We’ve all seen it…maybe even done it: purchased a piece of trendy exercise equipment that now sits idle where it’s used as a very trendy clothes rack. In hindsight, can we recall why we bought the equipment? Did we have a specific fitness goal? Or was it the need to do something related to fitness that drove us to make the purchase?
What does this have to do with artificial intelligence implementation?
Organizations of all sizes are getting swept up in the hype and jumping on the AI bandwagon – expecting everything from magical performance gains from their business operations, to astonishing new insights, to new heights of customer satisfaction. Yet a significant risk of any AI initiative is that in the rush to get something implemented, many organizations aren’t taking sufficient time to identify the problems they want their AI project to solve. The result is an ineffective implementation, lackluster results, none of the promised revenue gains or expense reductions, and perfectly good AI technology that goes underutilized – or worse, is vilified for the poor outcome.
It’s no wonder then that a startling number of business AI initiatives create some level of buyer’s remorse: recent surveys place the failure rate at 60% to 80%. While many of the failures can be traced to data issues (more about this in a moment), shortfalls in AI productivity and outcome can often be attributed to a lack of expertise in the organization to efficiently manage the technology; a lack of preparation for implementing an AI platform; unrealistic expectations; and perhaps most importantly, the absence of a problem statement that is actually solvable using an AI model.
A far better approach is to identify an existing strategic initiative, or a specific business problem, or a defined revenue opportunity, that would directly benefit from what an AI/ML/BI application can provide. If an organization’s leaders can’t readily identify an existing problem or opportunity, then an advanced technology platform is destined to fail. The road to AI success requires an organizational commitment and long-term investment in the full process of identifying the problem that needs solving, clearly determining what success will look like, ensuring a sufficient type, quantity, and quality of data to power that new platform, and only then selecting the appropriate technology application.
“It’s OK – we have plenty of data”.
But is it the right data?
Once the problems that need solving are clearly identified, and the organization’s AI goals are defined, the next step is to identify the data needed for your platform to deliver on its promise.
Most organizations have vast amounts of data, and the decision on which datasets – and equally important, which types of data to use – is not a one-and-done task. Data must be analyzed and selected based on the goals for the AI platform and the problem to be solved.
Behind every successful AI platform is a team of people, often a combination of in-house and third-party providers, making smart decisions about the various data sources that will be tapped and fed into the platform for both training of models and for ongoing production.
If your organization believes it’s ready to purchase and install an AI platform, your team must be prepared to feed it well. Companies eager to adopt AI often neglect to fully realize that data is vital to machine learning. You can have clear business goals, an ample AI budget, and purchase the best technology – but if you feed your platform a diet of poor data, you’ll be dissatisfied – and maybe even damaged – by the results. That wrong diet consists of incomplete, incorrect, biased, and outdated data.
An optimal diet includes information your organization has intentionally and unintentionally gathered across a broad spectrum of areas relevant to your goal. It must contain a variety of data types – both structured and unstructured – to give the model sufficient ground truths from which to draw inferences.
To produce accurate results, AI systems must be trained utilizing data that’s properly labeled and structured. Before an AI platform is purchased, perform a thorough review of your organization’s data. If your data is largely unstructured (videos, images, audio, pdfs, email, social media posts, text and other document files, chat logs, etc.), it will need to be labeled and annotated to become ingestible data.
When fed clean, accurate data, machine learning will successfully progress, generating accurate output, ultimately providing a return on investment that justifies its implementation.
Don’t Navigate the AI Road Alone
Launching an AI platform is no small task, and too many organizations make the mistake of not seeking assistance (led astray by claims of ‘Low Code’, ‘No Code’, ‘Plug-and-Play’). The fastest, most efficient, and most cost-effective road to AI accuracy and success is to partner with a data services firm that can help identify the types and sources of data for your application (this is a case where more is usually not better). The right partner can also assist with clarifying business goals and optimizing results.
If your machine learning platform isn’t performing as expected, or if you’re contemplating purchasing an AI or ML program, it’s time to consult a leading data services company that can maximize the value of your organization’s data. DataInFormation℠ by Liberty Source provides data labeling, image annotation, natural language processing validation, computer vision calibration, training data curation and related data optimization services with a 100% US-based staff and operations to ensure high levels of data security and cultural alignment.
To accomplish this, a team of data engineers, process managers and skilled data associates leverage a great deal of data handling experience and domain expertise to produce precision datasets that are essential to enable accurate machine learning. We work with our clients’ Data Science teams, Data Operations staff, Data Quality functions, and other key stakeholders to refine and enhance the data that is vital to the success of their advanced technology initiatives. And we’re completely platform agnostic.
Because the last thing anyone needs is another good intention that turns into a constant reminder of unfulfilled potential…
To discover how Liberty Source, through its DataInFormationSM suite of solutions, helps you achieve superior performance from your advanced technology investment, contact Joe Bartolotta, CRO, at joseph.bartolotta@liberty-source.com.
