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Forbes
30-07-2025
- Business
- Forbes
How To Deal With Four Common Traps That Sabotage AI Success
Stoyan Mitov is the CEO of Dreamix, a custom software development company helping tech leaders increase capacity without giving up quality. As AI becomes increasingly central to business operations in 2025, I've witnessed a troubling pattern: Despite massive investments, 74% of companies still struggle to achieve meaningful value from their AI initiatives, according to BCG research. After dozens of AI implementations at our software development company, I've identified the most common misconceptions that consistently derail projects, often before organizations even realize they're off track. Trap 1: 'AI Will Fix Everything' I often encounter the belief that AI is a silver bullet for organizational problems. Last year, we worked with a manufacturing client who insisted AI would solve their inventory management issues. Three months in, we discovered the real problem wasn't forecasting—it was inconsistent data entry by warehouse staff. This reflects a broader misconception that AI can compensate for broken processes or poor data hygiene. In my experience, AI tends to amplify what's already there, both strengths and weaknesses. If your manual processes are chaotic, AI will make them chaotically automated. The reality check: Before implementing any AI solution, we now work with clients to understand their current processes thoroughly. If you wouldn't trust a new employee to succeed with your existing workflow, AI may face similar challenges in delivering meaningful results. Trap 2: 'More Data Is Always Better' Another costly mistake I see repeatedly is the assumption that AI requires massive datasets to be effective. Organizations often spend months trying to compile comprehensive historical data, believing that more information will automatically lead to better AI performance. In reality, the quality and relevance of data matter far more than quantity. Poor data quality costs organizations an average of $12.9 million annually, and it is estimated that 80% of the effort in machine learning projects is spent on ensuring data quality. Clean, consistent data from a shorter period often outperforms larger datasets with quality issues. I've seen companies achieve meaningful results with focused, high-quality datasets while others struggle despite having years of poorly structured information. The trap here is perfectionism. Organizations delay AI implementation while pursuing data perfection that may never come and often isn't necessary. This can postpone valuable initiatives indefinitely, while competitors move forward with smaller but sufficient datasets that still deliver business value. Trap 3: 'Technical Excellence Guarantees Adoption' Many of the most challenging setbacks I've witnessed stem from treating AI implementation as purely a technical challenge. Organizations often focus entirely on building sophisticated systems while completely overlooking the human side of adoption. What's often missing is that the company culture determines whether AI initiatives succeed or fail. According to EY's research, 50% of senior business leaders report declining company-wide enthusiasm for AI integration, while 54% feel they are failing as leaders amid AI's rapid growth. I've learned to spot the warning signs early: If business stakeholders aren't actively involved in defining success metrics, if end users aren't part of the design process or if leadership talks about AI as something "the IT department is handling," the project is likely headed for trouble. Without buy-in from the people who will actually use the system, even the most technically advanced AI solution can become an expensive digital paperweight. Trap 4: The 'Quick Win' Pressure Cooker Executives often demand immediate results from AI initiatives, creating a dangerous cycle of overpromising and underdelivering. This impatience can be particularly destructive because many AI benefits compound over time, yet leadership may lose confidence and cut funding just as systems are beginning to learn and improve. This rush for immediate returns reflects a broader misunderstanding of how AI value creation works. BCG research shows that AI leaders pursue fewer opportunities than their peers but expect more than twice the ROI by focusing on the most promising initiatives. They understand that sustainable AI success requires patience and commitment rather than scattering resources across multiple quick-win attempts. The most successful organizations resist the pressure to show immediate results across every initiative, instead concentrating their efforts on fewer, more strategic opportunities that can deliver meaningful long-term value. The Self-Assessment That Could Save Your AI Investment Based on these patterns, I've developed a simple diagnostic that reveals whether your organization is walking into these traps: • Can you name exactly which business problem your AI initiative will