5 AI Strategies To Overcome Business Challenges

Technology has made it possible for companies everywhere to re-think how they are doing things, and establish new strategies that will help them stay competitive. AI can enable companies to do things better, fast

er, and at a lower cost. But it’s important to remember that simply having AI technology isn’t enough for true impact—companies must be strategic in how they implement those artificial intelligence solutions. That means developing an overarching enterprise strategy that aligns with your company’s business goals is of utmost importance. Otherwise, you are likely to experience challenges that can impede your progress.

This post will explore five AI strategies that you can use to overcome common business challenges and make your business better equipped for the future.

How AI Strategies Can Help Overcome Business Challenges

AI technologies can create opportunities for businesses to improve the way they support their customers and drive revenue growth in the following five ways –

  • Bringing Efficiency To Marketing Through Automation

Businesses can use enterprise ai solutions to help maximize their marketing budgets and find which strategies deliver the highest return on investment. But it takes a lot of time to monitor and analyze data across all the media channels. Here, AI marketing solutions come into play!

AI can generate better insights into customers’ behavior, preferences, and needs—helping marketers understand audiences like never before. For example, artificial intelligence development services can help companies collect, analyze and act on social media analytics in real-time for direct marketing; personalize online content; leverage first-party data to provide more tailored experiences, and enhance the customer experience by personalizing interactions.

  • Using AI Techniques to Keep Up With Competitors

It is always crucial to keep track of what your competitors are doing. Unfortunately, most business owners are not able to review the competition due to busy schedules. Here, the role of AI comes in.

Artificial intelligence solutions that use machine learning and natural language processing tools to identify the competition can help business owners compare their strategy with that of others in a fraction of a second. Businesses can make better decisions based on this information to avoid wasting time and money on strategies that won’t work for them.

There are various competitive analysis tools like Crayon. They track competitors with the help of different channels like websites, social media, and apps. Moreover, they provide business owners with a close look into any changes in competitors’ marketing planning like price changes, subtle message modifications, and PR activities.

  • Driving Customer Engagement Through Chatbots

Businesses can take advantage of chatbot technology to increase customer engagement. These bots are also responsible for collecting information, finding answers to inquiries, and resolving issues—which frees up employees to focus on tasks that require more human interaction.

AI chatbots like the IBM Watson platform support enterprise-level needs by combining automated insights with chatbot services like speech recognition, natural language processing, machine learning, and reinforcement learning. It is able to collect information from different sources like databases, files, websites, etc., understand the content through text analytics capabilities, provide answers with the help of business rules or external service calls and take actions on insights.

  • Gain Actionable Insights Through Predictive Analytics

AI-powered predictive analytics can help companies predict what might happen next with their business—helping them decide the best course of action. The results are often presented as prediction models, offering clear insights into which actions should be taken next to achieve the desired outcome. Along the same lines, AI algorithms can make suggestions to improve certain business processes like demand forecasting. 

Here are some examples of the different predictive analytics tools, which you can try for your business – 

  1. Algorithm-Based Predictions: These solutions apply machine learning and artificial intelligence techniques to create models that predict what is likely to happen in the future based on the data available. 
  2. Automated Alerts: These solutions send automated alerts to decision-makers when something unusual occurs in a particular dataset, for example, a spike in churn rate or a large number of customer service calls from a certain region at a specific time of day. 
  3. Prescriptive Analytics: These tools use AI algorithms that learn from the available data and present what is likely to happen next in certain situations based on that information. achieve the desired outcome.
  • Using AI to Increase Employee Productivity

Enterprise ai solutions are expected to eliminate repetitive tasks and focus on improving strategic decision-making.

For instance, AI-powered business process automation (BPA) solutions facilitate workforce planning with the help of complex algorithms that can optimize big data sets to come up with better forecasts. This helps businesses eliminate redundancies and allocate their staff more efficiently for long-term growth.

To provide employees with enhanced security, AI can be used to create automated security risk alerts. These smart models are capable of continuously monitoring changes in the environment around an organization—allowing businesses to take proactive steps to safeguard their interests before potential threats can emerge.


Based on the above points, we can see that AI’s impact goes beyond any single function within an organization. However, it is vital for companies to take a strategic approach when implementing AI solutions so they can realize the greatest benefits from this technology. 

To get the most out of AI, businesses must first identify which technologies have what functions and create a prioritized portfolio of initiatives based on business needs. If businesses focus too much on tactical implementations without considering the big picture, they are likely to end up overwhelmed by the sheer volume of data that needs to be analyzed.

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