The same report illustrates that 76% of employees with disabilities aren’t fully open about them. Luckily, advances in accessibility software (speech-to-text, predictive typing, visual recognition, and others) might help them eliminate some of these barriers so that they can focus on their job.
Cost-efficient processes
Adopting AI initiatives helped organizations reduce their expenses while simultaneously increasing revenue.
McKinsey’s State of AI in 2021 survey shows that over 33% of companies have decreased costs by over 20% (28% of businesses shaved off 10-19% of their expenses, and 18% cut less than 10% of costs). Additionally, 13% of companies had a 13% profit boost (21% had a 6-10% revenue increase, and 33% improved their revenue by less than 5%).
On top of that, AI reduces digital transformation costs to wireless-first and cloud-enabled connectivity architecture. IDC predicts that 65% of organizations plan to adopt wireless-first strategies to reduce expenses and enable richer experiences. Where does AI fit in this?
Advances in AI-powered analytics will alleviate reconfiguration and upgrade to the newer wireless technology cost-efficiently. Plus, it will help remote employees access updated tools more efficiently.
Informed decision-making
Predictive analytics software enhances your business decisions’ quality, creativity, and effectiveness. AI applications process large quantities of unstructured data from multiple sources and turn it into insights. This helps uncover gaps between your offerings and demand and introduce the services your clients want.
Analyzing data in real time is critical for actionable corrective actions. Take automotive IoT, where autonomous vehicles must respond to external objects and safety scenarios in milliseconds, or cybersecurity, where you need to react to intrusions immediately.
Client-centric business model
Automated self-service tools, digital assistants, and chatbots can help your customers solve their problems seamlessly, keeping them happier. According to the 2022 research by Deloitte, enterprise executives cite improved and better customer service and satisfaction as their primary reason to invest in AI.
AI-based algorithms and Big Data help companies develop detailed customer behavior profiles to understand their needs, problems, and preferences. With this data, you can tailor ad campaigns and interfaces for user segments, likely improving customer satisfaction.
Still, despite all these benefits, many companies need to be more cautious about the upfront costs of AI. That’s why you need to consider what business aspects of your organization could use an AI boost.
What business aspects should you enhance with AI?
There are countless ways to use AI technologies; selecting the right one is critical for your successful digital transformation. It’s no less important to make your solutions scalable. 88% of senior managers and executives believe they won’t achieve their growth objectives without scaling AI—however, 76% struggle with rising algorithm-based tech across their businesses.
What AI directions should your company choose, and how to successfully implement them at ai projects and scale?
Define the processes to improve
Start by assessing your IT infrastructure, internal policies, and workflow. This should give you a good idea about the most value-driven processes in your company. The audit also helps to pinpoint bottlenecks in your chain of operations. For example, it could be an overly complex onboarding process, invoice management, or slow customer service.
Then, you must select the processes you can enhance or automate with AI. However, it would help if you didn’t spread too thin across all your various business operations. Algorithm-based technologies can be pretty expensive. We recommend prioritizing the operations you could improve with minimal investments.
Optimize your workforce
Your employees need to understand how AI applies to their roles, so redefining your organization’s responsibilities and ways of working is imperative. You must also train your staff with new tools and security practices.
It’s important to distinguish the roles of AI practitioners and data scientists. Data officers, modelers, and machine learning engineers must uphold clear standards, rules, and processes to bring value to your business. Besides, vetted data science practitioners can apply new technologies across use cases with consistent results, improving your value from AI investments.
Establish cybersecurity and governance
Cybersecurity is detrimental to your reputation and financial well-being. You need to embed security mechanisms into your AI solutions and ensure compliance with privacy regulations laws (GDPR, PCI DSS, HIPAA, to name a few).
You also need to address the ethics problems. For example, algorithms can discriminate based on gender, race, socioeconomic factors, disabilities, or other conditions. This could lead to biases and, consequently, unreliable results. So, it would help if you had a transparent governance network to ensure the ethics code is correctly translated into the development.
Gather enough data
AI and machine learning systems need a large amount of data to provide accurate results. So, smaller businesses need to collect enough information before their deep learning platform can produce reliable insights.