Transformation Consulting

Operations Pain Points Solved: Transformation in the Age of Artificial Intelligence

This blog is part of a series about the operations pain points that many organizations face as they tackle digital transformation, data transformation, and change management. Our experts provide insights and recommendations based on their decades of hands-on experience and tackle some of the most pressing business and technology pain points.

In the few short months since, “Operations Pain Points Solved: Transformation for the Future,” was published, artificial intelligence (AI) has come to the forefront. OpenAI has not only created some of the most advanced AI interfaces that the world has seen up to now, but more importantly, it has created AI in a way that makes it accessible to almost anyone.

Other technology companies such as Google are certain to provide similar AI options in the very near future. So, what does this mean for organizations who are considering digital transformations? What does it mean for IT professionals?

Will AI make my job obsolete?

People are worried. Will AI be able to do a technology architect’s job? Will it replace whole teams of IT professionals?

No and no.

AI’s current maturity allows it to be used as a dependable partner in any IT organization, but it’s not going to replace entire ecosystems and the professionals that run them. It can help with arduous tasks such as archiving and categorizing network data and logs. It can also help automate time-consuming and repetitive processes, make sense of complicated data, and learn from its environment to create efficiencies.

AI will encourage teams to rethink the distribution of work so that IT professionals can focus on important projects and tasks for their roles as AI takes on the tasks that are suited for automation. In doing this, AI can help short-staffed teams close their skills gaps.

Many IT environments already incorporate AI in some form, and that will only continue as the quality and capabilities of AI grow. The future of work is about strong human-machine configurations.

Does AI know everything about my operations?

Again, no.

What makes AI indispensable is its infinite ability to learn. It takes data from locations as instructed and uses that data to inform decision-making and actions. But AI is only as good as the datasets used to train it.

There are many areas where AI lacks sufficient or accurate data because it’s only using the data of what has already occurred as it makes new decisions and predictions. Examples of this are the bias in mortgage lending and in many areas of healthcare, which will continue if solely dependent on AI.

Remember that AI simulates human thought processes, and automation is a set of predetermined rules or programs to follow without human intervention. Although often used interchangeably, they are not the same thing. Just as automation must be programmed to perform functions, AI that is integrated in technology used within IT ecosystems must be modified and trained with an environment’s datasets to perform the desired functions.

Because every IT environment is nuanced and unique, out-of-the-box technology that includes AI must learn from that environment to perform the specific set of functions needed within an organization’s operations.

How do I know if my organization is AI-ready?

According to Science Direct, an AI readiness framework can be a helpful tool for organizations to visualize their posture in four key areas: technologies, activities, boundaries, and goals. A questionnaire is used to determine current and future desired states in these areas, and the results are noted in a circular scorecard.

The simple review of current and desired future state allows organizations to quickly determine where they stand or at minimum start considering their strategic vision for each area. The competitive advantages to be gained from machine-learning initiatives, regardless of industry, make it a must to be AI-ready.

What are the types of advantages that can be gained from AI-powered processes?

It’s not all about chatbots and product recommendations; there are many ways that AI can support business growth. If you’re having trouble picturing exactly how AI can help your organization, consider the following examples.

  • Scale: streamline processes, set up business functions and services globally, establish automations, save labor hours, and save costs
  • Enhanced decision-making: incorporate design thinking into processes, create or optimize a data and analytics system, create sales and industry forecasting, act quickly to maximize ROI faster
  • Operating model: develop or improve dashboards, increase speed of data delivery, increase accuracy of data, improve information dissemination to the areas that can act upon it
  • Data management: maintenance of data repositories, machine learning model infrastructure management, risk management

The above is not a comprehensive list, but the examples show a broad sense of the possibilities of AI within any IT environment.

What about AI in robots?

Artificial Intelligence already powers autonomous machines that are helping manufacturing and delivery processes across industries. But there are also many other products incorporating AI, and the list of capabilities is only limited by imagination. AI is not just for manufacturing anymore; it can power intelligent processes in everyday products, too.

If you look at some of the products at CES 2023, AI is enhancing everything from washers and dryers to aquariums. For example, there are products that help diagnose or treat medical conditions, wearables that help fight fatigue, and appliances that can optimize cooking settings.

Given that a robot is technically a machine that can carry out a complex series of actions, whether control is guided, programmed, or embedded within, then we’re already in the age of robots even if they don’t all resemble the ones in the movie, “I, Robot.”

Want to learn more?

The “Operations Pain Points Solved” series highlights common issues faced by organizations everywhere. Read the other blogs in this series to learn about establishing a target operating model, optimizing the customer experience, data transformation planning, managing people, and more.


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