Business transformation and Artificial Intelligence (AI) are assumed to go hand in hand. But is that 100% true? We know how AI can impact businesses, but where are the success stories? How can AI become a game changer?
Adopting AI is challenging, but now we live in a world where computational capacity is at its peak, connectivity is seamless, and data is the new oil. We have to find a way to make AI and ML a reality for businesses of any size, and in this article, we will explore that.
Focus on the Why
Include AI in your business strategy to support your future growth. Evaluate opportunities in the market, analyze your business challenges and identify gaps, then see how AI can solve some of them. The approach of including AI without outlining how it helps in your growth plans may not be successful. Do not fall victim to just following the trend without focusing on the “WHY” of artificial intelligence technology.
If unsure, focus on some obvious real-world uses of AI, like process automation and efficiency. Other use cases include enhancing customer value through value-added features like personalized recommendations. These programs are a great place to begin. They are fairly easy to carry out, and calculating their ROI is simple.
Align Your Teams and Culture
Long-term success with AI initiatives for your business is only possible when your teams and people are aligned. When you start working on different AI initiatives in your organization, please ensure that you effectively collaborate and communicate with teams and stakeholders in your company. Your organizational culture should also enable people across the board to participate and contribute to AI initiatives. Build a culture that supports change management, accepts defeats, does course correction, promotes skill building, and encourages collaboration.
Educate on Building Blocks of AI
Bridging the knowledge gap between business executives and data scientists is another hurdle s in turning AI dreams into reality. Business professionals are familiar with the commercial environment but are less familiar with how AI can address current challenges. In contrast, data scientists are often less educated about the requirements of managing a competitive organization despite having a thorough awareness of the potential and limitations of AI. Both sides must take responsibility for improving their knowledge of business and AI by speaking to each other and utilizing expertise. Strong technical understanding empowers business people to encourage technical teams to imagine and invent solutions.
Recognize the Vital Role of Data
As machine learning guru Pedro Domingos puts it: “A dumb algorithm with lots and lots of data beats a clever one with modest amounts of it.” After all, the type and volume of data contribute to the accuracy of AI predictions. It explains why working with data takes up 80% of the time on AI projects. Accessing, cleaning, preparing, pre-processing, and normalizing data is tedious work at the onset of any AI initiative. But, when the correct data is unavailable at the right time, data becomes a strategic problem. For the success of your AI projects, business executives must comprehend the role of data, the types of data they already have, and the types of data they still need to begin generating the correct data sets.
Enterprise AI Platform
Enterprise AI is one of the primary enablers of digital transformation for organizations. As a business, you should start to focus on building, deploying, and operating enterprise AI applications for the future, in whichever way possible. True value from AI can only be derived by deploying enterprise AI applications at scale that manage many high-impact use cases within the organization. Top organizations across industries are using Enterprise AI platforms for fraud detection, anti-money laundering, inventory optimization, predictive analytics, improving human safety, enhancing customer satisfaction, and more. An enterprise AI platform enables organizations to build and operate AI/ML-based applications effectively and efficiently with reduced overhead.
Gearing Up for Success
As a result of digital transformation, companies have moved away from a hierarchical, siloed, top-down organization and toward a more open and creative bottom-up culture. A paradigm shift is currently being pushed by data and AI, one in which automation and data-based facts take precedence over opinions and where probabilities are employed to account for uncertainty. In this era of human-machine collaboration, it will be necessary to reconsider conventional operating models, role descriptions, personal performance metrics, and career advancement.
AI has great potential to change sectors and bring new sources of growth. By 2035, according to an Accenture study from the end of 2016, AI may double economic growth rates in 20 nations and increase labor productivity by up to 40%. With improved automation, edge computing, and accelerated data analysis, AI is simplifying procedures already.