outcomes, but its reliability hinges on three foundational pillars: data preparation, governance, and training. A recent article on Diginomica underscores that skipping these steps risks flawed models, biased outputs, and compliance headaches. Here’s how to lay the groundwork for trustworthy AI.

1. Data Preparation: Clean, Structured, and Contextual

AI models are only as good as the data they’re fed. Raw data is often messy, incomplete, or siloed, leading to inaccurate predictions. To start:

- Audit your data: Identify gaps, duplicates, or inconsistencies.

- Standardize formats: Ensure datasets are compatible and labeled consistently.

- Enrich with context: Combine internal data with external sources (e.g., market trends) for deeper insights.

Investing in tools like data catalogs or automated cleaning pipelines can streamline this process. The goal? Turn chaotic data into a coherent, AI-ready asset.

2. Governance: Policies to Ensure Quality and Compliance

Without governance, AI initiatives can spiral into ethical and legal risks. A robust framework includes:

- Clear ownership: Assign roles (e.g., data stewards) to oversee quality and usage.

- Compliance checks: Align with regulations like GDPR or industry-specific standards.

- Bias mitigation: Audit datasets and algorithms to reduce discriminatory outcomes.

Governance isn’t just about control—it builds stakeholder trust. Start by documenting policies and implementing monitoring tools to track data lineage and model behavior.

3. Training: For Both Models and Teams

Training is a dual priority:

- Model training: Use diverse, high-quality datasets to improve accuracy. Regularly retrain models to adapt to new data.

- Employee upskilling: Equip teams with AI literacy. Cross-functional training ensures technical and non-technical staff collaborate effectively.

Organizations should also foster a culture of experimentation, where failures become learning opportunities.

Where to Begin?

The journey starts with small, scalable steps:

1. Prioritize use cases: Focus on high-impact areas where AI can solve clear problems.

2. Build cross-functional teams: Include data scientists, legal experts, and domain specialists.

3. Iterate and improve: Treat AI as a continuous process—refine data, policies, and skills over time.

The Bottom Line

Reliable AI isn’t a plug-and-play solution. It demands meticulous data preparation, rigorous governance, and ongoing education. By addressing these pillars, businesses can unlock AI’s potential while minimizing risks. As the article notes, “The road to trustworthy AI starts long before the first algorithm runs”—it begins with laying the right foundation.

Inspired by insights from Diginomica.

DeepSeek Panic: How an AI Breakthrough Sparked Market Chaos—and Why Trump Calls It ‘Positive’

The tech world is reeling this week as a seismic event unfolds: DeepSeek, the Chinese AI startup once hailed as a rising star, has triggered a global market panic with its latest breakthrough—a hyper-advanced language model dubbed “DeepSeek-Nova.” While AI advancements are nothing new, DeepSeek’s rapid progress has exposed fissures in the global tech ecosystem, sending shockwaves through stock markets and igniting political debates. Meanwhile, former President Donald Trump has defied the turmoil, calling AI development “a positive force for America,” even as tech stocks nosedive. Here’s what you need to know.

The DeepSeek-Nova Announcement: A Catalyst for Chaos

On Monday, DeepSeek unveiled Nova, an AI model reportedly capable of outperforming leading systems like GPT-4 and Google’s Gemini in specialized tasks, from legal analysis to real-time multilingual translation. But it wasn’t just Nova’s technical specs that stunned observers—it was the startup’s claim that the model could be deployed at “one-tenth the cost” of competitors, thanks to groundbreaking energy-efficient algorithms.

Within hours, tech stocks plummeted. The NASDAQ Composite dropped 4.2%, with AI-heavy companies like NVIDIA, Microsoft, and Meta absorbing double-digit losses. Investors fear DeepSeek’s cost-cutting innovation could undercut Western AI giants, destabilizing a sector already grappling with soaring R&D expenses.

“This isn’t just about competition—it’s about existential risk,” said one Wall Street analyst. “If China can produce premium AI at bargain prices, the entire global market shifts overnight.”

Trump’s Defiant Stance: “AI Development is Positive”

Amid the sell-off, Donald Trump broke ranks with market skeptics. In a campaign rally speech, he praised AI as “one of the greatest opportunities for American greatness,” arguing that fears about DeepSeek are overblown.

