5 Questions To Ask IT Leaders Before Shaping Artificial Intelligence Strategy
AI strategy is required in every business. Below are the 5 significant questions to ask development teams before finalizing any AI strategy.
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The whole world is accommodated with artificial intelligence in this digital-oriented world. Artificial intelligence (AI), which is now swiftly becoming commonplace, came into being only a few years ago at the most cutting-edge companies. The knowledge and experience have not kept up as innovative hardware, software, and frameworks are brought from hype to mainstream. From better business decisions to smarter products and services, artificial intelligence is powerful enough to change almost everything.
Artificial intelligence has the potential to revolutionize business dimensions in all possible aspects. It is not a surprise to have an insight that artificial intelligence market dimensions are expanding drastically. The latest analysis depicts that in 2019, the market size of artificial intelligence was estimated at $27.23 billion. This figure is projected to reach the figure of $266.92 billion by the year 2027.
That’s why AI strategy is required in every business. Below are the five significant questions to ask development teams before finalizing any AI strategy.
Question 1: What is Your Main Goal When You Consider Utilizing AI Tools?
Artificial intelligence is such a vast field that it covers a wide range of tools, approaches, algorithms, definitions, and solutions. For example, there are concealed approaches for example supervised and unsupervised machine learning and rule-based algorithms. There also exist packages above these approaches, from Optical Character Recognition (OCR) to Natural Language Processing (NLP).
It is mandatory for developers to ensure that they are utilizing the appropriate tool for the appropriate problem. GPT, BERT, and numerous other complex neural network approaches might also be under discussion and having the limelight but that does not mean at all that this is an appropriate tool for most of the use-cases. IT leaders should ensure that they are selecting AI tools according to their problem, and not according to the publicity.
Question 2: Do You Understand Your Problem Well?
As having the right quality and quantity is essential, machine learning algorithms are implied in learning from data. The focal point of artificial intelligence is to imitate human intelligence in the best possible way. It is important for the development team and innovation leaders to have a better understanding of the problem. Development teams should answer the question: how self-assured are they that AI tools will meet or exceed the expected level of accuracy and efficiency?
Your developers will likely require advice from subject matter experts if they do not have enough knowledge about the concerned AI domain. These experts will be required to work with engineers and data scientists to tune, craft, and evaluate models. Hence it is mandatory to designate an expert with proper knowledge that is capable to answer all the questions of a development team.
We should not fail to recall that even experts can make mistakes. That’s why I don't expect AI systems to be perfect enough to address non-trivial problems. Artificial intelligence may be compatible enough with humans in chess, Go, and Jeopardy, but these limited domains are aberration rather than the rule.
Question 3: Artificial Intelligence Depends on Good Data. What Data Do You Have?
Machine learning algorithms require a vast amount of data for the development of accurate statistical models. Algorithm enhancement depends only on data quality. Data requirements can scale from thousands to millions of examples depending on use cases and algorithms. Checking data availability, data quality and ensuring that data does not include biases is mandatory.
Numerous innovation leaders believe that they have an enormous amount of untapped valuable data mined by AI algorithms. Most of the organizations maintain transactions, customer information databases, and so on but most of the time that data is inconsistent for the training of AI systems you want to address. A complete assessment of the available data is a pre-requirement for any AI venture. Data preparation can consume up to 90% of the development effort in an artificial intelligence project. That’s why ensuring data validation should be given high priority from the very beginning.
Question 4: Have You Scoped The Required Compute Resources?
Ask yourself once. Have you expanded the required compute resources if you are preparing to host your enterprise in the data hub? AI tools, for example, machine learning algorithms can be compute-intensive and may require extra hardware such as Graphics Processing Units (GPUs) for process load handling.
If you are considering acquiring cloud computing such as Amazon’s Comprehend or Google’s GoogleML, ask yourself, have you extended the costs? What are the information security issues with respect to sending user data outside the firewall? Hardware resources are required in those organizations that are new to AI for the training and deployment of AI models. Innovation leaders should ensure that they not only have compute resources but also have clearance to transfer data for the training of AI systems.
Question 5: How Long Will It Take To Find The Solution?
AI solutions require model training and testing along with best practices for solution development and application tools. Training and testing models is a lengthy process that can take up to a couple of weeks, months, or even years.
Precision: What percentage of false positives is bearable?
Recall: What percentage of missed targets is allowable?
How will updates be handled once the solution comes into being? Does the model require complete testing and retaining? How to ensure the model’s integrity once it is in production? Periodic reevaluation and tuning are required because variations in live data can occur over time.
AI models can possibly make mistakes so a backup plan is required in case of occurrence of false predictions. AI systems require continuous evaluation, tuning, updating, and maintenance just like every other traditional software after the completion of core development.
The Bottom Line
Significant preparation is required in terms of engineering efforts and embracing the right mindset within the organization for setting up an AI project that is prone to success. It is obligatory to stay realistic about the expectations that artificial intelligence can achieve. Even if your system is deployed on the fastest and expensive processors, this will not mandatorily perform or even meet the requirements of accuracy and efficiency of the human subject matter expert it’s intended to imitate.
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