solve and how you'll measure success? • Are the people who will use the AI system daily involved in its design? • Do you have at least one dataset that your team trusts completely for business decisions? • Is your timeline realistic enough to allow for learning and iteration? • Are you focusing on a few high-impact opportunities rather than experimenting everywhere? If you answered "no" to any of these questions, you're likely setting up your AI initiative for the same struggles that plague 74% of companies. Moving Forward The organizations that achieve 1.5 times higher revenue growth from AI—as BCG research demonstrates—aren't necessarily more technically sophisticated. They're more honest about their limitations and more disciplined about avoiding these common traps. Start small, measure relentlessly and remember that successful AI implementation is as much about changing how people work as it is about changing what technology can do. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Geeky Gadgets
30-07-2025
- Business
- Geeky Gadgets
Easily Build an Excel Multi-Step Data Entry Form for Error-Free Data Entry
Have you ever been overwhelmed by messy spreadsheets or struggled to collect data in an organized way? Imagine a scenario where instead of juggling multiple tabs and rows, you could guide users through a seamless, step-by-step process to input data directly into Excel. A multi-step data entry form can transform how you manage information, offering a structured and user-friendly approach that eliminates errors and boosts efficiency. With Excel's built-in tools and the power of VBA (Visual Basic for Applications), creating such a form isn't just possible—it's surprisingly accessible. Whether you're tracking customer details, survey responses, or project updates, a well-designed form can be a fantastic option for your workflow. In this guide Kenji explains how to build your own multi-step data entry form from scratch, using Excel's capabilities to create an interactive and dynamic tool. You'll learn how to prepare your workbook, design a visually appealing form, and use VBA to add advanced functionality like navigation buttons and data validation. Along the way, we'll explore tips for testing, debugging, and securing your form to ensure it works flawlessly. By the end, you'll have the knowledge to design a form that doesn't just collect data—it simplifies and improves the entire process. After all, the best tools are the ones that make your work feel effortless. Create Multi-Step Excel Form 1: Preparing Your Excel Workbook The first step in creating a multi-step data entry form is to set up your Excel workbook to store the collected data. Proper preparation ensures a smooth workflow and organized data storage. Follow these steps: Create an Excel table with clearly labeled columns that correspond to the fields in your form. For example, if your form collects names, email addresses, and phone numbers , your table should include columns for each of these fields. , your table should include columns for each of these fields. Add a 'New Entry' button to your worksheet. This button will serve as the starting point for users to access the form and begin entering data. This setup provides the foundation for your data entry process, making sure that all collected information is stored in a structured and accessible format. 2: Designing a User-Friendly Form A well-designed form is essential for creating a seamless user experience. While Excel offers basic tools for form creation, using PowerPoint for the initial design can provide greater flexibility and visual appeal. Here's how to design your form: Use PowerPoint to create a multi-step form, dividing it into sections for each step. For instance, Step 1 could focus on collecting personal details , while Step 2 gathers contact information . , while Step 2 gathers . After completing the design, import it into Excel. Adjust the layout to ensure that all fields are easy to read and interact with. Add navigation buttons, such as 'Next' and 'Previous,' to allow users to move between steps effortlessly. These buttons improve usability and guide users through the form. This approach ensures that your form is both visually appealing and functional, making it easier for users to complete the data entry process. How to Create an Excel Multi-Step Data Entry Form Watch this video on YouTube. Here is a selection of other guides from our extensive library of content you may find of interest on Excel functions. 3: Implementing VBA for Advanced Functionality To add interactivity and advanced features to your form, you'll need to integrate VBA into your Excel workbook. VBA allows you to create a dynamic form that can handle multiple steps and automate data management. Follow these steps to implement VBA: Enable the Developer tab in Excel to access the Visual Basic Editor (VBE). This is where you'll write the code for your form. In the VBE, create a user form with multiple pages to represent the steps of your data entry process. Each page should correspond to a specific section of the form. Import your PowerPoint design into the user form. Add input fields such as text boxes, dropdown menus, and checkboxes to collect data. to collect data. Logically name each element in the form to simplify coding and future maintenance. For example, name a text box for email input as 'txtEmail' for clarity. Integrating VBA enables you to create a fully interactive form that is tailored to your specific data collection needs. 