3 Strategic Mistakes That Leaders Easily Avoid When Thinking About AI Integration

A few years ago, a product manager at a technology company had a Data collection problem: Extracting software security vulnerability data from multiple web sources, consolidating vulnerabilities and storing them in a database.

Since this was an automation problem related to data, the Product Manager (PM) immediately concluded that it was A machine learning problem. Premier then “hired” the company’s data science team to build ML models to solve the problem.

The data science team agreed to the data collection task without any promise of “models”. They realized that their attempt to teach the Prime Minister that this It was a simple scenario (not a sophisticated ML model) would be a losing battle because there was a big internal push to use AI and the PM was sold on the idea.

Several weeks went by and when it came time to “deploy” the models, the model was not deployable. It’s just a software script that constantly reads specific web pages, heuristically cleans the records of security vulnerabilities and fills them in the database.

Although the PM was ultimately informed that none of the ML models were used or necessary, the scraping software was sold to the entire company as a security solution for providing machine learning.

This is not uncommon.

Such confusion Around artificial intelligence and where it is best used, it happens more often than we think. In the case of this tech firm, the confusion didn’t do much damage because it was a small project and the only thing lost was the data science team’s valuable time during those few weeks.

In many other situations, such miscategorization, poor understanding of AI, and misuse of resources can be detrimental Extremely expensive.

Imagine if the data collection problem was above Forced to use machine learning, although unnecessary. An ML solution costs much more to maintain than a simple software script. In addition, if the project lasted for a full year, the data scientists would be paid to solve a problem that a single contracted software engineer could solve. Most importantly, these data scientists could work on high-impact AI initiatives.

Based on this story, let’s break down three strategic mistakes that leaders can easily avoid to avoid confusion, reduce waste, and ensure that you truly reap Benefit from AI.

3 Mistakes Leaders Can Avoid When Thinking About AI Integration

A summary of AI leadership mistakes to avoid

#1: Waiting for “others” to understand AI

In 2018, industry research firm Gartner made a bold prediction that 85% of AI projects will “fail”. This is a shocking prediction, given how important AI has become in recent years.

One reason for this prediction is confusion among leaders about what AI is and what it can do.

That being said, your technical teams need to understand AI. However, executives, technology leaders, and product managers looking to make AI an integral part of their business must also be tech savvy.

We are not talking about developing an artificial intelligence model. Regardless, you need to be proficient in AI correct level Be comfortable exploring the possibility of using artificial intelligence to solve business problems.

Furthermore, this AI knowledge can be useful in several ways.

• Closing AI Adoption Gaps: Once you understand artificial intelligence, you will begin to see the building blocks to prepare your organization for approval. You’ll begin to notice gaps in your company’s infrastructure, cultural readiness, and talent pool, allowing you to develop strategies to lay the necessary foundation.
• Selection and recruitment of the seller: Knowing AI will also help you when talking to AI vendors and job candidates, where you can ask the right questions, separate the good from the bad, and make the right purchasing and hiring decisions.
• Maximizing Investments: A broad understanding of AI can also help you use the right thought process and frameworks to assess which problems will benefit the most from AI, helping you solve the rest of the problems with alternative approaches. By doing so, you increase your chances of seeing meaningful results from AI.

Action: If you are a new AI leader, Start by building a foundation Around understanding AI use cases, what it is and what differentiates AI initiatives from traditional software engineering. understanding Misconceptions about the field and how On-site opportunities It will also significantly help identify high impact use cases.

Some of this information can be obtained by reading relevant books as well as industry reports from large consulting firms. Attending AI leadership workshops and presentations can also be helpful. a podcast? I don’t recommend podcasts to build your foundation. The scattered nature of podcasts can be confusing and should be additional knowledge once you have a general foundation.

#2: Expecting quick financial returns from AI

Yes, AI holds the promise of cost savings and increased revenues. While you may see an immediate financial impact on some problems, in most cases, you may never see a noticeable financial impact from AI with just one initiative.

It may take a number of related initiatives to change your financial trajectory, or it’s something you’ll look at over the long term.

So when it comes to the ROI of AI, you need to focus on the benefits (short and long term) of using AI. Ask these questions:

• Ра An instant pain point Will an AI solution make it easier for your organization?
• Ра benefits Will you see the pain point addressed?
• what is An additional advantage An AI solution over a simpler one like manual?

The answers to such questions will clarify why AI is necessary and help you track the right business metrics.

Action: When you try to track the success of AI, It always starts with a metric that relates to its direct impact first. Once that’s done well and you’re getting results, track metrics related to long-term results that can take months or even years to observe.

#3: Leaving AI entirely in the hands of data scientists

In their quest to adopt artificial intelligence, companies often start by hiring a team of data scientists. This happens before leaders understand AI or have an AI strategy.

These data scientists then mine the data to discover potential AI capabilities. While a few identified projects may be important, many are more suitable for publishing a research paper – and not so much for creating value for the business.

This is not entirely the fault of data science. Data scientists brought in to solve AI problems for a company will have limited insight into the company’s business challenges.

Analyzing the data tells them nothing about the current process and workflow inefficiencies in the company. Additionally, your company may not be collecting data on the problems that would benefit most from AI. Instead of twiddling their thumbs, these data scientists are left with no choice but to solve “created” problems with relevant data.

Rather, business unit leaders, executives, and domain experts deal with the organization’s day-to-day challenges—whether it’s customer complaints, media coverage issues, or friction in your business processes.

These employees must be equally adept at automating and augmenting workflow capabilities with AI. They need to feel empowered to solve relevant business problems for data scientists.

For companies to succeed with AI, there must be deep collaboration between business leaders, domain experts, and their technical counterparts.

Action: When you are a witness Process inefficiencies, repetitive manual tasks, and lagging accuracy About existing software systems, start a note. Ask your team to track current baseline performance numbers and determine if the problem is related to solving a difficult decision-making task. Such problems are often excellent candidates for AI. Engage your technical experts to help further explore the problem and determine whether AI is a good fit and, if not, recommend alternative approaches.

How will you accelerate your AI adoption?

While many leaders believe that the success of AI adoption lies in the excellence of their technical teams, in reality, it starts at the top.

Executives and functional leaders deal with the day-to-day challenges that the organization faces. With a good understanding of AI, they are better placed Identify problems that AI will solve and accordingly Funds impactful initiatives. This, together The right expectations and success metricstranslates into better results for the organization.

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