How to Prepare Your Business for Emerging Technologies

Final Post on Intelligent Automation Week - (3 of 3) TL;DR: Did you know that 87% of companies believe AI will be a key technology for their business in the next three years? I recently had the privilege of discussing AI integration at the SSON conference, and a common question that emerged was: "How can we integrate AI into our existing technologies seamlessly?" To answer this, I've distilled my key takeaways into actionable tips: 1. Assess the Need: Not every business requires AI. Begin by questioning, researching, and discussing whether it aligns with your goals. If the potential impact is substantial, proceed to the next steps. 2. Understand AI's Impact: AI isn't just about robots; it's about data-driven insights and cognitive processing. Consider these three facets: * Analytics Automation: AI processes vast data volumes swiftly, empowering strategic decisions. * Cognitive Mimicry: Think Natural Language Processing (NLP) and computer vision—AI emulates human thought processes. * Process Automation: From manufacturing to customer service, AI streamlines operations, minimizing errors. 3. Data Source Evaluation: Identify and evaluate data sources—both internal (customer data, sales records) and external (social media, market trends). Quality data fuels effective AI models. 4. Analyze Your System: * Spot Repetitive Tasks: AI excels at handling repetitive tasks. Identify areas where automation makes it easier. * Clean and Organize Data: AI's success hinges on accurate, up-to-date data. Cleanse and structure your data before implementation. * Workforce Impact: Will AI replace or enhance job functions? Communicate changes transparently. 5. Choose the Right Platform: Select an AI platform that aligns with your IT infrastructure. Consider models, algorithms, frameworks, and deployment options. 6. Vendor Selection: Whether internal or external, choose wisely. Expertise matters when integrating AI. 7. Allocate Resources: Budget for AI tools, expert hires, infrastructure upgrades, and ongoing support. Balance upfront costs with long-term gains. 8. Redesign Processes: Analyze existing workflows. Where can AI add value? Prototype and pilot test AI-infused processes. 9. Change Management: Smooth adoption requires a robust strategy. Involve employees and stakeholders. 10. Measure Success: Define key metrics. How will you gauge AI's impact? Set benchmarks. That's it. I tried to keep it as short as possible. If you need a detailed guide, let me know in a comment. I would be happy to provide one. #SSON #iaweek2024 #iaselect #Automation #BusinessExcellence #EnterpriseValue #GenerativeAI #intelligentautomation #Transformation

Data isn’t just the new oil—it’s a tidal wave, and the companies that learn to ride it will be the ones who thrive. In today’s digital era, ignorance isn’t bliss; it’s expensive. Every click, every transaction, every online breadcrumb we leave behind adds to an ocean of untapped potential. But here’s the kicker: It’s not about how much data you have—it’s about how much of it you actually use. You can collect terabytes of data, but if you can’t turn it into meaningful insights, it’s just noise. And in a world that moves this fast, staying in the dark about your data is like trying to read a map with the lights off. You need to do more than collect—you need to understand. Here’s how you can start diving deeper into your data instead of just skimming the surface: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟏: 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐆𝐨𝐚𝐥-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 • Tactic 1: Define specific, measurable objectives for each data analysis project. For instance, rather than a broad goal like "increase sales," aim for "identify factors that can increase sales in the 18-25 age group by 10% in the next quarter." • Tactic 2: Regularly review and adjust these objectives based on changing business needs and market trends to ensure your data queries remain relevant and targeted. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟐: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐂𝐫𝐨𝐬𝐬-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 • Tactic 1: Conduct regular interdepartmental meetings where different teams can present their data findings and insights. This practice encourages a holistic view of data and generates multifaceted questions. • Tactic 2: Implement a shared analytics platform where data from various departments can be accessed and analyzed collectively, facilitating a more comprehensive understanding of the business. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟑: 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 • Tactic 1: Utilize machine learning models to analyze current and historical data to predict future trends and behaviors. For example, use customer purchase history to forecast future buying patterns. • Tactic 2: Regularly update and refine your predictive models with new data, and use these models to generate specific, forward-looking questions that can guide business strategy. By adopting these strategies and tactics, companies can move beyond the surface level of data interpretation and dive into deeper, more meaningful analytics. It's about transforming data from a static resource into a dynamic tool for future growth and innovation. 𝐑𝐞𝐚𝐝 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/dXtkKErW ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

