Cloud AI Can Help You Survive The Storm
The business world has always been challenging and competitive. But the environment that businesses and other organizations are working to weather today is downright stormy.
The pandemic shuttered businesses, prompted a shift to digital operations and brought certain economic sectors to a near standstill. Private and public organizations are still grappling to address this new world in which personal safety, social distancing and touchless experiences are key.
Cloud-native artificial intelligence (AI) and machine learning (ML) services can help address these challenges. Organizations around the world use these technologies today to help solve some of the world’s toughest problems, including how to put an end to the pandemic. Your organization and its stakeholders can benefit from cloud AI to achieve your goals, too.
Get Results Fast And Don’t Start From Scratch
With cloud AI, you can understand common customer and internal pain points. You also can use cloud AI to act on these insights. And when you leverage cloud AI, you don’t have to acquire expensive talent or build massive systems to make these things happen.
You can use pre-packaged AI models to spin up new applications and initiatives quickly with relatively low investment. In many cases, you won’t need to hire an army of costly data experts because these are self-learning services.
AWS SageMaker, which simplifies the process of building and implementing ML models, is one example of such a service. Google Cloud and Microsoft Azure offer a range of services as well.
Recently our company created a cloud AI-enabled chatbot for a public sector agency that was flooded with citizen calls about how to file unemployment claims. The chatbot, which handled millions of inquiries per week, has the intelligence to direct people to the resources they need to file unemployment claims. We set up the chatbot for this state agency within just days using readily available and trained AI models and services from Microsoft Azure.
Meet Business Objectives and Uncover Opportunities for Improvement
This chatbot example shows how cloud AI can help an organization that is already aware of a pattern or friction point to get a better outcome, such as opening a new customer service channel to allow employees to focus on other tasks and let citizens get the help they need.
Cloud AI also can assist to safely reintroduce workers and students to their locations. It can help automatically track the occupancy of classrooms and conference rooms. It can power AI-enabled biometrics applications to ensure only authorized parties can access online platforms and physical venues.
You can use pre-packaged AI services and models from major cloud providers to do such things. For example, Google Cloud’s AI-enabled services include:
- Recommendations AI – To deliver highly personalized product recommendations.
- Vision AI – To derive insights from images in the cloud.
- Dialogflow – To build virtual agents and other conversational experiences.
If you have a specific goal in mind, take a top-down, deductive approach. This journey will involve creating a theory and hypothesis, conducting observation and getting confirmation. If you’re looking for patterns or pain points, take a bottoms-up, inductive approach. This starts with creating a theory and hypothesis but then moves to pattern recognition and observation.
Avoid AI Missteps By Considering and Addressing Bias, Data, Drift, Privacy and Security
As you work to better understand and take action to improve user experiences and drive business outcomes, take care in your implementation and the use cases to which you apply AI. Work to prevent AI bias and privacy problems, and make sure your model works as intended. These are challenging problems for which humans don’t always have tidy answers, as Unisys President and COO Eric Hutto recently noted, but they bear consideration and attention.
- Continue to revisit your AI model because inputs and environmental factors change over time. As a Unisys colleague explained, such changes can lead to model drift, creating bad results. Monitor for drift and retrain your model when it happens.
- Ensure your AI implementation is secure. That will prevent unauthorized parties from adjusting model values, which could produce different and possibly problematic results.
- AI models rely on data. Make sure your data lake doesn’t become a data swamp.
The first step always involves data ingestion and cleaning, normalizing the data and sometimes removing personally identifiable information. Make sure you have the right controls around data to meet your own and regulatory security and compliance requirements. Then bring the data together with your AI services, including modeling, testing and validation.
In the past, you needed data engineers for data modeling, ingestion and cleaning; security engineers for security and compliance of your data; and AI engineers for analytics and ML. Cloud AI has brought down the barrier to entry, enabling, in most scenarios, a multi-skilled, full-stack cloud engineer to address all these tasks. You don’t need specialized and hard-to-recruit AI/ML roles. You can enjoy new operational efficiencies, improved R&D innovations and better customer experiences.
Don’t Be Afraid Of AI
Implementing AI may seem daunting. But don’t be afraid of AI, because humans can verify AI outputs and redirect efforts when required. As you gain confidence, you can reduce human intervention.
It is stormy out there. But this environment calls for action, not retreat.
The adoption of AI is an urgent requirement to ensure you don’t let the competition get ahead of you, avoid risk of failure and stay economically viable. Cloud AI now makes it much easier and affordable for you to increase customer and employee safety, improve user experiences and make your organization is more responsive and resilient when it comes to fraud and failure detection.
Cloud AI will enable your business to weather the storm and emerge stronger from it.