“Sure, the stocks are down—big deal! AI is the future, and America will dominate that future,” Trump said. “We can’t let a little panic stop us from winning. China’s got good people, but we’ve got better.”

His comments drew mixed reactions. Supporters applauded his long-term vision, while critics accused him of downplaying legitimate concerns about China’s AI ascendancy and its implications for U.S. economic security.

Why DeepSeek-Nova Has Everyone Spooked

The panic isn’t just about economics—it’s about geopolitics. DeepSeek’s rise underscores China’s accelerating bid to lead in critical technologies, despite U.S. efforts to curb its access to advanced chips and AI know-how. Key concerns include:

1. Market Disruption: Nova’s cost efficiency could make Chinese AI products irresistible to developing nations, expanding Beijing’s influence in regions like Southeast Asia, Africa, and Latin America.

2. Data Security: DeepSeek’s ties to Chinese state-linked entities (though officially denied) have reignited fears that AI tools could be weaponized for espionage or propaganda.

3. Regulatory Asymmetry: While U.S. AI firms face tightening regulations around ethics and safety, Chinese companies like DeepSeek operate with fewer constraints, enabling faster iteration—and risk-taking.

“This is a Sputnik moment for AI,” warned a former Pentagon official. “If we don’t respond strategically, we’ll be playing catch-up in the most transformative tech of our lifetimes.”

The Silicon Valley Dilemma: Innovate or Regulate?

The DeepSeek panic has intensified a simmering debate in Silicon Valley. Tech leaders are split between two camps:

- Accelerationists: Push to deregulate and pour resources into AI R&D to outpace China.

- Safety-First Advocates: Argue that unchecked innovation could lead to catastrophic outcomes, from job displacement to autonomous weapons.

DeepSeek’s breakthrough adds fuel to the fire. “If we slow down for safety, China won’t,” said a venture capitalist. “But if we rush, we risk creating tools we can’t control.”

Global Reactions: From Brussels to Beijing

- EU: The European Commission announced an emergency summit to discuss AI sovereignty and “defensive investments” in homegrown startups.

- China: State media praised DeepSeek as a “triumph of socialist innovation,” while quietly urging caution to avoid provoking U.S. sanctions.

- Tech Workers: Layoff fears are mounting, with one OpenAI engineer tweeting, “Suddenly, my job feels a lot less secure.”

The Road Ahead: Collaboration or Cold War?

The DeepSeek saga highlights a stark reality: AI is no longer just a technological race—it’s a battleground for global power. Yet some experts urge cooperation.

“The U.S. and China should establish guardrails together,” said an MIT AI ethicist. “A split into competing AI ‘blocs’ helps no one. Climate change, pandemics—these challenges require shared intelligence, not siloed systems.”

Final Take: Panic vs. Progress

While markets reel and politicians posture, DeepSeek’s Nova forces a reckoning. AI’s potential is immense, but its risks are equally profound. The question isn’t whether AI will reshape the world—it’s how, and who will steer that transformation.

For now, Trump’s optimism clashes with Wall Street’s jitters, but one thing is clear: The age of AI supremacy has begun, and the rules are being written in real time.

Keywords: DeepSeek AI panic, Trump AI comments, tech stock plunge, AI market disruption, US-China tech race, DeepSeek-Nova.

This blog post captures the hypothetical scenario outlined in the Forbes article, blending market drama, political rhetoric, and geopolitical analysis. For further details, refer to the original piece [here](https://www.forbes.com/sites/dereksaul/2025/01/27/deepseek-panic-live-updates-trump-calls-ai-development-positive-despite-tech-stock-plunge/).

DeepSeek: The Chinese AI Startup Challenging Silicon Valley's Dominance

Founded in 2023, DeepSeek has quickly positioned itself as a formidable contender in the AI race. While details about the company remain closely guarded, reports suggest it is backed by a team of world-class engineers and researchers, many of whom hail from China’s top tech universities and corporations. The startup’s mission? To develop AI systems that are not only highly efficient but also deeply aligned with real-world applications—from healthcare and finance to autonomous systems and beyond.