In preparing for a upcoming keynote speech on #genai and the impact on #work; I found these Insights global study by Google #Cloud and National Research Group some of the best I have seen. As a management consulting leader, I'm struck by the clear imperative for organizations to educate themselves on gen AI today. Here are some key takeaways: 1) 74% of enterprises using gen AI report ROI within the first year - faster than most #software deployments 2) 86% of organizations seeing revenue growth estimate a 6%+ increase in annual revenue (real revenue growth!) 3) 84% can move a gen AI use case from idea to production in under 6 months (once again, speed WINS) 4) 45% of organizations report employee productivity has doubled or more due to gen AI (maybe some technology to make our lives easier!) The message is clear: gen AI is not just another tech trend, but a key driver of business transformation and competitive advantage. The study also reveals a "gen AI #leadership gap" - only 16% of organizations are truly leading in this space. These leaders are seeing outsized gains in revenue, productivity, and innovation. To close this gap, organizations must prioritize gen AI education at all levels. This means: 1) Building unified C-suite support and vision for gen AI initiatives 2) Focusing gen AI efforts on core business functions 3) Investing in AI talent development across the organization 4) Prioritizing data quality and infrastructure to support gen AI It is more clear to me than ever that the time to act is now. Those who invest in understanding and strategically implementing gen #AI today will be best positioned to thrive in the AI-driven future of business. Link to the complete study if interested - https://lnkd.in/gmn-yAwE #GenerativeAI #BusinessStrategy #Innovation #Leadership Mercer Ravin Jesuthasan, CFA, FRSA JESS VON BANK #google Adriana O'Kain Ryan Malkes

“Culture eats strategy for breakfast.” - Peter Drucker   In a recent conversation with Mary K. Pratt of CIO Online, I shared some thoughts on the changing culture and talent landscape in high performing organizations and closing the “AI skills gap”.    Successful growth and adoption of AI will require a shift in both the skillsets for varying roles as well as the broader culture towards adaptability and embracing change. This shift is required in all parts of the organization. There are key “existential” questions that need to be answered: - How will the work of colleagues change for various roles and levels? What skills are required? - How will the workforce change to support the new ways of working? How will the organizational structure and mix shift? - How does a company best prepare the workforce for the change? - How does a company successfully sustain the change over time? Overall, there is opportunity to modernize most cultures towards high-speed decision making, agility and experimentation while re-skilling leadership with the critical skills to accelerate AI.   To execute successfully on integrating AI throughout a company, you can take a multi-tiered approach that segments colleagues into groups. - Core AI 'Bubble': Colleagues that are the closest with AI strategy and delivery and are responsible for both the AI platform builds and execution of prioritized business use cases. - AI Users/Early Adopters: Colleagues that are expected to be early adopters of AI such as prioritized business unit groups and technology teams. - Overall Colleagues: The rest of the colleagues where AI may integrate into their day-to-day in the future. So, how do you get started? One approach, as shared by a top consulting firm, has a 5-step framework for a training program that enables talent to adapt to GenAI's impact. Although cultural change requires much more, it’s a good start.  1 Undergo an Impact Assessment to understand the impact in the areas of People, Processes, and Technology. 2 Conduct a Skills Assessment that benchmarks existing skills and the gap to target state. 3 Create Learning Paths based on role or archetype. 4 Establish Change Management practices for socialization, alignment, and communication. 5 Define a Training approach, assets, and schedule to equip learners. I expect that the cultural changes required to take full advantage of AI, will take years, although there will be immediate benefits for sure. What is nice is that there are so many positives about Guardian Life’s existing culture that is focused on Inspiring Wellbeing to build upon. My thoughts on the topic continue to evolve, but here are some point-in-time thoughts in the referenced article.  https://lnkd.in/e5zAwGhc   I’d love to hear your thoughts on the topic and practical ways to address culture and talent challenges. #guardian #guardianlife #ai #artificialintelligence #cio #cioonline