How do you know whether an AI project will transform or sabotage your company?

Artificial Intelligence (AI) is in transition from technology and utilization point of view. Regardless of any uncertainty surrounding AI, ignoring its potential poses the risk that companies doing business the old way will go under. Business value, training data, and cultural readiness are essential for AI success. Without all three, traditional solutions are your best bet. For many companies delivering value from AI may be elusive. Models may not be tuned, training data sets might not be big enough, customers may be leery, bias and ethics concerning. Pushing an AI initiative into production before it is ready or expanding an AI strategy beyond an initial phase before properly vetting its results can cost a company money, or worse, send it in a direction detrimental to the business.

The key factors for determining whether a project is suitable for AI are business value, availability of training data, and cultural readiness for change. Here’s few pointers at how with Aion Tech to ensure those criteria are in line for your proposed AI project before your foray into artificial intelligence becomes a sunk cost.

Start with simplest solution possible

At the end of the day, the simple solution runs faster, performs better, and it can be explained to business partners. Explainability is a big part of it and the more people understand the tools and methods in use, the easier it is to gain adoption. With AI can be improved customer experience and to enrich database, optimize the process and increases accuracy, continuously improving and speeding up time to value. Same is valid for conversational chatbots.

Historic data used for predicting future results

Any finite set of data can be fitted to a curve. For example, you can take previous years data and use it for a model to predict, unless the underlying mechanism is completely random or being influenced by different external factors.

The data challenge

Most AI projects require data. Good data, relevant data, data that’s properly labeled and without biases that would skew the results. This situation comes up frequently in marketing applications, when companies try to segment the market to the point that the data sets become infinitesimally small. The sample problem occurs in any system trying to predict rare events. For example, if a company is looking for examples of fraudulent behavior, in a data set of a million transactions, there are a handful of known fraudulent ones and an equal or larger number of fraudulent transactions that have been missed. For rules-based projects can be build a rules-based system by interviewing experts and pulling together traditional formulas and AI is not needed. It would be overkill.

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Here are some of the pointers that Aion Tech can help you with assessing the AI value.

Mature vs. groundbreaking technologies

Measuring the business value of any initiative or technology is not a linear calculation and AI is no exception, especially when degrees of maturity and business potential are taken into consideration. Data mining (predictive variable), cost and training savings, investments and ability to facilitate new uses are influential for acceptable ROI, but putting a degree of trust in the technology, no matter how new or established, is essential.

Leading companies are using commercially available technologies to automate business processes, including DataRobot and Cloud. To determine whether a particular technology is worth investing in, the organization looks at whether it will save costs, time, and resources,?and if its mature enough, should be able to demonstrate it. For technologies with medium level of maturity, companies are assessing the ability to enable new use cases and their cost. With AI an image data can be transferred as text and increase data exchange. For the most cutting-edge, experimental AI technologies, the measure of success is whether they allow for new science to be done, and new papers to be written and published. Companies like Google and Microsoft have ready access to giant volumes of training data, in other companies the data sets are hard to acquire and require PhD-level experts to analyze and label along with related cost to training and building models (reaching up 10 to 20 times cost of commercial industry).

AI measurement and its spheres of influence

When there’s no direct way to measure the business impact of an AI project, companies will mine data from related key performance indicators (KPIs) instead (usually related with business goals, customer satisfaction, time to market, employee retention rates). AI use in health system and finance are rapidly increasing helping to alert for potential abnormalities, reduce hospital stay, automatic verifications prior insurance authorizations, improving operations and optimizing workloads, pulling data from multiple sources The value of AI in those cases is measuring the usage of the tool leading to efficiently delivered engagement, leading to increased productivity.

Focus on business benefits

?Since AI success may be subjective, its important to explain the impact of the AI on business. KPIs should be set around the project objectives, including the model, business and people metrics, as only the technical metric that seems to show success in reality may not be translated to effective impact on the company. Another point to keep in mind is to avoid measuring the progress in isolation. For example, if an AI project was designed to improve something that was already improving for other reasons, then a control group is needed to determine how much of the improvement is actually due to the AI. Other valuable KPI for AI project could be a reduction of false alerts or automatic removals- false positive alerts.

Measuring success incrementally

Automation leading to cost reduction is the easiest and clearest way to show economic benefits of AI, but it can also facilitate new revenue streams, or even completely transform a company’s business model. For example, with AI, an aircraft engine manufacturer saw it could get better at predicting failures and improving logistics so it could start offering engines as a service. “For the ultimate consumer, it’s better to buy miles flown than the engine itself,” he says. “That’s a new business model. It changes the way a company operates because the AI enables it.” And the business impact is immediately obvious as well. In addition to supply chain optimization, other short-term measures of progress can include client satisfaction.

Alignment with strategic vision

Some AI projects can hurt the bottom line, but still be important and transformative in the long term. For example, a company that rolls out a customer service chatbot can eliminate mundane tasks, but some people still may want to engage with peoples for upselling so the organization may not want that. If the organization is committed to both AI-powered transformation with measured ROI to back it up, and has a vision of being customer-focused, then it might look past the immediate hit to the bottom line toward other potentially more meaningful indicators.