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Getting Started With AI: A Practical Path Forward
2026-02-04 21:44:25| The Webmail Blog
Getting Started With AI: A Practical Path Forward jord4473 Wed, 02/04/2026 - 14:44 AI Insights Getting Started With AI: A Practical Path Forward February 5, 2026 By Madhavi Rajan, Head of Product Strategy, Research and Operations, Rackspace Technology Link Copied! 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This article outlines a phased approach to AI adoption, from improving operations to enhancing customer experiences and unlocking new revenue. Starting with AI can feel overwhelming. Headlines often focus on massive investments by global enterprises building or consuming frontier models at scale. For most organizations, however, that level of GPU-heavy infrastructure is neither required nor practical. If youre not running large-scale production models, the broader AI ecosystem doesnt need to dictate where you begin. Across the cloud landscape, organizations are at very different stages of AI adoption. While Fortune 100 companies invest billions in in-house development, many organizations in the Russell 2000 and beyond are focused on building practical capabilities that help them stay competitive. The question most leaders ask is straightforward: Where do I begin my AI journey? A useful way to answer that question is to think in phases. Most organizations move through three broad stages of AI adoption: operational efficiency, customer-facing experiences and new revenue streams. The level of investment required depends on several factors. These include compute, network and storage needs, the type of models in use, workload volume, organizational readiness and the phase of adoption. Understanding these variables early helps teams focus on use cases that deliver value without unnecessary complexity. Phase 1: Operational efficiency Organizations of all sizes struggle with inefficiencies caused by fragmented data and disconnected systems. These silos slow decision-making and can create costly errors. In some cases, businesses continue paying vendors months after a contract has ended simply because systems do not talk to each other. Using AI to improve operational efficiency across functions such as IT, finance, HR, supply chain, procurement and sales is often the lowest-risk, highest-impact starting point. These use cases are internal, measurable and closely tied to day-to-day productivity. The challenge is not a lack of data, but where that data lives. Critical information is often trapped in separate systems and supported by institutional knowledge that does not scale. When introducing AI, you need to be clear about intent. The goal is not to replace roles, but to remove friction so people can focus on higher-value work. Many established enterprises carry years of technical debt across product, operations, customer success and go-to-market systems. Simply buying an AI copilot rarely solves that problem. Off-the-shelf tools alone cannot bridge disconnected data or deliver meaningful ROI. Real value comes from applying AI on top of an organizations own data and processes. Consider a typical services business. Supply chain data lives in one system, customer records in a CRM and contracts in a homegrown application. The result is a collection of dashboards that offer limited insight into utilization, customer health or revenue trends. AI can act as an intelligence layer across these systems. It can surface which customers are growing, highlight utilization patterns and support scenario modeling. ROI becomes tangible through faster insights, fewer spreadsheets and better decisions. Speed to value also matters. How quickly do teams see results once a model is deployed? In one finance organization, analysts reduced time spent wrangling spreadsheets by roughly 40% with the help of an AI assistant. That time shifted to scenario modeling and analysis, where human judgment delivers the most value. Completing this phase gives organizations a clearer view of what their AI workloads require and how those capabilities can eventually extend to customer-facing value. Phase 2: Customer-facing experiences As AI matures, personalization becomes a key driver of customer retention. Buying AI tools does not equal adoption. AI must deliver specific business outcomes to matter. While automation can support customization, true personalization requires context, judgment and empathy. This applies across both B2C and B2B environments. In financial services, for example, some organizations use AI to assemble client intelligence that includes recent activity, potential opportunities and emerging risks. That insight allows teams to personalize interactions, anticipate needs and identify growth opportunities earlier. Continuous monitoring of customer consumption patterns helps organizations anticipate change. When paired with alerting and recommendations, customer-facing teams can deliver more relevant outreach, predict demand shifts and align offerings more closely to customer goals. This is especially valuable in subscription and recurring revenue models. With the right foundation, teams can enter every customer interaction better informed and more precise. Data, process insight and market context come together, enabling employees to move beyond routine tasks and focus on deeper, strategic engagement. Phase 3: Embedding AI into what you sell The first two phases help organizations improve how they operate and serve customers. The third phase is where AI becomes transformational, embedded into what you sell and directly driving new revenue. Success at this stage looks different by industry. In financial services, AI may streamline onboarding or fraud response while improving the customer experience. In other sectors, AI may become a differentiated product or service in its own right. This shift often requires new business models. Many AI-native companies tie pricing to outcomes rather than consumption alone. In these cases, AI is not just an internal capability, but a core part of the value proposition. Sustaining that value depends on culture and decision-making. AI influences the full lifecycle, from product development to billing and supply chain operations. Real impact only emerges when teams align across functions. While AI excitement dominated recent conversations, the next phase will be defined by how effectively you translate AI into practical execution and measurable outcomes. How Rackspace Technology can help Turning AI ambition into results requires the right foundation, governance and operational support. Rackspace Technology helps organizations design, deploy and manage AI solutions that align to real business goals, whether the focus is efficiency, customer experience or new growth opportunities. With deep expertise across hybrid cloud, data platforms and AI operations, Rackspace provides a structured path from experimentation to production. Learn more about how Rackspace supports AI initiatives here. Tags: AI Insights
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