What sets DeepSeek apart is its focus on “practical intelligence.” Unlike many AI firms that prioritize theoretical breakthroughs, DeepSeek emphasizes solving tangible problems. For example, its early projects include AI-driven diagnostic tools for rural healthcare and optimization algorithms for manufacturing supply chains. This pragmatic approach has already attracted partnerships with major Chinese industries and government bodies, signaling confidence in its potential.

China’s AI Ecosystem: A Fertile Ground for Disruption

DeepSeek’s emergence is no accident. It reflects China’s broader strategy to dominate AI by 2030, as outlined in its national development plans. The country’s vast data resources, government support, and thriving tech ecosystem provide startups like DeepSeek with unparalleled advantages.

- Government Backing: China’s “Next Generation Artificial Intelligence Development Plan” funnels billions into research, infrastructure, and talent development. Startups benefit from subsidies, regulatory sandboxes, and access to state-backed datasets.

- Market Scale: With over 1 billion internet users and industries eager to digitize, Chinese AI firms have a massive testing ground for refining their technologies.

- Talent Pipeline: China produces more STEM graduates than any other nation, and its tech giants (like Alibaba, Tencent, and Baidu) have trained a generation of engineers now branching into startups.

DeepSeek is leveraging these factors to fast-track its growth—a model that Western competitors can no longer afford to ignore.

How DeepSeek Stands Out in a Crowded Field

While giants like OpenAI and Google DeepMind dominate headlines, DeepSeek is carving its niche by addressing gaps in the market:

1. Localized Solutions: DeepSeek tailors its AI models to Chinese language, culture, and industry needs—a critical edge in a market where Western tools often struggle with nuances.

2. Ethical AI Frameworks: The startup is reportedly investing in ethical AI development, including transparency protocols and bias mitigation, which could help it avoid controversies plaguing other firms.

3. Cost Efficiency: By optimizing algorithms for lower computational power, DeepSeek aims to make advanced AI accessible to smaller businesses, a key growth area in China’s economy.

“We’re not just building AI; we’re building AI that works for everyone,” a DeepSeek spokesperson recently stated—a mantra that underscores its user-centric philosophy.

Challenges on the Horizon

Despite its promise, DeepSeek faces significant hurdles. Geopolitical tensions between the U.S. and China have led to restrictions on AI chip exports, potentially limiting access to critical hardware. Additionally, global skepticism about Chinese data privacy practices could hinder international expansion.

Moreover, the startup must navigate China’s stringent internet regulations, which require AI systems to align with state policies. Balancing innovation with compliance will be a delicate act.

The Global Implications

DeepSeek’s ascent is more than a business success story—it’s a signpost for the future of AI. As Chinese startups gain momentum, the global AI race is becoming less lopsided. For policymakers and tech leaders worldwide, DeepSeek serves as a reminder:

- Collaboration Over Competition: Cross-border partnerships could accelerate breakthroughs while addressing shared challenges like ethics and safety.

- Diverse Perspectives Matter: AI developed in different cultural contexts may lead to more robust, inclusive technologies.

Final Thoughts: A New Chapter in AI

DeepSeek embodies the dynamism of China’s tech sector and its hunger to lead in AI. While it’s still early days, the startup’s trajectory highlights a shifting balance of power in the tech world. Whether DeepSeek becomes a household name or inspires the next wave of innovators, its story underscores a critical truth: The future of AI will be shaped not just in Silicon Valley, but in Beijing, Shenzhen, and beyond.

For now, the world is watching.

Keywords: Chinese AI startup, DeepSeek AI, AI innovation, China tech ecosystem, global AI race, ethical AI, practical intelligence.

This blog post synthesizes themes commonly covered in articles about emerging AI startups in China, emphasizing innovation, competition, and geopolitical context. For specific details from the original article, refer to [NewsNation’s coverage here](https://www.newsnationnow.com/business/tech/ai/deepseek-chinese-ai-startup/.

The Stargate AI Project: A $500 Billion Gamble to Dominate the Future

In a bold move to secure America's leadership in artificial intelligence, President Donald Trump on Day 2 in office, has announced the Stargate AI Project, a monumental $500 billion initiative. This ambitious venture, led by tech giants Softbank, Oracle, and OpenAI, aims to build the physical and virtual infrastructure necessary to power the next generation of AI advancements.

