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Artificial intelligence is shaping digital life and augmenting human capacities all while presenting previously unimagined opportunities. While some see it as a threat to human autonomy, businesses implement AI to operate smarter and faster. As emerging algorithm-driven artificial intelligence (AI) proliferates, are we better off than we are today?
Artificial intelligence has made its way into businesses in nearly every industry, and it’s only continuing to rise in popularity. When utilized correctly, AI can be an incredible productivity booster and provide valuable insights to business leaders. However, many leaders are unsure of the best way to leverage it, and a misdirected AI initiative may cause more harm than good.
If you’re looking to implement artificial intelligence in your business, make sure you go in with a well-considered plan. Below, 13 experts from Forbes Technology Council share common mistakes to watch out for when implementing AI.
Source and Photo Credit: Forbes.com – Members of Forbes Technology Council share common mistakes businesses make when implementing AI initiatives. PHOTOS COURTESY OF THE INDIVIDUAL MEMBERS.
1. Adopting Too Many Tools At Once
The biggest mistake I see tech business owners make when implementing AI is trying to adopt too many different tools at once. AI is a delicate tool that can provide tremendous value to your business, but you have to be attentive and improve it. Some people think AI is “set it and forget it,” so they implement many different AI programs at once and ultimately don’t see positive results. – Thomas Griffin, OptinMonster
2. Not Having A Clear Objective
You must first define the problem you are trying to solve and how you will measure the impact of a solution. I’ve seen too many companies start AI initiatives without clear objectives, hoping to find something. This is usually a recipe for failure. – David Vasko, Rockwell Automation Inc.
3. Not Having A Single Source Of Truth
As data-driven solutions proliferate, the thing that is most needed is a single source of truth that the data can be funneled into. You can then use AI to drive models and build solutions. More often than not, this is forgotten amid the enthusiasm to build AI-driven models and solutions in silos. – Lydia Miller, TATA Consultancy Services
4. Not Analyzing Enough Data
It’s important to remember that AI is only as good as the data it ingests. If you aren’t analyzing a sufficient amount of data with your AI engine or if the data isn’t reliable, the outputs won’t add much value. Another challenge is continuously training the algorithm in production and providing feedback. Doing this manually on huge amounts of data isn’t possible. Automation is needed. – Hed Kovetz, Silverfort
5. Incorrectly Structuring Datasets
The foundation of any successful AI initiative is a well-structured dataset. If you’re an entrepreneur or small business, you likely won’t start off with sets of data large enough to make AI effective. Avoid making the mistake of not structuring your datasets correctly from the beginning, as it will be difficult to do so when you have a sufficient volume of data. – Maddison Long, CloudOps
6. Implementing Siloed Solutions
Some businesses implement siloed AI solutions or proofs of concept before aligning on a broader transformation strategy and cultural readiness. Such an approach significantly limits scalability and return on investment, even if it does not cause direct harm. – Didem Un Ates, Microsoft
7. Not Having The Right Size Team
Most businesses know that AI solutions pack a lot of power, but many still forget about the complexities it brings. Implementing AI requires the right-sized team to always keep your algorithms in their best shape. That’s why many companies prefer to outsource their AI development projects or scale their AI development teams via on-demand staff augmentation services. – Nacho De Marco, BairesDev
8. Not Doing The Necessary Groundwork
AI is often seen as a “silver bullet,” but any AI initiative requires a solid data foundation. Working with Fortune 500 enterprises, we see leadership pushing to implement emerging technology. However, any team needs to go back to basics first and make sure their data is clean and structured in a way that will be useful to the model and real-world application. – Michael Paladino, RevUnit
9. Assuming AI Is A Catch-All Solution
Too many businesses assume that AI is a panacea for all of their problems. Consequently, many of them erroneously jump on generalized tech trends without thinking it out. I’d recommend the opposite approach. Focus on a particular purpose for your AI. Strive to address a specific problem. Then decide if you can employ an off-the-shelf solution or if a custom one is really required. – Marc Fischer, Dogtown Media LLC
10. Misidentifying Both The Problem And The Best Solution
