The Value of AI: Exploring Opportunities and Challenges in AI Technology

Demand for Artificial Intelligence (AI) practitioners and budgets for AI initiatives have never been higher. Digital transformation brought about by driving forces like the global pandemic have greatly accelerated development of AI and machine learning (ML) technology. More and more companies are figuring out how to unlock the value of AI and make it work in the real world, often to the benefit of their business and their customers. Organizations looking to launch their own initiatives would do well to understand the true opportunities and challenges in the AI space.

How AI Adds Value to Society and Business

Value of AI

When we refer to the value of AI, we mean it in two facets: its benefit to society at large and its benefit to businesses. Some AI use cases are intended purely to maximize business efficiencies, while others positively impact the lives of their end users (and indeed, there is often overlap between the two).

Benefits to Society

AI can improve user experience of technology, enhancing customer-to-business interactions and creating a more personalized, faster user experience. We see this in the use of fraud detection technology, personalized virtual assistants, customized search recommendations, and many other AI-powered interactions.

In the best of cases, AI can even help save lives. Smart cars with driver assist capabilities are a popular example of using AI to improve road safety. But take for example the application of AI to medical imaging technology. Using ML techniques, algorithms can analyze MRIs for cancer detection. Doctors use these results to target radiotherapy more precisely, causing less damage to tissue. In some scenarios, the contributions of AI could be a matter of life or death.

In another example, researchers are applying AI to extreme weather forecasts. If they’re able to predict the intensity of a tropical storm based on satellite images, for instance, they can inform rescue teams and potential evacuees of action plans before it’s too late.

Major industries like healthcare, automotive, aerospace, and military are leveraging AI for safety reasons, making it an increasingly instrumental component of their operations. Ultimately, AI can have a tremendous positive impact on various areas of society, from saving time to saving lives.

Benefits to Business

Organizations are turning to AI to maximize internal efficiencies as well. They’re using AI to grow their business, increase revenues, and drive down costs. In the energy sector, for example, companies use AI to predict energy consumption in advance, which helps them regulate how much production they need and generates substantial savings.

Companies also use AI to optimize internal workflows. Algorithms can manage mundane tasks previously performed by humans, saving significant effort and time. A bank’s chatbot, for instance, can answer routine customer questions almost instantaneously for a speedier customer experience and less overhead for the company.

AI also helps streamline machine learning processes. If a team needs to develop training data for their ML model, an algorithm can provide initial hypotheses on data labels. This makes the job of the data annotator much easier: instead of starting from scratch, they simply need to verify or correct an existing label. These algorithms are useful for providing quality control in AI development processes as well, ensuring that the labeled training data meets expectations in accuracy. In any case where a business is leveraging a lot of data, even small tweaks made to a given process can add a lot of value.

Unlocking the Value of AI

Avoiding the pitfalls of AI deployment is a significant step toward tapping into the value that AI can offer your business, your customers, and in some cases society as a whole. In each stage of your model build process, it’s important to ask yourself key questions:

Define a Business Problem

  • What problem am I trying to solve?
  • Is AI the right tool for solving this problem?
  • Is there enough quality data to solve this problem with AI?

Many organizations choose the wrong problem to solve or don’t narrowly define their problem to start. In some scenarios, AI may not even be the answer; instead other tools like linear regression may be a better fit for your initiative.

Get the Right Data

  • Where will I obtain my data?
  • How do I ensure my data is the right quality?
  • Who will label my data and how will I ensure those labels are accurate?
  • How will I reduce bias in my data?

Data acquisition and management are the most common challenges organizations pursuing AI face. Getting the data right will amplify your chances of success. Working with the right data annotation tools will also help increase your deployment rate.

Ensure Org Readiness

  • Do I have the right team in place to implement this AI solution?
  • Are key stakeholders on board with this initiative?
  • Does my organization have the money, time, and people available to invest in this initiative?

Your organization must possess sufficient resources to solve your business problem. If it doesn’t, consider whether it makes sense to seek out a third party partner to fill in the gaps. We have developed a tool that maps your organization on the AI adoption journey. Try it out today.

Build Model

  • Does my model do what I originally intended it to do?
  • Is my model showing bias?
  • Does my model need additional training data?
  • Is my model meeting the accuracy and confidence thresholds I require?

Building your model should be an iterative process; expect to finetune repeatedly as you work to achieve desired metrics.

Monitor Performance

  • How will I monitor my model’s performance?
  • How will I manage data drift?
  • How will I scale my solution?

Retraining and updating your model after deployment is paramount to maintaining model performance, as real-world data can and does change. Developing retraining data pipelines will make it easy for your team to quickly retrain your model when needed.

If your initial model delivers good ROI, you may think about scaling your solution. Having the tools, pipelines, and key resources in place will help you achieve scalability faster and more efficiently.

The Future Value of AI

AI development is moving away from a model-centric approach and toward more data-centric methods as teams realize the critical role that high-quality data plays in model performance. But what’s even more exciting is to see the progress toward human-centric AI: AI that learns from and collaborates with humans. This evolution seeks to bridge the gap between human and machine and will be where the true value of AI is unearthed.

As teams work on more human-centric solutions, a key theme remains important: responsible AI is the path forward. Ensuring AI works as intended is ensuring that it’s providing value to the business or society it’s serving. Building AI with a responsible lens is crucial to mitigating bias and maximizing representation, which ultimately means creating systems that work better for everyone.

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