Artificial intelligence for business is changing the way we do work and companies are slow to adopt enterprise AI. A study conducted by MIT Sloan School of Management found that only 11% of surveyed business executives said they plan on integrating machine-learning capabilities into their products or services within three years.
One reason for this is because machine learning can be expensive and time consuming. For example, according to McKinsey analysis machine learning adds a 30% annual cost increase per unit at scale which would equate to $600 million in additional costs over a four year period. And it’s estimated data scientists require more than five times as many hours coding machine algorithms compared to traditional programming languages such as Java and C++.
Recently, Datatron’s Founder and CEO Harish Doddi was a guest on the DM radio podcast to share his insights on the matter. This blog expounds upon his talking points from that episode.
Actualizing Data Value in the Modern Enterprise
One area where the adaptation of machine learning in enterprise is important is in Cybersecurity. Machine learning algorithms can help Security Operations Centers detect anomalies in data sets.
Lack of Communication And Stagnant Community
With the way cybersecurity operates currently, a lot of processes are siloed and there is very poor communication between teams. Machine learning can help fill the gaps between teams in an organization by providing them with data.
The benefits machine learning brings to cybersecurity are threefold. First, machine learning algorithms use timelines as events occur for more efficient detection; second, they detect anomalies like simple typos or even typographic errors which human analysts may not notice at all; third, these same machine learnings systems provide new insights into security incidents that humans would never have thought about on their own — providing a holistic perspective beyond simply detecting threats. The productivity gained from machine learning will allow organizations to focus on innovation rather than pure prevention.
An Outdated Tech Stack
Another important reason why companies should adapt machine learning into their stack is because their current solutions are rather outdated.
Using Humans To Solve Human Problems In Customer Service
Using humans to solve human problems can lead them to perform very mundane tasks that should be automated, such as various tasks in customer service. Some tasks in customer service tend to be very mundane; companies should not use humans for these tasks because machine learning can be applied to help automate these tasks.
Human employees are best utilised when they are used for tasks that involve creativity rather than tasks that are tedious, repetitive, boring, and monotonous. Companies should not use humans for tasks that machine learning could solve and instead focus on using machine learning to automate these mundane, repetitive tasks. If companies do this then they will be able to utilise their human employees in ways where their value cannot be replaced.
For example, when it comes to customer service; a company’s human employee can provide emotional support by empathising with the individual who is currently contacting them as opposed to having tedious conversations with people which would otherwise be handled by machine learning software like Google Assistant or Siri.
Disrupting Customer Service With Machine Learning
Artificial intelligence can disrupt the customer service experience in many ways; machine learning can answer questions and provide information about the company, machines may be able to handle repetitive tasks such as retaining customers or providing customer support.
DevOps teams can also be improved by utilising enterprise AI because machine learning can handle the more tedious tasks such as automating infrastructure and code deployments, intelligence machines may also be able to troubleshoot problems.
Why C-Level Executives Are Hesitant to Adapt AI
Business decision makers are hesitant to adapt AI solutions in their company because a lot of them see it as a threat to their work. Executives fear machines would eventually take over their responsibilities.
This fear of taking risks is misguided because it does not take into account what is good for the company; innovation is stifled because of the bureaucracy of self interest. Executives that do not take these risks cannot become greater leaders because they are crippled by the status quo.
C-Level executives can overcome fear of AI risk by first understanding that AI is not really a threat to their job, but rather a force that creates new opportunities for their job. These executives can also take into account how machine learning MLOps AIOps artificial intelligence business companies have the potential to reduce costs and increase efficiency.
Risk Takers Make the Most Effective Leaders
Risk takers, after all, make the best business leaders. Look at Elon Musk, for example. Elon Musk is the CEO and product architect at Tesla, founder of SpaceX, co-founder of Paypal. He has a net worth exceeding $90 billion. His ventures have solved problems that no one else could solve: he’s built rockets for space exploration; developed electric cars that achieve zero emissions; and created an online payment system to make sending money around the globe as easy as pressing a button on your phone.
Executives should take risks because the proof is in the numbers. A study found machine learning MLOps AIOps artificial intelligence business companies are not just buzz words or theoretical opportunities but a way forward for both big businesses and small startups with access to machine resources. The global AI market alone will be worth $59B by 2025.
Data Driven AIOps
The implications for companies adopting a strong AI strategy are overwhelming. Data driven machine learning MLOps AIOps artificial intelligence business companies will be able to aggregate data from various sources and give insights into the behavior of customers, employees or suppliers.
Companies can monitor their systems for anomalies in real time with AI-driven operations management. This eliminates downtime that comes at a high cost because it interrupts production lines, leads to customer churning and jeopardizes security by leaving up vulnerabilities on networks without any human supervision. Automating these processes allows them to scale much quicker than before as well as provide valuable insight for machine decision makers which lead to better decisions overall.
Building On Top of Current Machine Learning Models
The implementation of MLOps should not be something that is made from scratch, if it doesn’t have to be. It should be a machine learning process that is built on top of the current machine learning models that are in production today. This allows for an incremental approach to build up over time and also means you can take advantage of your existing MLOps framework as well.
Robust machine learning models in production can help companies speed up their machine learning projects by reducing the need for training data. This is why having an actionable model catalog is very useful.
The first step in MLOps is to collect enough machine learning model information through a machine intelligence-driven process, which involves parsing machine status logs and analyzing their impact on production performance metrics (e.g., customer churn rate). This will help you understand how your models are performing so that you can use them more efficiently and take corrective actions quickly when needed.
The next steps involve deploying machine intelligence tools with an automated deployment system such as Kubernetes or Docker Swarm to scale up machine learning workloads dynamically, monitor tasks automatically across clusters for failures using AI algorithms, alerting operators of any potential issues before they arise and consolidating analytics reports from multiple sources into one centralized dashboard to make sure that machine learning models are performing as expected.
Model Behavior Audits and Management
Taking on a success-based approach to auditing machine learning models is also important because machine learning models are not perfect. If you want your company’s data scientists to trust their machine learning model predictions even when things go wrong (realizing this will happen), then the ML audit process should be designed in a way that allows machine learning models to be iterated upon and improved.
To conclude, machine learning is a burgeoning field with many exciting opportunities. The uptake among enterprises for machine-learning systems will still be low due to high costs and complexity; additionally there remains a significant amount of suspicion towards these technologies without any prior understanding or experience with them.
Companies looking to increase awareness should take advantage of educational resources available so employees can get a grip on the fact that these tools are meant to work with them, not against them.
Realizing this is important because machine learning is not some machine-powered behemoth that will gobble up jobs and spit out the occasional stock. Rather, machine learning should help make companies more efficient by utilizing its ability to work across a variety of tasks with different levels of expertise.