A comprehensive artificial intelligence business strategy can boost business and make the enterprise an industry leader. Let’s look at the round-up of pro tips shared by leaders in the AI industry.
The current market scenario is proof that businesses need to adopt the latest technology to survive the competitive markets. Artificial intelligence has become a necessary step in digitally transforming the business and creating new growth opportunities.
However, the success of an enterprise depends on how well it plans and implements the AI strategy within the business. Wouldn’t it be helpful to have pro tips and advice from experts who’ve been in the industry for years?
This round-up post contains the top tips from twenty-five AI experts known for their domain expertise in various subfields of artificial intelligence.
A separate AI strategy is essential even if a business has a data strategy.
AI-enabled services are a better option for small businesses instead of customization.
Don’t ignore change management challenges and HR issues. Managing company culture is a must.
Start with the pain point and expected outcome.
Add a buffer amount to the budget and work with it.
Focus on edge cases and minimize data noise.
Don’t expect perfection the first time.
AI should be integrated within business workflow.
Start at the beginning and understand the requirements and challenges for AI adoption.
Get feedback from customers and make necessary changes to the system.
The AI business strategy needs to be constantly evaluated and edited to fine-tune the systems.
AI should make users more capable while sounding as human as possible when interacting with customers.
Automation is the primary goal of using vertical AI agents in the enterprise.
Bots and virtual assistants can increase the worth of the employee and the business.
Data scientists should stop overfitting the data, which defeats the purpose of using the algorithm.
Identifying the right problems to tackle within the business is essential for AI implementation to be successful.
AI will keep evolving, and data scientists and analysts need to learn and upgrade their domain knowledge.
AI uses reactive and proactive user experience to provide real-time data analytics for marketers. The insights require a huge volume of accurate data to be fed to the algorithms.
Issues with business scalability can be solved using AI to structure the business model accordingly.
Marketing is much more than human-to-human interaction, and it’s necessary to seamlessly use algorithms, bots, and humans to get the desired results.
No need to invest heavily in AI projects in the first stage. Bet on safer and smaller AI technologies that guarantee results.
Businesses already have valuable data sitting in their systems. They only need an AI team to work on it.
Building AI systems is similar to building a rocket ship. It won’t take off unless the elements are in the right proportion.
AI works in combination with IoT (Internet of Things), Big Data, Augmented Reality (AR), and cloud computing to deliver results.
Align and organize data around the data layers for accurate insights. Move on from older data storage systems.
Start by using ML solutions that are already integrated into the systems. New models can be developed later.
Hire a group of data analysts to decide which AI and ML systems to use. Don’t rely on the judgment of one person.
Cloud-based infrastructure will reduce analytical costs for the enterprise.
It will take time to make the most out of AI systems. Even the leading companies have a lot more to do.
Testing and perfecting an ML model takes months and years. Hire more ML engineers to speed up the process.
Machine learning doesn’t mean the machine is learning. It only provides the necessary numbers at a great speed.
Implementation errors in ML can be impossible to find unless various baseline models are implemented which beat the new model and show the error.
Focus on ‘boring’ business problems to know how much AI can help find an easy solution.
AI should be used to overcome challenges rather than as a prestigious investment for the enterprise.
The spend-first-plan-later approach by businesses will lead to a higher risk of AI failure.
Chatbots and virtual assistants empower employees with self-servicing and increase customer experience.
Messy datasets are the ones that deliver results. Businesses should train their data analysts to work on similar sets.
The data-centric approach is a great way to train AI systems to be more accurate in their predictions.
Data literacy training is compulsory for employees to ask the algorithms the right questions.
It should be continuous, individualized, and asynchronous.
Enriching the data in the enterprise will make it easier to build an ML model.
Choosing the right machine learning model is the responsibility of humans.
GUI-based data visualization tools have become more popular over the last few years and deliver better reports.
ML algorithms can be used to analyze most types of data.
Chatbots, virtual assistants, and tellers are a great way to manage customer service in various industries like banking, retail, etc.
Drones can be effectively used for smart services and linked with IoT to share real-time information.
Smart services are the way to drive the economy for an enterprise. That’s possible with AI technology.
Understand how to create an intelligent bot that can do the job it’s supposed to do.
Digitalism, when done right, can bring transparency and democracy into the system.
Creating a combination of short-term and long-term goals is necessary to decide the type of AI systems required.
A system isn’t intelligent on its own. Enterprises should always take proper measures to deal with the errors.
AI technology is not as abstract or automated as it seems. People are still very much necessary to ensure automation is going smoothly.
ML algorithms are not 100% perfect. The results depend on the data feed to the algorithm. If the data is biased, the results will also be biased.
A machine can predict the future when the future is not too different from the past.
Identify the right type of data to get the desired results. Better data is useful than more data.
There is no best ML algorithm available in the market. Enterprises should work on what’s best for their requirements.
Big data is the technology used to analyze data and a must for every enterprise to improve its capabilities.
A model is an extraction of reality that tracks complex information available to it.
AI is much more than ML and deep learning. They are not the same. ML and DL are entry steps into AI.
Machine learning is neither art nor science. It is craftsmanship that includes people from different levels (end-user, developer, enterprise).
Even today, not many small companies want to automate their routine tasks where they cannot accurately measure ROI.
Even if data science is automated, the world will still need data scientists to automate the systems.
Data volume, velocity, and variety are crucial factors to consider when dealing with big data.
Understanding the limitations of the current AI systems is important. That way, enterprises can know where and how to use AI.
Write down the legal documents about why the business is using AI. The purpose should be clear for every employee.
Enterprises and customers are thinking big. Technology is making it happen through cloud services.
Focus on your business vision to know what kind of AI is required in the enterprise.
The HR teams need to be provided with the right metrics to hire talent. Data science requires a combination of skills.
Company culture is a major bottleneck for AI implementation.
The business processes and systems should be aligned to get the results.
A majority of the enterprises are using supervised learning ML algorithms in their businesses.
Machine learning is math and not magic. It is a collection of mathematical techniques.
Data literacy and infrastructure is required to adopt AI in an enterprise.
Custom-building AI pyramids will work only when you take care of data cleaning.
AI is not limited to technology. Employees and management play a vital role too.
AI is not limited to the IT sector. It can be used almost anywhere, even in humanities. AI in the medical field is working wonders.
AI is yet to compete with human ability. It currently works with humans to deliver results.
Many SMEs hire offshore experts to develop an AI business strategy by overcoming the challenges and minimizing risk factors. According to the survey, 40% of the enterprises are developing proofs-of-concept for personalized AI applications, while another 60% are using pre-built apps like virtual agents and chatbots.
It helps them make calculated investments in AI systems while increasing the success rate. A properly planned and implemented AI strategy will put the business at the top of the charts.