Being ready for AI


Today I will talk about being Ready for AI. Most executives know that artificial intelligence (AI) has the power to change almost everything about the way they do business and could contribute up to $15.7 trillion to the global economy by 2030. But what many business leaders don’t know is how to deploy AI, not just in a pilot here or there, but throughout the organization, where it can create maximum value. The “how” is the sticking point with any emerging technology, and AI is no exception. How do you define your AI strategy? How do you find AI-literate workers or train existing staff? What can you do to get your data AI-ready? How do you ensure your AI is trustworthy? To complicate matters, the answers to these questions often vary from one company to the next and the environment is continually evolving. But businesses can’t wait for the dust to settle. AI adoption, which has happened in fits and starts, will accelerate dramatically in 2020. Let me give you six areas to think about for your consideration. First one is about structure: teaming for ROI and momentum. Oversee AI by bringing AI, IT and core business leaders together in a structured way to manage priorities, data strategy, resources and use cases. Create a single source of truth in a digital platform with self-service tools and a virtual environment for collaboration, will help connect business problems with AI solutions. Build on projects – Instead of applying AI to a complete process, focus on specific tasks that are common across the business and develop reusable AI solutions. AI teams should create and manage a digital platform for collaboration, support and resource management. Think of it as the one-stop shop for AI efforts: a virtual environment with pluggable tools, where business and tech professionals will share resources (such as data sets, methodologies and reusable components) and collaborate on initiatives. The second: the workforce. Teach users, developers, and data scientists to work together. As predicted last year, upskilling non-AI professionals to work with AI has become a crucial part of workforce strategy. Train, coach, collaborate. For your business specialists to become users and developers, they’ll need training in basic data science concepts. Your data scientists, in turn, will need coaching and new collaborative structures to enable them to partner effectively with business staff. Adapt your workforce strategy. Recruiting and upskilling are just two pieces of the puzzle. You also need to systematically identify how AI is changing job roles and skills; evolve upskilling performance and compensation frameworks and develop new collaborative processes. Cultivate a welcoming culture. To attract and retain AI talent, use AI responsibly and offer top minds the resources, individual empowerment and collaborative culture that enables employees to do great work. Number three; let me shift to Trust. Concerns have grown over how AI could impact privacy, cybersecurity, employment, inequality and the environment. Customers, employees, boards, regulators and corporate partners are all asking the same question: can we trust AI? Assign accountability for responsible AI. Trustworthy AI requires fairness, interpretability, robustness and security, governance and system ethics. Create roles and establish metrics so all teams are working to build responsible AI. Controls based on experience. When building controls for AI, apply what you’ve learned from other technologies. Best practices include developing processes to bring different stakeholders together and continually testing and monitoring AI systems. Explore how tech can build trust. Innovations in responsible AI are advancing quickly. Machine learning algorithms, for example, are getting better at explaining their rationale, strengths, weaknesses and likely future behavior. Four is my favourite: it is all about the Data. Locate and label to teach the machine. The big question about data: how to create value. The top AI-related data priority for 2020 is to integrate AI and analytics systems to gain business insights from data. That’s a realistic goal. AI can be used with data and analytics to better manage risk, help employees make better decisions, automate customer operations and more. But there’s a problem—a big one. Businesses aren’t providing the foundation that AI needs to be successful. Many executives say labeling data is a priority for their business in 2020. Label and standardize. Identify the data sets you need to train AI to solve specific business problems, then prioritize capturing and labeling that data in line with enterprise-wide standards. Use new AI data tools. Lean and augmented data learning, transfer learning and other AI approaches—often integrated into existing applications—can help doing more with less much more easily. Pay attention to policy. Align teams that are helping shape policies in different jurisdictions and address compliance by applying best practices globally. Number five is about Reinvention. Monetize AI through personalization and higher quality. Boosting the top and bottom lines with AI is not a distant dream. Many businesses are already using AI to improve operations and enhance the customer experience. But in 2020, several them will plan or develop new business models based on AI and investigate new revenue opportunities. Many will cultivate these new businesses in separate parts of their organization, distinct from business units that are more internally focused. Build new, data-driven business models. Most of AI’s economic value will come from the consumption side, so get started evaluating what higher-quality, more personalized products and services AI could enable. Put AI on your strategy team. AI’s ability to find trends in data and extrapolate them into the future—along with letting you explore scenarios around new products, markets and business models —can support even your most monumental strategic decisions. Consider investments and acquisitions. With so much AI innovation happening through startups, look at potential markets, technology and talent to access through partnerships, investments or acquisitions. Finally number six: Convergence. Combine AI with analytics, the IoT and more. AI’s power grows even greater when it is integrated with other technologies, such as analytics, ERP, the Internet of Things (IoT), blockchain, and even—eventually—quantum computing. Start with analytics and the IoT. Many technologies can benefit from AI, but advanced analytics and the IoT will bring sizable benefits. Bring it all together with DevOps. Combining AI with other technologies also requires bringing together different teams. DevOps’ emphasis on continual feedback and iteration can speed processes and improve productivity. Keep training with new data. Enterprise systems and IoT networks create data continuously. To prevent a decline in performance, continually train AI algorithms with the new data. So let’s summarize, to be successful and take advantage of AI, your company will need to: ensure AI has its own organizational structure and workforce plans generate trustworthy algorithms and the right data to train those algorithms create a plan to reinvent the business to grow revenue and profits with AI. Finally: converge AI with other existing and emerging technologies to create the most value. To meet these challenges and recommendations, you need a partner that can affect your end to end AI strategy and implementation. Setting the foundation and infrastructure is critical to your success. IBM has capabilities across Systems, Cloud, Watson and Services that can help. Engage with us today in a Discovery Workshop or come to an IBM Garage, so we can help you achieve your AI goals. Thank you for taking the time.

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