AI is transforming the way we operate business around the world. Everything from the ads we see online to how we choose shows to watch while sheltering in place is now influenced by AI.
As popular as it has become, getting started with AI within a company can seem like a monumental task, especially while competitors are moving at a quicker pace with their AI initiatives. But AI doesn’t have to be complicated and you don’t need to reinvent the wheel to introduce it to your operations.
The key to getting started is to focus on the business problems you would like to solve, especially the business problems that can most benefit from advanced analysis of data. Look for areas where meaningful human effort is needed to complete routine tasks or make better decisions while large volumes of data sit idle or are underutilized. Here are three areas where AI can help optimize:
1. Decisions and insights
How can we gain data-driven insights to help make better decisions? The type of decisions that can unlock areas of improvement at your organization by:
- Lowering costs
- Improving customer experience and engagement
- Reducing customer churn
- Detecting fraud
It is estimated that only 4% of businesses effectively capture value from their data. This is partly due to the fact that 90% of digital data generated is dark or unstructured. Advanced analysis of both structured and unstructured data can reveal hidden, and often surprising, insights. Companies worldwide are already using such insights for countless applications including those that create better customer experiences, more efficient manufacturing of products and supply chain management, personalized shopping, AI-generated podcasts and entertainment content, and protection against cybersecurity threats.
2. Processes and workflows
How can we leverage the power of process automation to help free up time and resources? Throughout the workday, employees are often engaged in time consuming rote tasks and workflows that could be performed exponentially faster by intelligent machines. These types of tasks are often repetitive, create bottlenecks, and don’t require the human touch. By implementing automation in this area, your team can take back the precious time needed to focus on high value work that grows the business.
In this regard, something even simpler than advanced AI and machine learning, such as robotic process automation (RPA), can be used to send out invoices or process credit card payments. Automating tasks like these not only saves time but can reduce human errors and costs. According to Gartner, the COVID-19 “pandemic and ensuing recession increased interest in RPA for many enterprises” due to increased pressures on businesses to better manage operations and costs.
3. Teamwork and communication
How can we make team collaboration more efficient to raise productivity? Here’s where next-generation communication tools and knowledge management systems play important roles.
These days, information is the new gold. By making it easily accessible, searchable, and sharable, information can help each of us perform better at our jobs. At enterprises, information is typically stored in and retrieved out of complex knowledge management systems that sit at the core of decisionmaking. Employees depend on these systems to access the organization’s knowledge base and improve communication and collaboration across teams.
Powering communication and knowledge management systems with AI can usher in an entirely new level of productivity. For instance, machine learning can be used to map diverse data types housed in disparate storage repositories and enable universal search capabilities across everything from PDFs to spreadsheets, images, text, audio packets, and even videos. Natural language processing (NLP) can be used to summarize documents or help communication tools improve grammar or tone.
AI is also used in detecting sentiment and emotion contained in digital conversations and social media chatter. Companies use sentiment analysis to identify key emotional triggers for a variety of use cases, not only to improve employee relations but also those with customers—de-escalating situations where frustration or threats are observed, understanding brand loyalty, or identifying early signs of happiness or dissatisfaction within teams or customers.
Additional questions to help kick off your AI initiative
After landing on the key areas where AI can add value, consider the following questions:
- Which use cases can quickly prove value?
- Is there sufficient managerial or executive support for the intended use cases?
- Are ROI expectations realistic among the stakeholders?
- Are changes required to the compute infrastructure for this purpose? This involves hardware and IT capacity planning.
- Do we have access to the right data for the intended use case? In the exploration phase, considering the 4 Vs of data can help accelerate discovery and unlock hidden value.
- What are the common missteps to avoid in implementing AI?
- Can we find the right skills in-house or via external vendors to make this this a reality? It takes 18 separate skills to bring an AI solution to life from ideation to production level implementation.
Get informed and learn from the experts
AI is a vast and complex field. So, it would be helpful to get started with key AI terms and concepts. You may also enjoy learning how machine learning differs from traditional data analytics.
Of course, if time is at a premium, you can form a partnership with an AI firm. The data in your business tells a story. Finding these stories is what AI professionals do best. And, perhaps more important, they can help you avoid costly mistakes.