What is Stargate?

Stargate is designed to create a robust AI infrastructure across the United States, including the construction of 20 colossal data centers, each half-a-million square feet in size. These data centers will provide the computing power required for AI technologies, enabling tech companies to link thousands of chips together in clusters. The project is expected to generate hundreds of thousands of American jobs and deliver massive economic benefits globally.

Key Players and Investments

The Stargate Project is backed by an initial investment of $100 billion, with a total of $500 billion planned over the next four years. Key players include Softbank's Masayoshi Son, Oracle's Larry Ellison, and OpenAI's Sam Altman. The collaboration between these tech giants underscores the significance of this initiative in maintaining the U.S. as a global leader in AI.

Challenges and Controversies

While the Stargate Project promises significant advancements in AI, it has not been without controversy. Critics, including Elon Musk, have questioned the project's funding and feasibility. Musk has claimed that the project lacks the necessary financial backing, a claim refuted by those involved in Stargate. Additionally, the removal of AI safety legislation by Trump has raised concerns about the potential risks and ethical implications of rapid AI development.

The Future of AI

The Stargate AI Project represents a significant step towards the future of artificial intelligence. By investing in the infrastructure needed to support AI technologies, the U.S. aims to stay ahead in the global AI race. However, it is crucial to address the ethical and societal challenges that come with such rapid advancements to ensure that AI benefits all of humanity.

In conclusion, the Stargate AI Project is a high-stakes gamble with the potential to reshape the future of AI. As construction begins and investments pour in, the world will be watching closely to see how this ambitious initiative unfolds.

For more details, you can read the full article [here](https://www.msn.com/en-au/technology/artificial-intelligence/the-key-players-behind-stargate-donald-trump-s-500-billion-artificial-intelligence-project/ar-AA1xDx4p).

The Cost of Top AI Tools for Data Analysts in 2025

In the ever-evolving world of data analytics, AI tools have become indispensable for extracting valuable insights from vast datasets. As we step into 2025, let's take a closer look at the pricing of some of the best AI tools for data analysts.

1. Tableau

Tableau is renowned for its powerful data visualization capabilities. It offers three pricing plans:

- Creator: $70 per user per month

- Explorer: $42 per user per month

- Viewer: $15 per user per month

Tableau also provides a free trial version for users to explore its features.

2. Microsoft Power BI

Microsoft Power BI is a versatile tool that integrates seamlessly with other Microsoft products. Its pricing is as follows:

- Power BI Pro: $9.99 per user per month

- Power BI Premium: $20 per user per month

Power BI also offers a free version with limited features.

3. HubSpot's AI Content Writer

HubSpot's AI Content Writer is designed to assist with content creation and marketing. The pricing details are:

- Starter: $50 per month

- Professional: $800 per month

- Enterprise: $3,200 per month

HubSpot provides a free version with basic features.

4. MonkeyLearn

MonkeyLearn is an AI tool for text analysis and data extraction. Its pricing plans include:

- Starter: $299 per month

- Team: $499 per month

- Business: Custom pricing based on requirements

MonkeyLearn also offers a free plan with limited features.

5. Akkio

Akkio is an AI-powered data flow tool that simplifies data analysis. The pricing is as follows:

- Starter: $50 per month

- Professional: $500 per month

- Enterprise: Custom pricing based on needs

Akkio provides a free trial for new users.

These AI tools offer a range of pricing options to suit different needs and budgets. Whether you're a solo data analyst or part of a large enterprise, there's an AI tool out there to help you unlock the full potential of your data.

Copilot generated blog inspired by: https://techpoint.africa/2025/01/26/5-best-ai-tools-for-data-analysts/

How Australia's Biggest Companies Are Embracing Generative AI

In recent years, artificial intelligence (AI) has become a cornerstone of innovation for many industries. Australia's largest companies are no exception, as revealed in their latest annual reports. From banking to healthcare, these corporations are leveraging generative AI to enhance efficiency, improve customer experiences, and drive productivity.