“Automation,” “machine learning” and “AI” are often used interchangeably. Leaders may misidentify the business problem; further, whether an issue can be solved via ML, AI or the best automation candidate within the business is still foggy for many business leaders. Identifying the right business problem, choosing the best tool or platform, inputting the required data sets, and finding the key partners to deliver are the four pillars of AI success. – Soumen Chatterjee, Wipro
11. Implementing AI For Its Own Sake
AI is a technology like any other. We’ve seen many projects fail because businesses wanted to implement “buzzy” technology and used that as a starting point. A more effective way to begin an AI initiative is with a really clear view of the fundamental business problem you’re looking to solve and work backward from that. Focus on a specific solution and use AI in service of that. – Konrad Feldman, Quantcast
12. Implementing Solutions Without Sufficient Data
Lots of data is required for most AI solutions to work well. I also see people assuming that a generic AI solution is appropriate when an industry-specific or use-case-specific solution is the right answer. Implementing AI can be hard, but it can provide some incredible value when it’s done right. – Michael Fulton, Expedient
13. Thinking AI Is ‘One-Size-Fits-All’
AI is not “one-size-fits-all.” It is a buzzword, and too many people think it will make them look advanced on the tech front. Utilizing AI for data-driven projects is where the success is. – Bhavna Juneja, Infinity, a Stamford Technology Company
Source: Forbes.com 13 Common Mistakes That Can Derail Your AI Initiatives
In Conclusion
AI initiatives hold immense potential for organizations across various industries. However, they can also be prone to a range of common mistakes that can derail their progress. It is crucial for businesses to be mindful of these potential pitfalls to ensure the success of their AI initiatives. Not having a clear strategy and objectives can lead to a lack of direction and wasted resources.
Failing to allocate the necessary budget and expertise can hinder the implementation and utilization of AI technologies. Another common mistake is underestimating the importance of data quality and availability. Without accurate and accessible data, AI algorithms will fail to deliver meaningful insights.
Additionally, organizations should be cautious of overlooking the ethical implications of AI, such as bias and privacy concerns. Finally, a lack of stakeholder engagement and change management can hinder adoption within the organization. By avoiding these common mistakes and prioritizing careful planning, businesses can maximize the potential of their AI initiatives and drive successful adoption and integration.
FAQs:
1. What are AI initiatives?
AI initiatives refer to projects or endeavors that involve the implementation and use of artificial intelligence technologies and solutions to achieve specific objectives or business goals.
2. Why are AI initiatives important?
AI initiatives can provide organizations with numerous benefits, such as improved efficiency, enhanced decision-making capabilities, cost savings, and the ability to automate processes that were previously manual or time-consuming.
3. What are the common mistakes that can derail AI initiatives?
The following are some common mistakes that can derail AI initiatives:
- Insufficient data quality and quantity
- Lack of clear objectives and use cases
- Failure to align AI initiatives with business strategy
- Ignoring ethical considerations and potential biases
- Underestimating the importance of change management
- Overlooking the need for continuous monitoring and improvement
- Not involving key stakeholders throughout the process
- Choosing the wrong AI technology or solution
- Underestimating the time and resources required for implementation
- Failure to address regulatory and compliance requirements
4. How can insufficient data quality and quantity derail AI initiatives?
Insufficient data quality and quantity can hinder the accuracy and effectiveness of AI models and algorithms. AI systems heavily rely on data to learn, train, and make predictions. If the data used is incomplete, inaccurate, or biased, it can lead to flawed results and unreliable outcomes.
5. What is the importance of clear objectives and use cases in AI initiatives?
Clear objectives and use cases help organizations define the purpose and desired outcomes of their AI initiatives. Without clear objectives, it becomes challenging to measure success or determine whether the AI solution is addressing the organization’s needs. Use cases provide specific scenarios where AI can be applied, guiding the implementation and ensuring it aligns with business goals.
6. How does aligning AI initiatives with business strategy contribute to success?
Aligning AI initiatives with the organization’s business strategy ensures that AI is being used to address strategic priorities and support broader goals. When AI aligns with business strategy, it becomes easier to secure executive buy-in, allocate resources effectively, and demonstrate the value of AI initiatives to stakeholders.
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