Executives worldwide are keenly aware of artificial intelligence’s (AI) immense potential. Increased cost savings, expanded revenue growth, faster time to market, and personalized customer experience are some of the top reasons organizations use AI now, but these merely scratch the surface of possibilities. Recent industry predictions indicate that by 2024, more than 50% of user interface interactions will use AI-enabled computer vision, speech, and natural language processing. By 2025, at least 90% of new-enterprise apps will embed AI. And that’s just the start.
Future spend is in line with aspirations, as global investments on AI systems will reach $97.9 billion in 2023—more than 2.5 times the estimated $37.5 billion spent in 2019. By 2024, more than 50% of all IT spending will go directly for digital transformation and innovation.
Despite significant investment in AI, many organizations are not seeing a return on their investment. Only one-tenth of proofs of concepts (PoCs) reach production deployments, and about half of all AI initiatives fail. More than half of data scientists’ (expensive) time is spent on data integration, management, and solution deployment—not actual data science tasks.
If you know your organization could soar with AI, but you can’t seem to break through the roadblocks, you’re not alone. Helping data professionals in the transition to successful AI deployments is why Oracle partnered with IDC to study the challenges and paths to AI success. The result is an exclusive infobrief, Thrive in the Digital Era with AI Lifecycle Synergies.
Gain inside access to research findings at our webinar on February 4 with myself and Ritu Jyoti, Program Vice President of Artificial Intelligence Strategies with IDC's software market research and advisory practice. You will learn the AI barriers data professionals face, best practices for overcoming them, and the investments you should make now to make AI a sustainable competitive advantage for your company.
Challenges to AI success can happen anywhere in the AI lifecycle, which spans numerous stages and involves multiple stakeholders. These stakeholders aren’t only data scientists but also data engineers, IT teams, business analysts, and application developers. Lots has to happen before machine learning models can be built, such as defining the business problem, acquiring data and prepping it. This work requires collaboration with different roles and functions and reuse of existing work.
Without the right underlying infrastructure and data platform foundation for doing machine learning, and the right processes between stakeholders, organizations can spend weeks or months on machine learning projects that are never deployed into production. There are numerous roadblocks that happen throughout this lifecycle which we will cover in our webinar, but three major issues most leaders face include access to data, operationalizing machine learning, and managing a complex lifecycle to build models.
Access to data includes the management of data on-premises, within the cloud or a hybrid of the two. Finding and combining data is challenging for most organizations, especially if that data comes in multiple formats (ex: structured, unstructured), comes in different velocities (ex: batch vs. streaming), and/or is incomplete.
Operationalizing machine learning includes the deployment, monitoring, and management of machine learning models—a step which many machine learning projects do not reach. For successful AI deployment, models often must be re-coded, and once those models are deployed, they must be continually monitored to ensure accurate outputs, or be retired and retrained.
A complex lifecycle to build models includes multiple roles and stakeholders, including data scientists, data engineers, business analysts, app developers, and IT. Roadblocks can occur within each stage of the lifecycle and are oftentimes process-related vs. purely technology-related.
When you prepare and account for addressing common roadblocks at each stage of the AI development lifecycle, your organization can build, deploy, and manage more AI-powered applications faster, with less operational overhead. Sign up here for our webinar on February 4 to learn how your organization can thrive in the digital era with AI lifecycle synergies.