“Civilization advances by extending the number of important operations which we can perform without thinking of them.”
Artificial Intelligence (AI) is fundamental to our progress. Intelligence demonstrated by machines running business software applications need to evolve so that machines can not only perform the mundane operations automatically but can also garner intelligent insights, predictive analytics, and intelligent reasoning for split second decisions. AI can enlighten us with new knowledge that will help us make productive and efficient business decisions and thus a competitive advantage.
Let’s look at some typical work-flows at organizations:
- Inside Sales uses a cloud service that exports opportunity/leads from Salesforce to an Excel report on a daily basis to help pursue lead generation.
- Supply Chain Management uses a cloud integration platform to integrate purchase order transactions between multiple trading partners.
- The finance department uses an integration platform to reconcile and consolidate financial data from NetSuite ERP (SaaS ERP) to their enterprise SAP ERP instance.
- Finally, the IT department maintains a consolidated master record of Products and Customers in its Informatica MDM solution.
Now, let’s assume people within the organization want to understand the following:
- An inside sales rep wants to know who in our own organization has the best access to decision makers at a target account?
- A production manager wants to know that to meet production demands which supplier is more efficient based on historical and real-time inventory data?
- A VP of Sales may want to know that after an M&A which common customers can be targeted for upselling?
- A CTO may want to know what product features during the last 5 years have been directly responsible for increase in revenue?
Imagine how long it would take at your organization to answer these questions? Herein lies the opportunity for artificial intelligence to build a knowledge-ready organization that helps you stay one step ahead of competition.
According to McKinsey, three factors paved the way for the AI revolution: the exponential increase in computing power (together with its lower cost), the massive explosion of data generation and usage, and the advancements in algorithms.
The convergence of these three factors has enabled machines to perform cognitive functions such as learning, perceiving, problem solving, and reasoning. Some experts even believe that eventually, machines can learn creativity.
Because AI can already perform some cognitive functions associated with humans, its adoption has proliferated. AI is already all around us. It’s in robotics, autonomous vehicles, mobile applications, ecommerce websites, and smart homes, to name a few. AI also drives business applications such as analytics, deep learning, virtual agents, and machine learning.
But in spite of all these benefits and applications, we are only scratching the surface of what AI can do. AI possesses limitless potential for the benefit of consumers and enterprises alike.
With tremendous business benefits such as real-time analytics, deep learning, and automation, enterprises are also in a race to utilize AI and gain competitive advantages for their companies.
The Artificial Intelligence Race: The Benefits for Early Adopters
Deloitte surveyed 250 leaders who are not only aware in the cognitive space, but are also working in companies that are aggressively adopting cognitive technologies or AI. These leaders reported nine key benefits that their enterprises are experiencing because of cognitive technologies:
Enhancing products. AI is enhancing products in terms of features, functions, and performance for early and aggressive adopters of AI. For instance, AI has enhanced the services of music and movie streaming companies such as Spotify and Netflix by providing smarter recommendations. AI utilizes customer data such as the music or movies they consume to provide more relevant recommendations, leading to greater engagement and satisfaction.
Improving decision-making. Machine learning is at the heart of AI. Machine learning utilizes an ever-evolving algorithm that is able to process data and learn how to detect patterns and make better recommendations or predictions. With massive amounts of structured and unstructured data at their disposal, enterprises are finding that analytics tools with machine learning capabilities are able to process and learn from these data and provide business intelligence that would otherwise remain uncovered.
Creating new products. AI has enabled enterprises to develop new products with cognitive capabilities. For example, AI has empowered some companies to offer customer support automation services by developing chatbots. Chatbots utilize machine learning to provide meaningful and relevant support and recommendations to customers. Giants in tech such as Amazon have also introduced smart assistants like Alexa which utilizes AI for voice recognition and managing other smart devices.
