Want to see what improvements you could make to your company’s performance? Do you want to know how to Evaluate AI Maturity Curve?
Let it be a career or people, everyone feels secure when they know what step they have to take. Especially while handling a business. One inefficient move can cost you millions. That’s where a maturity model helps you.
A maturity model is a tool that helps to assess the current effectiveness of an organization. It also supports figuring out what capabilities they need to acquire next to improve their performance. They are structured as a series of levels of effectiveness.
Once you have carried out the assessment, which is the initial stage of the model, to determine your level, you can prioritize what capabilities you need to learn next. This prioritization of learning is the big benefit of using a maturity model. Especially if your company uses AI, knowing how to evaluate AI Maturity Curve will help you determine the next step.
There are various maturity curves based on what your company uses. Such as the Analytics maturity curve, cloud maturity cloud, and data maturity curve. In this article, we will be focusing on how to evaluate AI maturity Curve.
Why Does a Company Evaluate Where it Stands on the AI Maturity Curve?
An Artificial Intelligence maturity model provides an organization with a structure for assessing its current AI readiness and capabilities. This then informs the firm when to prioritize investments towards AI technologies, skills, and processes which are needed to develop, manage and maintain the AI-based system.
Some common reasons for wasted time and effort on AI initiatives are poorly chosen pilot projects and ineffective assumptions regarding how ready the data and teams were to deploy. These failures may lead to a potential loss of confidence in AI from the organization’s leadership and leaving the program dead in the water.
Artificial intelligence requires a new set of skills and a new set of tools and ways of operating. It is important for an organization to consider these challenges seriously and adequately prepare for the long-term AI journey.
Evaluate AI Maturity Curve
AI or Artificial Intelligence has revolutionized from the devices in our hands and homes to how we drive. Nowadays AI has even taken on the drive-thru at restaurants, auto-drive in cars, and many more.
With widely distributed physical assets that can be difficult to access or inspect in detail on an individual basis, the utility industry is an optional use case for AI. By integrating artificial intelligence into your asset monitoring efforts you will be able to realize significant efficiency gains in quickly locating and categorizing assets and determining their condition. This will lead to better decisions regarding the business.
However, AI can be difficult, to begin with. Therefore, before using any AI solutions, it is useful to understand where you stand on the AI maturity curve.
The Initial Stage: Viewing and Validating
For services that do not make use of AI, it is not about using AI but more about preparing for it. This starts with how you view and validate the assets. Too many services still rely on outdated data collection methods. This leads to costly resurvey because the data is subjective and unobjective. Further, the data itself tends to be older – checklists and form data siloed across departments, with less value on its own.
Many companies collect visual data using drones, helicopters, satellites, or ground crews. This is where they begin their Artificial Intelligence journeys. Collecting visual data and combining it with data in a location provides utilities. This offers many benefits. Such as reducing time spent on non-contributing actions like re-inspections and allowing you to bundle work that needs to be done, driving operational efficiencies.
Creating a digital system of audit sets up services to analyze change over time, which is very difficult. With all your asset data in one place, you can start utilizing the predictive capacities or probabilistic nature of AI. These capacities of AI will help you to compare the past with the present to predict the future.
Assessing Asset Conditions
The next stage of the AI maturity curve is using artificial intelligence technology to assess asset conditions. This can be achieved via:
1. Identify and sort assets based on priority
The main aim here is to identify the anomalies in asset images based on priority. Doing this manually could take a person a lot of time. However, by stacking issues based on priority and entering this into the machine, you can apply AI image detection. This will help you to review a huge library of images and instantly identify the most urgent issue to attend to.
2. Correcting, assessing, and teaching the machine
Just creating models and completing certain tasks on day one is not enough. This doesn’t indicate that it will evolve. The inspectors will be using a system to tag new conditions and to alter and change conditions while reviewing the problems and issuing work orders.
3. Let the machine do its work
Machine Learning algorithm helps process data in new ways that were impossible to achieve before. Training an ML algorithm is an evolving lifecycle of continual improvement. As inspectors change their priorities, they will retain the machine. This will alter its outputs to adjust. Multiple machine learning algorithms will be used to drive efficiencies in mid-stage utilities.
Operationalizing AI
For many people, operationalizing AI may still feel theoretical but some utilities are doing it today to make better business decisions.
Automating is one such are. As automating inventory is fairly static, so services try to make sure it is correct all the time by accounting for changes in the field. Late-stage utilities use artificial intelligence to automatically update inventory systems when changes are identified.
This is more useful in disaster management or response. When a hurricane hits, artificial intelligence can compare post-disaster and pre-disaster inventory data. This can further produce a delta report that can show response teams the best places to focus their efforts. Moreover, by connecting this technology to your work systems, utilities can train the system to automatically create a work order for a downed pole.
Size Matters, Sort of
While the AI maturity curve often corresponds with utility size, but there are few exceptions. Whether AI software is best for your organization, but knowing to evaluate AI maturity curve and implementing it tends to get more value. However, what’s best for your organization always depends on the company’s goals, risks, and business model. Whether it’s AI maturity curve, Analytics maturity curve, cloud maturity cloud, or data maturity curve. The most important thing for you is to know where you stand to guide your next steps.
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