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Navigating AI in Business: Moving Beyond “Using AI”

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In today’s rapidly evolving business landscape, the role of artificial intelligence (AI) has shifted from being a mere buzzword to a strategic imperative. However, if the goal is simply to be “using AI”, we risk missing opportunities and wasting resources. To truly harness the power of AI, organizations must shift their mindset from vague aspirations to actionable goals. Let’s explore a more meaningful approach—one that transcends simplistic adoption and focuses on process enhancement. 

How to Approach AI in Your Business: Moving Beyond “Using AI” 

To keep pace with the current fast-moving AI market, some organizations are tempted to deploy AI solutions simply to keep up with competition or perceived demand. Without clear alignment to business goals, those attempts at “keeping up” can be costly misadventures. Instead, organizations should focus on specific objectives tied to business outcomes. Clear objectives, aligned with broader strategies, pave the way for successful AI integration. 

The Essence of Business Software 

At the heart of every business software solution lies the intent to enhance specific processes—making them faster, easier, and more efficient. Traditionally, business teams evaluate their processes to identify areas of heavy manual intervention, risks of time delay, or bottlenecks that prevent scaling. AI solutions should not be any different: AI is a means to improvement rather than an end goal.  

The ML/AI Spectrum: From Rules to Generative AI 

At the risk of oversimplifying, traditional machine learning (ML) and AI have been likened to rules-based decision engines, evolving from a series of if-else statements to sophisticated neural networks. It’s about recognizing when to decide and when to create, harnessing the full spectrum of AI capabilities.  

When applying ML/AI to our business processes, we might focus our attention on critical decision-making moments. ML solutions can process complex relationships between varied inputs to produce high-quality, repeatable decisions to return decisions or recommendations. In turn, these decisions drive actions that further automate our business processes. Examples include: Will this shipment be late? Is this customer happy? What products should we recommend? 

While decision-focused AI optimizes existing processes, generative AI unlocks new possibilities by empowering creation. Our prompts and supporting business context enable the Large Language Model (LLM) to respond with creative output of text, images, video, software and more. Our Generative automation opportunities now shift toward creative tasks that are often time-consuming, mindful operations in our processes. For example: creating new product descriptions, drafting personalized marketing messages, and summarizing product manuals to answer specific questions. 

A Better Goal: “Improving Processes with AI” 

  1. Define Clear Objectives

Rather than a vague mandate to “use AI,” start by identifying specific business processes that can benefit from AI-driven enhancements. Ask: 

  • Which processes are resource-intensive? 
  • Where are the bottlenecks? 
  • What repetitive tasks can be automated? 
  1. Align with Business Outcomes

AI should serve a purpose beyond novelty. Consider how AI aligns with your organization’s goals: 

  • Cost Reduction: Can AI streamline operations, reduce manual effort, and optimize resource allocation? 
  • Customer Experience: How can AI enhance customer interactions, personalization, and satisfaction? 
  • Innovation: Can AI drive new revenue streams or unlock untapped markets? 
  1. Collaborate Across Functions

AI success requires cross-functional collaboration. Involve stakeholders from IT, operations, marketing, and finance. Their insights will shape AI implementation and ensure alignment with broader business strategies. 

  1. Measure Impact

Set measurable KPIs tied to process improvements. Whether it’s reducing response time, increasing conversion rates, or minimizing errors, track the impact of AI interventions. 

Decision vs. Creation: A Strategic Shift 

It’s essential to recognize the strategic shift from decision-focused AI to generative AI. While decision-focused AI optimizes existing processes, generative AI unlocks new possibilities for creativity and innovation. The key lies in leveraging AI capabilities to enhance both decision-making and creative endeavors. 

As businesses navigate the complexities of AI adoption, the focus should be on purposeful enhancement rather than merely “using AI.” By aligning AI initiatives with clear objectives and business outcomes, organizations can unlock the full potential of AI to drive innovation, streamline operations, and enhance customer experiences. Whether it’s improving decision-making or fostering creativity, AI stands ready to transform the business landscape when approached with clarity and purpose. 

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Robert Bagley

Robert is an experienced data practitioner specializing in data strategy and architectures that enable business analytics, product performance, customer experiences, and applied machine learning.

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