Kenza Kbabra, MBA’s Post

AI is making an impact, but doesn't have to stress your organization out. We talk about responsible use of AI and with a plan, the appropriate guardrails, and training, it is a core driver to achieve a company's ability to impact their industry positively!!! Diane Gutiw's article is a great article you should read!

Businesses need to put forth the effort to understand AI and identify the appropriate AI models to use before adoption. Like any tool, the adequate use of AI can deliver significant returns on investment, but the incorrect use has the potential of risk and loss. https://splr.io/6046mvBrE

Can AI make an organization more effective and functional? An organization’s structure and its governing rules—commonly referred to as its bureaucracy—often impact its internal efficiency and effectiveness. Many organizational issues stem from conflicting or inefficient rules and structures. Currently, evaluating whether this bureaucracy is functional and effective relies on subjective assessments made by individuals within the organizational hierarchy. There is no clear, systematic approach to measure the health of an organization’s bureaucracy. GPT models and similar AI systems have the capacity to analyze and summarize large volumes of data. By combining this capability with data from various organizational tools and services, AI could potentially assess an organization’s overall effectiveness. In the near future, I envision organizations computing a "Bureaucracy Index" (B-Index) based on diverse data sources. This index could help organizations continuously refine their hierarchy and rules, creating a more adaptable, efficient structure. The B-Index will enable organizations to make more informed and confident adjustments to their structure and rules. Feel free to share your thoughts in the comments.

👨💻 Take a look into the future of technology with "Harnessing AI for Business Growth and Innovation," a compelling article by our CTO, Maksim Belov. Learn how AI is not just a buzzword but a transformative tool that can redefine how we approach business challenges and opportunities. 📊    🔗 https://lnkd.in/dHVRyW4v #ArtificialIntelligence #Innovation #BusinessStrategy #CoherentSolutions

The next level of AI is agentic and Salesforce is shaping a new way of working. Read Silvio Savarese great article in Forbes

#McKinsey research estimates that #generativeAI could add the equivalent of $2.6 trillion to $4.4 trillion of value #annually. With continued #advancement in Generative AI, the key decision makers in companies are generally the CIOs and CTOs - to provide access to third party tools for employees mainly to improve #productivity and #efficiency. While reaching out to several technology #leaders and analyzing generative AI initiatives at over 50 companies (including their own McKinsey), this leading #consulting firm has identified 9 actions that the technology leaders can take to create value, orchestrate technology and data, scale solutions, and manage risk for generative AI. The top one that resonates well with me is identifying the #usecases. Read the full guide to learn about the remaining suggestions to help you achieve high ROI while NOT compromising data #security. #customerexperience #artificialintelligence #research

GenAI vs Traditional AI : Did you know that while 91% of executives believe in the potential of AI, many still struggle to differentiate between GenAI and traditional AI for business needs? This statistic, though hypothetical, highlights the challenge leaders face today: choosing the right AI approach for their organization. Two approaches that complement each other—and when to use each effectively—are Traditional AI and GenAI Traditional AI - Often referred to as analytical AI, it is the foundation of data-driven decision-making: ⭐ Data Dependency: Relies on structured data, making it ideal for solving well-defined problems. ⭐ Predictive & Prescriptive: Models use historical data to provide actionable insights. ⭐ Expert-Led: Requires data scientists and statistical experts to develop and maintain solutions. ⭐ Applications: Traditional AI excels in forecasting, operational efficiency, and solving predictable business problems. GenAI - Creative and accessible, GenAI is a game-changer, unlocking new possibilities for organizations: ⭐ Multi-Modal Capability: Works with text, images, video, and voice data, enabling richer insights. ⭐ Content Creation: Generates new and unique outputs, such as personalized customer responses or marketing visuals. ⭐ Democratized AI: User-friendly tools allow even non-technical executives to engage with AI. ⭐ Advanced Use: While accessible, customizing GenAI models still requires skilled teams. ⭐ Applications: GenAI is perfect for creative industries, customer personalization, and unstructured data challenges. Few Suggestions : ⭐ Don’t Use GenAI Just Because It’s Trending: Start with the business need. ⭐ Weigh Costs vs Benefits: GenAI is resource-intensive—use it for clear business value. ⭐ Consider Risks: Traditional AI operates within internal controls, while GenAI leverages large public datasets, raising compliance and ethical concerns. As the article points out, traditional AI or simple automation often solves problems effectively without over-engineering a solution. GenAI, while transformative, should be reserved for high-impact, strategic use cases where its unique capabilities align with business goals. (This post reflects my thoughts, structured and refined with the help of AI-powered tools, showing how AI enhances clarity. The "91%" is a conceptual statistic used to illustrate the growing reliance on AI.) #GenerativeAI #TraditionalAI #AIInnovation #BusinessStrategy #DataScience #DigitalTransformation #EthicalAI #FutureOfWork

3 Reasons Why Your Business Isn't Ready for AI (Yet) Artificial intelligence has become the buzzword du jour, with businesses scrambling to incorporate it into their operations. But the truth is, most aren't ready. Before you dive headfirst into AI, ask yourself these 3 crucial questions: 1. Do you truly understand your business challenges? Too often, companies jump to AI as a solution without first deeply analyzing the problem. AI is not a magic wand - it requires a solid foundation of human expertise and well-defined business objectives. 2. Do you have the right data infrastructure in place? Effective AI relies on high-quality, centralized data. If your data is siloed, incomplete, or of poor quality, your AI initiatives are doomed to fail. 3. Do you have the right talent on your team? Deploying AI successfully requires a diverse skill set - from data scientists to change management experts. Many businesses lack the in-house expertise to make AI work. So what's the solution? Start by focusing on the "human" side of "human + AI." Deeply understand your challenges, get your data house in order, and build the right team. Only then should you look to AI to amplify your efforts. The magic doesn't happen with AI alone. It happens when you pair cutting-edge technology with deep human intelligence. Are you ready? #Beawai #ArtificialIntelligence #HumanPlusAI

Is Your Business Ready For AI? Here's How To Tell

Ever trusted AI, only to find out it made something up completely? It’s surprising, even unsettling, when AI systems confidently deliver information that’s entirely incorrect. This phenomenon isn’t just a glitch. It’s a behavior known as AI hallucination. What is AI hallucination? AI hallucination occurs when an AI model generates false or misleading outputs while presenting them as accurate. Why does this happen? ⦿ Data limitations:  ↳ The AI lacks enough context or is trained on incomplete or biased data. ⦿ Overgeneralization:  ↳ The model tries to predict something it doesn’t truly understand. ⦿ Ambiguity:  ↳ When faced with vague or contradictory information, AI might "fill in the blanks." ⦿ Complexity:  ↳ Errors arise in highly nuanced or domain-specific topics the model isn’t trained for. What are its effects? • Misinformation → Inaccurate data misleads users and hurts credibility. • Misinterpretation → False outputs confuse decision-making. • Resource wastage → Fixing AI errors wastes time and effort. • Trust erosion → Frequent errors reduce confidence in AI. What are the solutions? • Better data → Use diverse, high-quality datasets. • Human oversight → Experts verify critical outputs. • Improved models → Enhance algorithms for ambiguity. • Context awareness → Broaden AI's knowledge scope. • Clear disclaimers → Warn users of uncertainties. AI hallucinations remind us that while AI is powerful, it isn’t infallible. But with the right strategies, we can minimize these errors and build systems that are more reliable and trustworthy. P.S. How do you think we can improve AI systems to handle hallucinations better? Let me know your thoughts! #AI #AIHallucination #Data #Model #DeepLearning