Streamlining operations. AI has enabled companies with heavy machinery and equipment to perform predictive maintenance by analyzing the performance, temperature, power consumption, and other factors affecting the condition of these assets. AI is also being used to quickly detect anomalous behaviors and possible intrusions in enterprise systems.
Empowering employees. While it is widely believed that AI will replace workers through automation, another way of looking at it is that AI will free up workers for more strategic, creative, and innovative tasks. Some experts even say that AI is not taking away jobs from humans, but transforming them.
Finding new markets. AI is utilizing massive amounts of customer data to help enterprises understand their customers and find similar customers. AI also enables enterprises to utilize data generated by social media websites and applications. This makes it easier for enterprises to discover new markets or enter into new economies.
Capturing information. For many enterprises, information in unstructured sources such as paper documents or employee hard drives are no longer being utilized. Programs with AI capabilities such as search and analytics, image capture and processing, and NLP are enabling enterprises to find critical information and generate business intelligence from them.
Optimizing sales and marketing. AI is being used for sales and marketing in a variety of ways. Enterprises use AI to gather data on customer behavior, analyze them, and turn them into actionable insights. This information enables enterprises to deliver the products customers want, how they want it, and when they need it. Ad targeting is another way AI is used for marketing. Google and Amazon have been using AI to increase conversion of internet ads and to recommend relevant products to customers.
Reducing headcount. While some leaders utilize AI for cost-savings by reducing headcount, more leaders believe in the job transformation that AI brings. Sure, it is undeniable that reducing headcount benefits the bottomline in the interim. In the long run, however, enterprises expect that AI and humans will work together in new, transformative ways.
How CIOs Can Stay Ahead of the AI Race
Gartner provides three key insights for CIOs in order for them to stay ahead of the AI race:
Key Insight #1: Develop a Strategy for AI
The benefits offered by AI can be so enticing that it may tempt business leaders to implement AI projects without carefully studying the technologies, vendors, processes, and even the skills required to make the most of AI. On the other hand, some leaders may remain skeptical about the necessity of AI in today’s highly competitive business environment.
CIOs are in prime position to lead enterprises in their AI projects. They can inform other C-level executives and even the board of directors about the developments in the AI space, especially regarding how it affects businesses. It is also important for CIOs to separate the hype from reality when it comes to AI and other technologies. Finally, the CIOs must develop a strategy for implementing AI and integrate it with their overall business strategy instead of developing AI projects on an ad hoc basis.
Key Insight #2: Develop the Necessary Knowledge and Skills
Big Data analytics, machine learning, and natural-language processing (NLP) are among the leading commercial applications of AI. However, these applications require a specialized set of skills such as data science and data engineering. CIOs must ensure that the enterprise builds or acquires the necessary skills and expertise to implement the AI strategy and to manage and govern AI solutions. Furthermore, the CIOs must also ensure the retention of talents with data science or engineering expertise.
Key Insight #3: Keep Testing
According to Gartner, the “market conditions for commercial success with AI technology are well-aligned, making AI safe enough for CIOs to investigate, experiment with and strategize about potential application use cases.” This means that the benefits offered by AI are already proven and well-founded and the technologies in the market are mature enough for adoption and for reaping the benefits of AI.
CIOs should not be afraid to test AI technologies. They can start by rolling out one AI business application on a small scale in order to build experience and business cases. Once enterprises or their IT departments have the necessary experience with dealing with AI, they can undertake bigger AI projects such as Big Data analytics that require the integration of its enterprise systems.
AI Starts with Integration
AI is dependent on high quality data. In order to generate business intelligence, make smart decisions, or predict outcomes with minimal to zero human intervention, AI first needs to collect and learn from quality data. This means that the first step to a successful AI implementation project is integrating quality data from various structured and unstructured sources.
Liaison’s ALLOY® platform connects, cleanses, harmonizes, enriches, and secures data coming from various sources so that enterprises can prepare for AI projects and solutions. It ensures that quality data is supplied to AI programs so that the enterprise can reap its full benefits.