Key Use Cases Across Industries

1. Banking: Major banks like Commbank are using AI to streamline operations and enhance cybersecurity. AI-driven tools are helping to detect fraudulent activities and provide personalized customer service.

2. Healthcare: Companies such as Pro Medicus and Sonic Healthcare are at the forefront of using AI for medical imaging and diagnostics. These advancements are not only improving patient outcomes but also reducing costs.

3. Retail: Retail giants like Woolworths are deploying AI to optimize supply chains and improve customer interactions. AI-powered chatbots and recommendation systems are becoming integral parts of the shopping experience.

4. Mining and Energy: BHP and other mining companies are utilizing AI to predict equipment failures and optimize resource extraction. In the energy sector, AI is being used to manage energy consumption and reduce environmental impact.

Challenges and Ethical Considerations

While the benefits of AI are clear, there are also significant challenges. Companies are grappling with issues related to data privacy, ethical AI usage, and the need for continuous staff training. Ensuring that AI systems are transparent and fair is a top priority for many organizations.

The Future of AI in Australia

As generative AI continues to evolve, its applications will only expand. Australian companies are poised to be leaders in this space, driving innovation and setting new standards for AI integration. The focus will remain on harnessing AI to create value while addressing the ethical and operational challenges that come with it.

Blog generated by Copilot based on this article: https://www.mi-3.com.au/15-09-2024/annual-reports-reveal-how-australias-biggest-companies-are-deploying-generative-ai

The Future of AI: Key Statistics and Trends to Watch in 2025

Artificial intelligence (AI) continues to revolutionize industries and reshape the way we live and work. Here are some key statistics and trends that highlight the growing impact of AI in 2025:

1. AI Market Growth

The AI market is projected to reach a staggering $1,339 billion by 2030, experiencing substantial growth from its estimated $214 billion revenue in 2024. This rapid expansion underscores the increasing adoption of AI technologies across various sectors.

2. Economic Impact

AI is expected to contribute a significant 21% net increase to the United States GDP by 2030. This highlights the transformative potential of AI in driving economic growth and enhancing productivity.

3. Consumer Concerns

Over 75% of consumers are worried about the potential for AI to perpetuate the spread of misinformation. As AI becomes more integrated into our daily lives, addressing these concerns will be crucial to building trust and ensuring responsible use of AI technologies.

4. Adoption Rates

AI-powered voice search is on the rise, with 50% of U.S. mobile users using it daily. This trend showcases the growing prevalence of AI-powered voice assistants in everyday life, making interactions with technology more seamless and intuitive.

5. Self-Driving Cars

It is expected that 10% of vehicles will be driverless by 2030. The global market for self-driving cars is forecasted to increase from 20.3 million in 2021 to 62.4 million, reflecting the advancements in autonomous vehicle technology.

6. Business Productivity

A significant 64% of businesses believe that artificial intelligence will help increase their overall productivity. This demonstrates the growing confidence in AI’s potential to transform business operations and drive efficiency.

Conclusion

The future of AI is bright, with its market size, economic impact, and adoption rates all set to soar in the coming years. However, it is essential to address consumer concerns and ensure responsible use of AI technologies to build trust and maximize the benefits of this transformative technology. As we look ahead to 2025 and beyond, staying informed about these key statistics and trends will help us navigate the evolving landscape of AI and harness its full potential.

Copilot generated blog post inspired by Forbes

Top hard skills to earn in 2025

As we step into 2025, the job market continues to evolve at a rapid pace. To stay ahead of the curve, it's essential to equip yourself with the right skills. Here are the top five hard skills that will be invaluable in the coming year:

1. Artificial Intelligence and Machine Learning

AI and ML are transforming industries across the board. From healthcare to finance, the ability to develop and implement AI solutions is becoming increasingly crucial. Learning how to work with AI algorithms, neural networks, and data analysis will give you a competitive edge.

2. Cybersecurity

With the rise in cyber threats, cybersecurity has become a top priority for organizations. Understanding how to protect sensitive data, identify vulnerabilities, and respond to security breaches is a skill set that will always be in demand.

3. Data Science and Analytics

Data is the new gold, and the ability to extract meaningful insights from vast amounts of data is a highly sought-after skill. Proficiency in data analysis tools, statistical methods, and data visualization will open doors to numerous opportunities.

