Success Story: How Machine Learning Improves Business Processes
Are you among the 57% of businesses Statista reports are leveraging machine learning and artificial intelligence (AI) to enhance customer experience? Or perhaps you’re part of the 59% who are fast-tracking investments in these technologies to future-proof against ever-changing customer demands and improve business processes.
While the benefits of machine learning are easy to identify, the path to effective implementation is not. It’s a journey fraught with both obstacles and opportunities.
Hariharan KP, Head of Service Operations at solverASSIST, provides a pragmatic roadmap – shaped by real-life challenges and successes – to help your organization adopt machine learning and improve business processes.
How We Successfully Integrated Machine Learning to Improve Business Processes
Initially, the solverASSIST team encountered several challenges. Dealing with unstructured data – trying to make sense of video files, watermarked images, and a whole range of messy inputs – was like putting together a puzzle with the pieces that weren’t made to go together.
Data integration – merging information from various sources, like monitoring and ticket tools – also introduced obstacles. As for model development, it proved that integrating machine learning is far from a plug-and-play situation. Building a machine learning model that works effectively requires a fair amount of trial and error.
Last but not least, there was the challenge of data handling. To remediate this issue, solverASSIST relied on distribution frameworks to manage large data sets without causing a system meltdown.
To overcome the above challenges, solverASSIST created a new approach:
- Define Your Goals: Knowing our objectives made it easier to pick the right ML models that align with our mission.
- Evaluate Data. We scrutinized our available data in terms of quantity and quality, as good data is the bedrock of effective machine learning.
- Consider the Complexity of Models. It was crucial to understand the intricacies of the ML models we use. It’s not a “one-size-fits-all” situation.
- Consider Scalability. Can the machine learning model handle large data sets, for example? The solverASSIST team looked at whether their chosen algorithms could efficiently process large data sets without slowing down.
- Consider Cost and Return on Investment. Before taking the leap, we weighed the costs against the potential returns. It’s not just about spending money; it’s about making smarter investments.
How Machine Learning Revolutionizes and Improves Business Processes
Hariharan KP’s team has seen some practical, game-changing benefits since integrating machine learning into business processes. The algorithms function like an extra set of eyes, spotting intricate patterns in big data sets that humans could easily miss and providing real-time analysis. This is crucial for gleaning insights and thus making better business decisions.
Machine learning has also simplified the process of data cleansing. It systematically clears up inconsistencies in the data, making it more reliable for everyone involved.
Currently, the solverASSIST team is exploring machine learning for the following functions:
Automated Ticket Routing: Machine learning algorithms help intelligently distribute tickets to the right agents based on their expertise and workload to ensure quicker resolution times.
Sentiment Analysis: Natural Language Processing (NLP) techniques are used for sentiment analysis to gauge customer satisfaction levels and modify services accordingly.
Anomaly Deduction: Machine learning identifies irregularities in data or operations, allowing for quick identification and resolution of issues that might otherwise go unnoticed.
Incident Prediction. Predictive algorithms identify potential incidents before they occur, allowing for preventive measures to be taken.
Capacity Planning: Machine learning aids in analyzing workload patterns and identifying bottlenecks in performance, thereby helping in efficient resource allocation and planning.
Virtual Assistance: AI-powered chatbots provide real-time customer service, answering queries and directing customers to the appropriate service channels.
The Benefits of Machine Learning to Improve Business Processes
As Hariharan KP succinctly says: “Machine learning can process large amounts of data and process information to give quick insights.” In industries like maritime and logistics, where time is money and efficiency is key, these quick insights can lead to transformative – and proactive – decisions.
Here’s a closer look at the benefits solverASSIST has witnessed by leveraging machine learning:
ML Improves Business Processes by Reducing Anomalies
Our machine learning algorithms sift through historical data to spot strange patterns or behaviors, letting us tackle potential issues before they snowball. For example, a machine learning algorithm could analyze engine performance data to spot unusual patterns, like sudden changes in fuel efficiency.
By flagging these anomalies, we can perform preventive maintenance before a major engine failure occurs.
ML Improves Business Processes by Identifying Root Issues
Machine learning models analyze past incidents to help us determine the underlying causes, so we’re not just treating symptoms. If there’s been a recurring issue with delayed cargo deliveries, machine learning can sift through past incidents and associated variables – from weather conditions to port traffic – to identify the root cause.
This kind of analysis enables targeted solutions, like altering shipping routes during specific seasons, to avoid bottlenecks.
ML Improves Business Processes by Classifying Tickets
Fortune Business Insights expects the global natural language processing market to grow from $26.42 billion in 2022 to an incredible $161.81 billion by 2029. Thanks to natural language processing, support tickets find their way to the right agents more efficiently than ever.
When a problem arises – maybe a shipping container’s tracking is off – the ticket lands in customer support. Using natural language processing, the system automatically directs the ticket to an agent specialized in tracking issues, expediting the resolution process.
ML Improves Business Processes by Predicting Customer Behavior
Machine learning allows us to tune into customer interactions using historical data to predict future behavior and offer more personalized service.
For example, machine learning can analyze historical data from interactions and transactions to predict when a customer will likely need additional shipping capacity. This allows us to proactively offer solutions before the client even recognizes the need.
ML Improves Business Processes by Optimizing Support Channels
Our machine learning models help us predict the most effective support channels based on past interactions. Machine learning algorithms analyze how effectively past queries were resolved via email, chat, or phone. For example, if complex issues are resolved more efficiently over the phone, the system will prioritize this channel for similar future queries.
