Smarter, fact-based decision making is the catalyst that will unlock AI’s value. In functions like supply chain, organisations can save millions a year with AI supported decisions that reduce working capital, optimise logistics and inventory, and improve demand forecasting.
Millions in new revenue also awaits with AI analytics that support data-driven decisions to expand market reach, introduce new products and services, and drive customer satisfaction and repeat business.
But that value won’t be disbursed evenly across organisations. Some businesses will master AI technologies to realise billions in bottom-line value. Laggards may make game efforts, but McKinsey estimates they’ll be outperformed 10x by AI leaders.
That’s because they’ll fall short in solving three key obstacles that stand in the way of deploying AI at scale. These obstacles below aren’t new challenges, but they haven’t been resolved despite heavy investments into data lakes and other cloud-based technologies. That leaves many organisations in a state of post traumatic data disorder that needs to be cured to unlock bottom-line value from AI.
1. Enterprise data challenges
If there’s a constant in enterprise analytics, it’s that data’s always a disaster. It’s scattered across multiple systems, locations, owners and functions. Even organisations that describe themselves as “data driven” and use analytics for a competitive edge, often don’t have a perfect handle on their data.
In best-case scenarios where an organisation employs a data lake, data remains scattered across multiple domains and is often incomplete. Even if data’s consolidated in a single location, it’s often “dirty” — incomplete, inconsistent, riddled with errors and a surefire way to make bad decisions.
Many enterprises also lack formal data documentation. That makes it difficult for data scientists to fully understand the data and can cause major issues with misinterpretations and errant conclusions.
Traditionally, organisations have invested in tools to consolidate, harmonise, cleanse and enrich data, but this approach is unwieldy, time consuming and prohibitively expensive to run across the entire enterprise. As a result, data scientists continue to spend the majority of time cleaning and preparing data for analysis, one small slice of the enterprise at a time.
2. Limitations of data without context
Data requires context to drive outcomes in an enterprise. This not only involves identifying the outcomes that matter, but understanding which processes impact the outcome and how the processes themselves function.
While many organisations have attempted to “brute-force” data science onto business problems, it’s seldom successful. Context and domain expertise is essential to fully leverage the power of data and AI.
3. AI alone doesn’t drive decisions, humans do
The vast majority of enterprise decisions are made by humans. This is where even the most successful analytics applications fall short — they fail to close the loop between the insights generated and the action that needs to be taken by the business.
One issue is that change management is often needed for teams and individuals to embrace analytics and adapt prevailing practices. People are complicated, biased by their experiences and can be either an obstacle to analytic optimisation or an agent of change. Even if a human accepts and agrees with the analytics output, they may need to make changes in three or more different platforms to effect the desired business improvement.
It’s critical to reduce that friction as business cycles accelerate, employee tenure continues to decrease and the volume of decisions to be made increases exponentially.
AI-driven cognitive augmentation
The increasing pace and complexity of global business has continued to drive these challenges over the past decade. While the emergence of cloud-based technology has given a glimmer of hope, large scale enterprises still find it challenging to deploy analytics at scale as a result of these challenges. Even those that jumped aboard this wave early, continue to struggle moving beyond the pilot purgatory.
One approach that’s proving effective is the deployment of AI-driven cognitive augmentation. This helps organisations understand every aspect of the enterprise by using data crawlers to capture near real-time information from any number of operational systems thousands of times a day. Data is consolidated, harmonised, cleansed and contextualised – making it ready for use by humans and in AI applications.
Proactive decisions are then recommended that improve business performance, using a combination of business domain expertise and machine learning. This uncovers opportunities that are often hidden as a result of massive business complexity and lack of time. It also predicts business outcomes by leveraging real-time data and AI, driving confidence in the decisions recommended.
While challenges to AI adoption aren’t easily overcome, it’s clear that those capable of closing the gap will reap the rewards. That requires both a fundamental thinking of processes and an embrace of innovative technologies purpose-built to help enterprises rapidly capture some of the trillions of dollars in value being unlocked through AI.
Rajeev Mitroo is managing director of Asia Pacific for Aera Technology. Mitroo leads Aera’s go to market strategy and initiatives across key markets throughout Asia Pacific, working with partners developing self-driving enterprises.