4: Adding Dynamic Features Dynamic functionality is crucial for making sure a smooth and intuitive user experience. Use VBA to handle navigation, data validation, and storage. Here's how to add these features: Write VBA code to validate user inputs at each step. For instance, when a user clicks 'Next,' the form should check that all required fields are filled before allowing them to proceed. Program the form to save data to the next available row in the Excel table. This ensures that new entries do not overwrite existing data. Include error messages or prompts to guide users if they miss required fields or enter invalid data. These dynamic features enhance the reliability and usability of your form, making it easier for users to complete the data entry process accurately. 5: Testing and Debugging Before deploying your form, it's essential to thoroughly test its functionality to ensure it works as intended. Testing helps identify and resolve any issues that could disrupt the user experience. Follow these steps: Test all navigation buttons to confirm they work correctly and allow users to move between steps without errors. Verify that data entered into the form is saved to the correct rows in the Excel table. Check for issues such as data overwriting or incorrect placement . . Use VBA's debugging tools to identify and fix errors in your code. Pay close attention to potential issues like navigation glitches or validation errors. Thorough testing ensures that your form is reliable and performs well under various conditions. 6: Assigning Macros to the Form To make your form easily accessible, you'll need to link it to the 'New Entry' button you created earlier. This step simplifies the process for users and ensures the form is readily available. Here's how to assign macros: Write a macro that opens the form when the 'New Entry' button is clicked. This macro serves as the bridge between the button and the form. Assign the macro to the button, allowing users to launch the form with a single click. This eliminates the need for users to navigate through menus or manually open the form. By linking the form to a button, you streamline the user experience and make the data entry process more efficient. 7: Saving and Sharing the Workbook To preserve the VBA code and macros, save your workbook in a macro-enabled format (.xlsm). Inform users that they must enable macros when opening the file to use the form. This ensures that all features of the form remain functional and secure. Additionally, consider protecting the workbook with a password to prevent unauthorized access or modifications. By following these steps, you can create a robust multi-step data entry form in Excel that simplifies complex tasks, enhances data accuracy, and improves productivity. Media Credit: Kenji Explains Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. 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Forbes
04-07-2025
- Business
- Forbes
10 Careers AI Will Replace In The Next 5 Years
Experts reveal which jobs AI will and won't replace by 2030 and how you can pivot if your job is on ... More the chopping block. CEOs are already adjusting their hiring strategies as McKinsey projects that 30% of U.S. work hours will be automated by 2030. What skills do you lack? And what skills do you have that will sustain your career until 2030? There are 10 careers AI will replace, along with four skills AI won't replace. But if you find your job on the chopping block, learn how you can pivot and make yourself indispensable in the next five years. 10 Careers AI Will Replace By 2030 Statistics show that AI will replace 300 million jobs, and 41% of companies worldwide plan to reduce their workforce by 2030. The McKinsey Report reveals that acceleration of generative AI is expected to automate an additional eight percent of American workers' work hours across all economic sectors, increasing the overall percentage of automated work hours to around 30%. LiveCareerUK released its Jobs AI Will Replace Report, outlining the 10 careers AI is most likely to replace by 2030. If your job is on the list of those disappearing, the experts suggest a direction for you to pivot 1. Data Entry Clerks. How to pivot: "Re-skill in data analysis or data management Learn Excel, SQL, or Python to shift into roles that interpret and act on data, not just record it." 2. Telemarketers. How to pivot: "Re-skill in digital marketing or customer success Build skills in CRM tools, social media engagement and sales strategy to stay valuable in a human-centered sales role." 3. Basic Customer Service Representatives. How to pivot: "Re-skill in technical support or customer success. Focus on more complex problem-solving roles that require empathy, expertise, and relationship-building.' 4. Retail Cashiers. How to pivot: 'Re-skill in retail management or supply chain operations. Move into areas that require strategic thinking, leadership, or technical know-how in the retail ecosystem.' 5. Proofreaders and Copy Editors. How to pivot: 'Re-skill in content strategy or digital marketing. Leverage your writing instincts in higher-order tasks like brand storytelling, SEO and campaign planning.' 6. Paralegals and Legal Assistants. How to pivot: 'Re-skill in legal tech, compliance or litigation support. Apply your legal knowledge in tech-forward fields that blend law with AI and automation tools.' 7. Bookkeepers. How to pivot: "Re-skill in financial analysis or advisory roles Move beyond basic number-crunching to deliver strategic insights that businesses can act on.'" 8. Fast Food and Restaurant Front-line Workers. How to Pivot: 'Re-skill in culinary innovation or restaurant management. Creativity, leadership and operations knowledge will always be in demand, even if robots flip the burgers.' 9. Warehouse Workers. How to pivot: 'Re-skill in logistics coordination or warehouse technology roles. Learn to operate, oversee, or improve the systems that are replacing repetitive labor.' 10. Entry-Level Market Research Analysts. How to pivot: 'Re-skill in business analytics or data storytelling. Go beyond data collection by learning to turn insights into decisions with tools like Tableau, Power BI or Python.' A Final Takeaway On 10 Careers AI Will Replace As AI reshapes the workforce, there are smart ways to future-proof your career if it's threatened. One consolation is that the most in-demand skills will be human skills that AI simply can't replicate, not technical ones. SHL's chief science officer, Sara Gutierrez, believes skills that are harder to automate such as strategic thinking, creativity, acting ethically and the ability to deliver human-tech collaboration, matter most. 'We're seeing a sharp rise in demand for the ability to critically evaluate and analyze information, generate new ideas and to develop innovative approaches to problems, Gutierrez says. "These are the capabilities that underpin effective use of AI tools and distinguish those who can leverage technology from those who are simply exposed to it. Employees who can effectively use generative AI tools, interpret data outputs and integrate those insights into their workflows are quickly becoming indispensable.' Gutierrez explains that candidates can better demonstrate these skills by going beyond listing skills and showing them in the context of real work. 'That might mean highlighting projects where they had used AI tools to solve business problems or sharing how they adapted during an organizational change,' She points out. "Creating content (e.g., writing, posting, presenting) about how they're learning and applying emerging tools can be especially compelling.' Jon Hinkle, CEO of TRG Datacenters identifies four skills employers will prize most in the coming years that AI can't replace. 1. Conflict Resolution: AI Can't Build Trust. 'Disagreements happen everywhere—between teams, departments, even with clients,' Hinkle says. 'We look for people who know how to de-escalate situations, stay calm under pressure, and find common ground. That soft skill prevents small issues from turning into costly ones." 2. Adaptability: Learning Is the New Experience. 'The difference is how quickly they learn and adapt when something changes—which is almost weekly now.' 3. Leadership: AI Can't Inspire People. Hinkle explains that even with the best tools, people still want to feel connected to a mission. 'A good leader can explain the why, give clear direction, and motivate others when things are uncertain or chaotic. That kind of leadership builds loyalty and performance." 4. Systems Thinking: Connecting the Dots Is a Human Skill. 'Plenty of tools can run an analysis, but very few people can look at the output and say, 'Here's how this impacts our marketing, support, and ops all at once,'' Hinkle insists. 'That big-picture view is what helps us make decisions that actually work." Atalia Horenshtien, head of AI practice at Customertimes, told me by email that if you're concerned that AI will take your job, focus on what AI can't do . . .yet. 'AI will likely replace tasks, not whole jobs, especially those rooted in repetition,' according to Horenshtien. 'What it still can't replicate well: original thinking, emotional intelligence, ethical judgment and complex decision-making. If your role leans heavily on these, double down. If not, it's time to pivot.' To outsmart AI threats to your career, identify the job skills AI will and won't replace. If you find your job to be one of the 10 careers AI will replace, start thinking now how you can pivot so by 2030 you're still indispensable in a job that machines can't mimic.
