Artificial Intelligence (AI) has emerged as the most over-hyped term since “Big Data.”
As we enter the third decade of the 21st century, many of us are asking ourselves why the promise of AI has not been realized. Upon asking the question, we discover an overwhelming number of reasons why case studies and documented successes are few.
Some of the reasons include (but are not limited to) a lack of clear management vision, data warehousing and technologies that do not allow for the data to be organized and leveraged correctly, a misunderstanding of the strengths and weaknesses of machine learning and cognitive systems, and many more beyond the scope of this brief.
Despite this lack of progress, there is still a general perception among senior executives that AI-powered analytics can provide companies rich, revenue-generating insights to better compete in an increasingly competitive digital landscape.
Many companies have taken a pilot-driven, siloed approach to implementations, which unfortunately demonstrates that investment into AI resources can be prohibitive – e.g., costs for data scientists, AI software, and data infrastructure are needlessly duplicated.
An enterprise-wide approach, however, offers an economies-of-scale value proposition as the investment is deployed across multiple use-cases to advance core business goals.
Limited development of AI capabilities in organizations is often more about company culture than it is about technology. Among the reasons for the lack of enterprise-wide adoption of AI in analytics are limited vision, talent gaps, change management, employee fear, lack of digital mindset, and so on.
The keys to a successful enterprise-wide implementation of AI in analytics are:
Top-down vision and approach
Disciplined approach to identifying/prioritizing/sequencing use-cases under a strategic roadmap
Dedicated change management/effective communication
Adding AI to analytics capabilities has immense potential to increase productivity while also reducing employee workloads. This is exactly why AI is seen as a substantial competitive advantage for early adopters. With analytics, AI can crunch numbers and process data much faster than a team of data analysts while also producing higher quality datasets.
By integrating AI across the entire business, numerous applications of AI technology can generate a competitive advantage. One recent example concerns a Trianz client in the coal-mining industry.
This enterprise has leveraged artificial intelligence to analyze land surveys, resulting in the identification of mining deposits that was missed using more traditional methods. Further, it was able to streamline its supply chain with predictive AI analytics, increasing haulage truck utilization and reducing fuel consumption. Another significant benefit was material asset management.
AI-powered analytics reduced supply shortages and maintained constant output generation, meaning logistical planning was improved as the enterprise could reliably predict when storage capacity would be full. You can read more about how Trianz helped this coal-mining outfit in this Mining Global news article.
In another project, Trianz was able to leverage AI analytics to improve customer service provision. AI in analytics was useful for driving the customer experience by “massaging the sales pipeline” with intelligent algorithms that monitor keystrokes and webpage behavior from the user.
As an example, imagine that a user is interacting with a web chatbot but seems ready to leave the chat. With live AI analytics in the background, an enterprise could detect if a user is about to leave the website and transfer them to a human customer support agent. This helps retain prospective customer interest, giving your business another vital opportunity to convince the customer to make a purchase.
AI becomes increasingly useful as its reference datasets grow. More data leads to more insights, making enterprise-wide adoption an excellent way to supply your AI analytics platform with data. With a unified data analytics platform, the end result would be a data-driven culture founded on high-quality insights.
Implementing AI analytics technology is not enough; changes to enterprise culture are vital to maximizing effectiveness. Top-level executives have traditionally driven business change via something called the “highest-paid person’s opinion,” abbreviated as HiPPO.
For enterprise-wide AI analytics to work, your business needs to migrate away from opinions and start leveraging factual data for key business decisions. This change will improve project outcomes, increase the achievement of business objectives, and ultimately drive long-term growth.
At Trianz, we help enterprises reach the forefront of technology. Our goal in helping companies bring AI into their analytics is for them to deliver rich, sales-and-service-driving insights to their decision-makers – the type of insights that companies need now more than ever. You can read more about Trianz analytics consulting services and our analytics client success stories here.
Trianz is an industry-leading analytics consulting service provider with more than 400+ client engagements—including numerous Fortune 500 companies. We are also rated as a key player in the data analytics consulting market, according to Grandview Research. Our combination of effective strategies and excellence in execution has helped us become the top-rated technology service provider, as published in our annual client satisfaction survey.
Reach out to Trianz today to start building competitive advantage by adopting AI-powered analytics.
Contact Us Today
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
The Cloud is the Key to Transformation Success… Transitioning your applications to the cloud is undeniably a critical factor to a successful digital transformation endeavor. It’s more than just a lift-and-shift, however. Let’s explore several things that you need to consider before migrating your applications to the cloud, including: Readiness of your application portfolio Where to begin – the right business case and migration strategy Technology requirements and considerationsExplore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore