Three years before ChatGPT changed the intelligent-machine paradigm, analytics, AI, and big data projects failed a whopping of the time. And of those 15 percent of the projects that reached deployment, only of them ended up turning a profit. 85 percent 60 percent And today? With all the rapid advances in fields such as , deep learning, and natural language processing (NLP), are businesses still struggling to successfully leverage AI? machine learning (ML) Why AI? Before answering this question, it would be helpful to understand why organizations are so interested in AI and how they might go about using it. Waves of businesses and organizations are already seeking out AI tools to help improve their efficiency. The options are really endless—generative AI tools can code software for a new digital service or develop a service-oriented chatbot to improve the UX. Other businesses may want to use ML for image and speech recognition to build a security tool. And ultimately, decision-makers across all industries have their eyes on these tools due to their potential to boost revenue and cut costs. Goldman Sachs predicts generative AI alone can drive a rise in global GDP over a 10-year period, and some might consider this forecast to be on the conservative side. 7 percent That doesn’t mean businesses should fall prey to the hype, which definitely happens. Many companies get overly excited about buying and playing with a new tech toy that they overlook how it will affect their immediate and long-term roadmaps and business models. That’s a mistake. Without a deep understanding of available and how they fit within a given business’s IT architecture, its benefits will be minimal at best and possibly even detrimental. AI solutions Without a solid integration plan, AI adoption will likely hinder the company’s momentum and create internal confusion and complications—not to mention waste time and money. Preparing an AI nest Not unlike preparing your business for a digital transformation, AI integration requires getting your house in order to maximize the tech investment. While vastly different from the digital migrations of the last decade, successfully leveraging AI requires a similar company-wide commitment, and it can't be done by cutting corners. Here are the core processes and stages that can be the difference between a successful AI initiative and a failed one. Establishing the foundation To successfully integrate AI solutions, a business leader can’t skip over the basics, like having a deep understanding of the company’s business model, objectives, roadmap, consumer needs, and use cases. Here, a thorough audit of the entire digital ecosystem and tech stack is equally important for a successful integration. This involves an assessment of a business’s complete technological capabilities, resources, processes, and data sets to identify any gaps or bottlenecks that could disrupt a smooth integration into the current system’s infrastructure. Readiness gaps could stem from a lack of skilled data scientists and AI experts, a lack of necessary data, or simply poor quality data that can, unsurprisingly, lead to unreliable AI models. By pinpointing any potential gaps in readiness that may exist, a business can then position itself to develop a plan for training or partnering with experts to help with the integration process. Defining objectives and deployment Once any potential readiness gaps have been addressed, a business can start building the foundation on which their AI integration will be built. It’s crucial that business, tech, and data strategy, along with all processes, are clearly defined because these will guide decisions around selecting the most ideal AI tools, platforms, and infrastructure components. After defining the strategy, the business needs to design a new roadmap complete with processes, data, and tech integrations to incorporate the role of AI. To bring AI-driven initiatives to fruition, decision-makers must address new steps, timelines, and milestones to efficiently ready the existing infrastructure. At this point, the business is well-positioned to start testing the impact of its AI and run pilots. Once the system is deployed, it’s crucial to collect feedback and make constant improvements to optimize the AI’s performance. Maturation and scale By collecting feedback and making iterative improvements to the models, a business can start gaining a competitive advantage. As an ongoing process, AI integration, over time, will need to scale, and leveraging a company’s proprietary data sets provides that opportunity. These valuable datasets will enable any business to develop algorithms and proprietary technology to fit a company’s unique business challenges and data compliance requirements. in an impactful way is a very active process that requires businesses to restructure many core elements of the company—it can’t be done by making a few minor changes here and there. It takes strong commitment and focus, but most importantly, successful integrations of AI demand a complete understanding of every detail of the business. Without a deep understanding of why, where, and how AI can help, a business isn’t going to be able to get the ROI they hope for. Integrating AI About the author: Miguel is a visionary CEO and Tech and AI Serial Entrepreneur renowned for co-founding Keenfolks, Scoretize, and . With a decade-long corporate background leading global CPG brands, he heeded his entrepreneurial calling in 2008. OG.AI As the driving force behind Keenfolks, an award-winning Global Integrative AI Agency®, Miguel pioneered the revolutionary Integrative AI Methodology, uniting Business, Marketing, and Prop Technology to fuel growth for Fortune 500 giants. Miguel's leadership, exemplifying the tagline "Fuel Your Greatness," bridges the realms of technology and business with a global impact.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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