In the current digital era, Artificial Intelligence (AI) is no longer a futuristic concept. It is a reality that is transforming the way businesses function. AI is an innovation that has proven to be a solution for businesses that want to scale their operations in a cost-effective and efficient manner. The benefits of AI are numerous, from improving customer experiences to increasing productivity and reducing errors. This article will explore the strategies and best practices for scaling AI in your business.
The Importance of AI in Scaling Your Business
The implementation of AI has become a crucial factor in scaling businesses. The Deloitte AI Index shows that companies across different sectors are operationalizing AI to drive business value. According to Gartner, more than 75% of organizations will shift from piloting AI technologies to operationalizing them by the end of 2024. However, implementing AI at scale remains a challenge for most organizations. Only about half of AI projects make it from pilot to production. As stewards of a company’s digital transformation, chief data officers, chief AI officers and other data leaders are important voices for the effective and ethical use of AI to improve operations, drive innovation and grow revenue.
Strategies for Scaling AI in Your Business
Identify the Right AI Use Cases
Integrating AI into your organization starts by identifying how AI platforms, foundational models, generative AI and machine learning (ML) align with key goals. Businesses tend to overestimate the impact of AI capabilities and underestimate the complexity—requiring data and analytics leaders to manage expectations, or risk costly project failures. Think about the things your teams are being asked for the most and how AI could make life easier for those lines of business.
Build a Strong Data Foundation
AI requires a strong data foundation to be effective. Data leaders must ensure that the data being used for AI is accurate, relevant, and of high quality. This includes ensuring that data is properly labeled, structured, and prepared for use. Data governance practices should also be put in place to ensure that data is being used ethically and in compliance with regulatory requirements.
Develop a Multi-Disciplinary Team
Successfully designing and implementing an AI-driven initiative will likely require a multi-disciplinary team. Developing the technology takes machine learning engineers, visualization experts and design thinkers. Then, deploying the technology requires engaging an even greater set of stakeholders, including end business users who will work with the new AI tools daily. Bringing employees throughout the business on board encourages widespread adoption of the tools and ultimately helps embed AI into company culture.
Create Critical Mass with Intentional AI
Intentional AI means that the C-suite at your company, not just the IT department, is focused on using AI to drive important business and strategic objectives. Executives are intimately involved in championing the adoption of AI and are explaining how it aligns with business goals so that everyone at the company understands AI’s importance. In practice, this can mean establishing an intelligent data foundation and creating an application program interface (API) gateway to pre-program common algorithms for onboarding, for example.
Tune Out Data “Noise” to Capture the Right Information
A powerful benefit of AI-based tools is how they help businesses derive insights from large volumes of data. But not all data is created equal. If you’re overloaded with data, it’s hard to isolate the information that matters. You may be caught in a state of paralysis, unsure how to move forward. To avoid this, start small by picking a section of one oil basin, for example, and examining some 200 data fields. From there, narrow it down to roughly 50 critical data elements and design rules and algorithms to manage that information and test data quality.
Track Your Marketing Campaign’s Success with AI
AI can be used to make predictions about the results of your marketing campaigns. It collects data from previous campaigns, including customer demographics and past engagement with your brand. Based on that information, it then makes forecasts about the likely impact of an upcoming campaign, such as how many of your customers might click on an ad or open an email. AI can also help you determine whether a campaign is meeting its goals. This allows you to make adjustments in real-time.
Revolutionize Your Digital Marketing
The power of AI in digital marketing has already been proven to be a game-changer for all. From the advanced personalization that many marketers expect from their software to the increased speed of conversions, AI is revolutionizing content creation and marketing. Conversational AI allows you to offer a more personalized experience for customers using certain messaging apps. Not only does it allow for better personalization, but it also provides something that customers don’t get from other channels: speed.
Enhance Your Customer Management Relationship
AI can redefine your customer service by enabling new ways to interact with customers and enhance and automate many aspects of the experience. Customer management relationship (CMR) software that uses AI can provide a much richer, multichannel experience for you, which helps you manage your customers efficiently while giving them maximum satisfaction.
Automation Can Streamline Marketing
Marketers use machine learning to predict which customers are most likely to respond to a particular marketing campaign, which products they’re most likely to purchase next, and more. To keep up with competitors and reach customers where they are, you need to be active on multiple social media platforms, which means regularly creating and sharing content across many channels. Several AI tools use machine learning to create personalized content that aligns with your brand and targets.
AI-Powered Analytics Generate Actionable Insights
Business analytics involves analyzing past data to make future projections and optimize operations. Machine learning makes this task quicker and more accurate by allowing companies to process large amounts of data at once using algorithms that can detect patterns faster than humans can. Companies that use machine learning for business analytics gain access to actionable insights about their customers and businesses, leading to increased efficiency across the board.
Conclusion
AI is no longer exclusively for digital native companies like Amazon, Netflix, or Uber. AI is embedding itself into the products and processes of virtually every industry. AI is most valuable when it is operationalized at scale. Implementing AI at scale remains a challenge for most organizations, but with the right strategies and best practices, businesses can ensure the success of their AI efforts. As stewards of a company’s digital transformation, chief data officers, chief AI officers and other data leaders are important voices for the effective and ethical use of AI to improve operations, drive innovation and grow revenue.