How Machine Learning Promotes a Data-Driven Company Culture
When asked how machine learning can foster a data-driven team culture, Hariharan KP discussed key elements beyond mere data crunching.
The promise of machine learning lies in its ability to deliver real-time analysis and quick insights. This is a game-changer for a team that’s accustomed to waiting for data or working with outdated information. Decisions can be made more efficiently.
One of the underappreciated benefits is the user-friendly interface that machine learning and other business intelligence tools often provide. This usability does more than make life easier – it democratizes data. When data is accessible and understandable, it fosters a culture of ownership and accountability. Team members are not just passive data recipients but active participants in data-driven practices.
However, Hariharan KP emphasizes the importance of quality data input for quality output. In fact, a survey reveals that alarmingly, only 16% of companies characterize the data they are using as “very good”.
The immediate feedback from machine learning models also educates team members on the importance of accurate data entry. If the information entered is flawed, the insights will be flawed, and everyone will learn to up their game.
The Best Tips For Adopting Machine Learning to Improve Business Processes
A tech leader in the maritime and logistics sectors, Hariharan KP possesses deep expertise in driving business growth through data-driven strategies and technology. His real-world experience in leveraging machine learning makes his insights invaluable for businesses considering a foray into this transformative tech.
Hariharan shares his best tips on adopting machine learning:
Tip One: Start Small
It might be tempting to roll out a large-scale machine learning model immediately, but it is crucial to start small. Validate the feasibility of your machine learning approach with a smaller project first. There’s no need to go all-in with a “big bang” approach; you risk failure and potentially significant losses. Take measured steps and scale up as you gain more confidence in the technology.
“This is a classic problem,” warns Hariharan KP. “Machine learning is there, so can I change the world? No. Machine learning is there, but you have to go slow on your approach.”
Tip Two: Ensure Data Accuracy
There’s no room for compromise regarding data quality. Before you even think about machine learning, ensure your data is accurate. Faulty data can render even the most sophisticated machine learning model ineffective, leading to poor business decisions.
“Data quality is of primary importance,” reminds Hariharen KP. “This is fundamental for any machine learning model.”
Tip Three: Be Patient
Machine learning is an iterative process; it learns from data over time. Don’t expect it to solve all your challenges overnight. Manage your expectations and understand that machine learning isn’t a magical fix. Patience is crucial when you’re adjusting to this new data-driven approach.
“It’s not human – it won’t give you everything in the first go. Manage expectations. Machine learning is not a magical solution.” – Hariharan KP.
Tip Four: Measure the Impact
To understand whether your machine learning model is actually benefiting your business, you need to rely on metrics. Are your key performance indicators improving? If you do not see the desired impact, it may be time to reassess and adjust.
“Be clear on the objective,” advises Hariharan KP. “What do you want machine learning to address? The goals and the efforts have to be clear, as well as how you will measure the success you get out of these systems.”
Tip Five: Stay Updated
Machine learning evolves fast. What’s cutting-edge today may become outdated in a matter of months. Keep yourself and your team updated with the latest advancements to ensure you’re leveraging the most current and effective techniques.
What Are The Trends in Machine Learning?
As we stand on the cusp of a new era in machine learning, it’s clear that this technology is slated to revolutionize isolated processes and entire business ecosystems. Hariharan KP predicts that ”machine learning will facilitate collaboration between human experts and AI systems, resulting in hybrid models that combine human intuition and AI capabilities.”
Hariharan KP expects machine learning to become commonplace across many business platforms, making predictive analytics more widespread than ever. This integration will:
- Give businesses deeper insights.
- Enable more informed and data-driven decision-making.
- Help organizations anticipate market trends before they emerge.
Trend One: Data Governance
As machine learning proliferates, there will be a heightened focus on data governance to ensure data quality and security. In a world increasingly driven by data, maintaining its integrity is paramount.
Trend Two: Augmented Analytics
Machine learning will transform how we view analytics. Through natural language processing and automated integration, analytics will become accessible to more people, not just data scientists. Think of it as “analytics for everyone,” where the complexity runs behind the scenes, but the user gets straightforward, actionable insights.
Trend Three: Internet of Things (IoT) Integration
Forbes reports that 80% of companies have already integrated IoT into their operations. Unsurprisingly, Hariharan KP predicts that IoT devices will increasingly leverage machine learning for real-time data analysis, making decentralized decision-making possible and efficient.
The applications are limitless, whether it’s smart ships in the maritime industry or real-time logistics tracking.
Trend Four: Ethical AI
As machine learning becomes more integrated into daily operations, the ethical implications – like data privacy and algorithmic bias- will come to the forefront, necessitating responsible AI practices.
Trend Five: Talent Development
Machine learning’s rise will also expose a current skills gap in the market. Businesses must invest in upskilling their workforce and focusing on talent development to stay competitive. “You need to develop people as you build these systems,” urges Hariharan KP.
Harness the Potential of Machine Learning to Improve Your Business Processes
Hariharan KP’s experience in digital assets, IT services management, and data-driven approaches has led to numerous successful business digital transformations. In his role, he navigates everything from standardizing IT service delivery to innovating for incremental improvements.
Ready to use your machine learning technologies to their fullest potential? Reach out to Hariharan and the solverASSIST team.