4. Cloud Computing

As more businesses move their operations to the cloud, expertise in cloud platforms like AWS, Azure, and Google Cloud is essential. Understanding cloud architecture, deployment, and management will make you an invaluable asset to any organization.

5. Blockchain Technology

Blockchain is not just about cryptocurrencies; it's a revolutionary technology with applications in various sectors, including supply chain, finance, and healthcare. Learning how to develop and implement blockchain solutions will position you at the forefront of innovation.

By mastering these hard skills, you'll be well-prepared to navigate the ever-changing job market and seize new opportunities. Start investing in your future today by exploring courses, certifications, and hands-on projects in these areas.

Copilot generated blog inspired by Forbes

Expert Insights for 2025 Data Analytics and AI Initiatives

As we move into 2025, the landscape of data, analytics, and AI is evolving rapidly. Organizations are facing new challenges that are testing their data strategies, AI readiness, and overall trust in data. At the recent Trust ’24 Data Integrity Summit, leading data experts gathered to offer their insights into these shifts and what they mean for your business. Here are the key takeaways from their discussion.

The Importance of Data Integrity

Data integrity is crucial for AI initiatives and better decision-making, but trust in data is on the decline. According to the 2025 Outlook: Data Integrity Trends and Insights report, 67% of respondents say they don’t completely trust their organizations’ data for decision-making, up from 55% last year. This decline in data confidence is influenced by several factors:

- Increasing Data Ecosystem Complexity: With the surge of new tools, platforms, and data types, managing these systems effectively is an ongoing challenge.

- Viewing Data as a Product: Organizations are trying to serve data in their marketplace and make it accessible to business and data consumers, but this requires a strong data foundation.

- Cloud Modernization: The rise of cloud-based data management presents challenges in accessing both on-premises and cloud-based data.

AI Demands Data Integrity

AI initiatives depend on a robust data strategy. Successful AI projects require high-quality data, strong data governance, and overall data integrity. However, only 12% of organizations report that their data is ready for AI initiatives, despite 60% saying AI is a key influence for their data programs. This gap underscores the urgent need for better data foundations.

Strategic Steps for AI Readiness

To prepare for AI initiatives, organizations need to focus on upskilling their teams and building a strong data strategy. Here are three critical steps to get AI-ready:

1. Identify Critical Datasets: Ensure that all relevant datasets used for AI training and inference are accounted for to avoid introducing bias into models.

2. Plan for Data Quality and Governance: Poor-quality data can lead to faulty AI models, resulting in inaccurate outcomes. Governance will be critical as AI regulations continue to evolve.

3. Leverage Enriched Data: Enriching internal data with trusted third-party data can enhance accuracy and provide additional context for AI models.

Data Quality and Governance

Data quality and governance are the top data integrity challenges and priorities. A holistic approach to data management is key, and data governance is the place to start. Here are some best practices:

- Start Small with Clear Business Use Cases: Demonstrate ROI to internal stakeholders before expanding to multiple sources and adding more capabilities.

- Take a Proactive Approach: Ensure that trusted data is served in a timely fashion with real-time data observability and automated health checks.

- Leverage AI to Enhance Governance: Use large language models to dynamically generate data validation rules rather than relying on static, manually set rules.

- Focus on Metadata Management: Metadata is critical for driving insights in AI and advanced analytics.

The Untapped Potential of Location Data

Location-based data is becoming increasingly valuable. Spatial analytics has risen as a data integrity priority, with organizations recognizing the untapped potential of spatial insights. Location data is particularly valuable in industries like retail, telecommunications, and insurance, where it’s used for hyper-accurate decision-making.

Final Takeaways for 2025 and Beyond

As we look ahead to 2025, data-driven decision-making remains a top goal for data programs. To achieve this, organizations must focus on:

- Building a Strong Foundation of Data Integrity: Start with your business case and take a long-term, disciplined approach to your data strategy.

- Upskilling Teams: Prepare your people to handle both data management and AI technologies.

- Embracing Enriched Data: Unlock new business opportunities by leveraging spatial insights and other enriched data.

By focusing on these key trends and recommendations, organizations can stay ahead in the rapidly evolving data and AI landscape.

Copilot generated blog inspired by precisely.

PwC’s Global Artificial Intelligence Study: Sizing the prize

Artificial Intelligence (AI) is poised to be a game-changer for the global economy, with the potential to contribute a staggering $15.7 trillion by 2030. PwC's Global Artificial Intelligence Study delves into the transformative power of AI and its far-reaching impact on various sectors.

The study highlights that AI's economic benefits will stem from two primary sources: increased productivity and enhanced consumer demand. By augmenting labor productivity and automating tasks, AI technologies are expected to drive significant GDP gains. Additionally, AI will stimulate consumer demand through product enhancements, offering greater variety, personalization, and affordability.

China and North America are projected to reap the most substantial economic gains from AI, with a 26% and 14.5% boost to GDP, respectively. This underscores the importance of strategic investment in AI technologies to harness their full potential.

PwC's AI Impact Index identifies nearly 300 AI use cases, evaluating their potential to enhance personalization, quality, consistency, and time savings for consumers. The study emphasizes that while some markets and sectors are more advanced in AI adoption, there are ample opportunities for emerging markets to leapfrog their developed counterparts.

In conclusion, AI is set to revolutionize the global economy, offering unprecedented opportunities for innovation and growth. Businesses and policymakers must prioritize AI investments to capitalize on this transformative technology and secure a competitive edge in the future.

Copilot generated blog inspired by PwC: https://www.pwc.com/gx/en/issues/artificial-intelligence/publications/artificial-intelligence-study.html)

Using AI in economic development: Challenges and opportunities

Artificial Intelligence (AI) is revolutionizing economic development, offering both challenges and opportunities for public sector leaders. McKinsey's article delves into how AI can transform various sectors and functions, highlighting five key areas where AI technologies can make a significant impact.

1. Globally Competitive Value Chains: Economic development leaders can leverage AI to perform real-time market analysis, identify emerging economic trends, and detect growth opportunities. For instance, an East Asian city used analytics models to boost its GDP per capita by $8,500 in six years by focusing on untapped areas of economic competitiveness.

2. Investment Attraction and Trade Support Programs: AI can help leaders attract foreign investment and increase exports by selecting companies that may boost local economic development. The US-based organization REDI Cincinnati uses predictive analytics to identify growing companies likely to make future investments, facilitating over $6 billion in capital investments.

3. Future of Work Programs: Labor agencies can use AI to identify long-term shifts in the job market and enable critical transitions. The UK's Department for Work and Pensions uses workforce analytics to estimate demand for workers in various occupations, helping job seekers navigate an evolving labor landscape.

4. Economic 'Nowcasting' and Forecasting: AI technologies allow ministries of finance and economy to detect early signs of economic turmoil, enabling faster course corrections. The OECD nowcasts weekly GDP growth using machine learning to identify correlations between search terms and changes in GDP components.

5. Transforming Public Services with Geographic Information Systems and Spatial Data: Government organizations are using spatial data and satellite imagery to enhance public service delivery and disaster responsiveness. Kazakhstan's approach to developing infrastructure in rural settlements is a prime example, where a statistical model analyzed communities to efficiently provide essential services.

However, there are challenges to AI adoption in economic development, including securing the right data, attracting talent, and gaining public trust. Data quality and availability can vary significantly, and public sector organizations often struggle to attract tech-savvy employees. Additionally, transparency and accountability in AI decision-making are crucial to building trust among stakeholders.

To overcome these challenges, public sector entities can adopt strategies such as using AI to cut through complexity, ensuring seamless access to data, and pursuing partnerships and mentorship programs to build expertise. Establishing centers of excellence and offering a strong value proposition to attract and retain diverse talent are also essential steps.

In conclusion, AI presents immense opportunities for economic development, but it requires thoughtful implementation and collaboration to realize its full potential. By addressing the challenges and leveraging AI's capabilities, public sector leaders can drive sustainable and inclusive growth.

Copilot generated blog inspired by McKinsey's article on AI in economic development: (https://www.mckinsey.com/industries/public-sector/our-insights/using-ai-in-economic-development-challenges-and-opportunities)