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According to a survey conducted by O’Reilly, 85% of organizations have already adopted AI or are evaluating it. Some have already implemented it in production processes or analysis. Despite its massive adoption rate, experts think that there is still a long way to go before companies can put their AI efforts on solid ground.
Analysts at O’Reilly also highlighted similar concerns when they said, “Whether it’s controlling for common risk factors – bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production – or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.”
One of the biggest challenges is that AI does not integrate with the processes and approaches businesses are currently using. That is why they must completely overhaul their organization processes and mindset just like mobile app development company Toronto did. To do that effectively, you need an AI strategy. How can I create an artificial intelligence strategy that works? That is exactly what we will discuss in this article.
In this article, you will learn about seven unique tips that will help you create a winning artificial intelligence strategy.
1. Slow and Steady Wins the Race
Most businesses adopting AI rush through to adopt it. You might think of this as a good thing, but experts have different opinions on this. According to Dr Jerry Smith, Vice President of Data Sciences, Machine Learning and Artificial Intelligence at Cognizant, “Modern AI has high IQ but low EQ.” He warns,“If you get data and use AI to analyze it and learn from it without emotion and do it at scale, you’re basically turning a psychopath loose in the system.”
Business and IT leaders should sit together and decide what goals they want to achieve with artificial intelligence. Most businesses and mobile developersimplement AI to save them from numerous challenges and issues but if they do not set up the right framework and strategy, it could do more harm than good in the long run.
2. Prioritize Culture and Skills, Not Tools
Shawn Rogers, Vice President of Analytic Strategy at TIBCO said, “Technology is often where companies start if they are trying to innovate, and that’s not surprising but turning your back on the people and culture aspect will certainly doom you to failure.”
If you want to succeed in today’s business world, you need to learn new skills and polish them from time to time and adopt an organization culture which puts more emphasis on taking action. This allows you to take full advantage of machine learning and artificial intelligence technologies. Even if you have the best tools but don’t have the skills and organizational culture ripe for AI adoption, you will struggle with AI implementation.
3. Prepare for Scalability
When you are just starting out, adopt a few models by using a defined group of data. Start small and take it from there. As you continue to scale, you might soon end up with dozens of even hundreds of different models. That is not all, you would be dealing with multiple authoring environments, which would create new challenges especially when it comes to putting together the right data science teams to keep up with ever expanding demands of the organization.
4. Look for Bias at Unexpected Places
It is easy to get your head around the relationship between input and output in artificial intelligence. The tricky part is to look for bias in areas where you least expect it. Most people will blame AI for biases and hate to see biases in AI and machine learning algorithms but what they don’t realize is that human underwriters who are there to evaluate the risk also have some kind of bias. After all, the person who is building the whole AI based system is also humans and humans have bias. Despite this, very few businesses ever pay attention to this as a result, they fail to mitigate bias at a data level.
5. DevOps is not Enough Anymore
Just because you have adopted DevOps does not mean that they can relax now. DevOps is important for AI adoption, but it is not enough. Businesses must look beyond that and integrate MLOps early. According to George Mathew, client partner of Fractal Analytics said, “This integration needs to be planned early on in the application lifecycle and followed throughout the phases.”
He also thinks that businesses will have to create additional pipelines which allows them to compare insights generated from AI models with actual numbers acquired from on field research and surveys. This will help you identify discreteness in data so that inaccurate data do not become a part of AI models.
6. Train and Improve Models in Iterations
For beginners, it is important that you start with use cases in mind. Even though most AI and machine learning based use cases evolve with the passage of time in an iterative way, according to Ashish Thusoo, who is the CEO and co-founder of Qubole. Organizations should focus on continuous data engineering and give programmatic and database access in order to train and deploy models.
7. Be Ready to Explain Yourself
Explainable AI is the new branch of AI which allows humans to better understand, trust and manage artificial intelligence. This will put more organizations in trouble as regulatory audits will ask for more details. They should be ready to answer questions such as
- How is the AI model training conducted?
- What data sets were used for this purpose?
- Which model metrics are generated at every stage?
According to George Mathew, “These elements need to be collected and stored throughout the time period that the models run in production – and beyond, for some use cases. The solution architect needs to prepare the architecture to address these requirements, the project leader has to include these steps and deliverables in the project plan, and the data scientists and engineers building the application have to work with this framework.”
You need to provide necessary support to achieve that throughout the system development lifecycle otherwise, you will fail.
Which is the best AI strategy tip you have ever received? Let us know in the comments section below.
