Generative AI has rightly seized the attention of business executives. From writing code to generating art to summarizing technical documents in seconds, generative AI offers an impressive, never-before-seen set of capabilities, empowering businesses to scale new heights much faster. That's why it is increasingly part of almost every stakeholder communication. Every business wants to integrate AI into its operations in some form. About 98% (nearly all companies) report that the urgency to deploy AI has increased over the last year, according to the CISCO AI Readiness Index.
Another survey, MIT Technology Review Insights, shares similar findings: 82% of C-suite and other senior executives agree that "scaling generative AI use cases to create business value is a top priority for our organization." But how many organizations are fully ready?
As per Cisco's 2024 AI Readiness Index, only 13% of companies globally are classified as "fully ready" to capture AI's potential. These numbers are starkly lower than the percentage of enterprises that want to integrate AI into their workflows.
The challenges identified in achieving AI readiness in these reports are data integration and pipelines. Since much of the organization's highly valuable data is currently stored in disconnected CRM or legacy systems, real-time accessibility becomes limited. This makes it challenging to keep up with ongoing changes. These issues can act as bottlenecks in building a clean, accurate, and scalable data foundation that AI models can rely on. AI depends on historical data to generate output. Furthermore, bias and a lack of explainability could introduce new risks.
In this article, we discuss truths about AI readiness that no one talks about and how organizations can better prepare for it.
Do you know why most organizations struggle to realize AI's full potential? It's not because of a lack of good executive leadership, tech expertise, or the chosen AI subscriptions or investments. But it's often because data foundations are fragmented, inconsistent, or inaccurate. Businesses may be aggressive in deploying AI models into their workflows. But many overlook the fact that AI relies heavily on historical data. Messy, uneven, or inaccurate data quality can prevent AI initiatives from moving beyond isolated pilots, as they yield poor results compared to their real potential.
That's why data integration is the leading priority for AI readiness. There are some challenges, though. Managing data volume, moving data from on-premises legacy systems to the cloud, enabling real-time access, and managing changes to data. How can you address these issues? By modernizing data infrastructure and building automated pipelines. This strategic yet critical move can feed reliable data into AI models, enabling them to perform at their optimal levels and deliver consistent results.
Even with the proper data foundation, AI can't scale unless there is a structural and cultural shift within the organization. But what is this change? It's about empowering the workforce through AI training and upskilling, establishing clear ownership, defining KPIs, and fostering trust.
With multiple Generative AI/LLM tools available online, people are experimenting, often using multiple tools at once. But it usually slows productivity because these tools are not trained on their company's unique datasets. That means people are reworking on assets. Also, people lack guidance, while managers struggle to redesign workflows or measure AI-driven outcomes. This combination creates a structural bottleneck. Therefore, true AI readiness requires organizations to enable AI as an extension of their processes, embedded into the systems. And not an isolated experiment or minimally adjusted.
Lastly, it doesn't mean AI can replace the workforce right away; instead, it augments them. This can help companies derive more value from their AI investments without creating fear amongst employees. So, they can focus more on what matters: delivering more strategic, innovative assignments without worrying about their jobs.
Have you heard about McDonald's AI experiment that was called off after drive-thru ordering blunders? Or news about Air Canada, which was ordered to pay damages to a passenger after its virtual agent gave incorrect information? Headlines about AI going wrong are not uncommon. What does this tell us? That AI is as good as the data it's trained on. AI learns from historical data. Sometimes that can reflect the world's imperfections.
For example, a nursing recruitment drive at a hospital may have been all-female 20 years ago, but a business can't screen candidates solely based on gender/sex. An AI model trained on past data may not be able to differentiate between this. A similar situation unfolded at Amazon some time ago. Its AI recruiting tool was trained on a dataset containing CVs mostly from male candidates. The tool interpreted that women candidates were less preferable. This shows bias. A dataset that has bias of any kind, be it age, sex, race, or gender, can cost an organization serious business and reputational damage in an all-inclusive world. As the use of AI scales, risks multiply: hallucinations, security, privacy, and compliance.
That's why governance, risk controls, and ethical guidelines should be essential foundations, not optional. With responsible AI adoption, you can scale your operations while maintaining ethical integrity.
Maximizing AI's real potential lies in building foundations. And that is complete with not just investing in tools, but also in data management, governance, culture, and people. Here are some strategic levers:
Information flows from different sources: CRM, finance, operations, social media, email, customer service, etc. Organizations need to unify this data to avoid duplication and silos and maintain consistency. This step can help businesses provide more accurate and up-to-date databases for AI models.
Establish a centralized governance framework around data accuracy, consistency, security, and compliance. This not only helps provide more reliable and consistent inputs to AI models but also increases regulatory compliance.
AI adoption should be across departments and functions. Businesses shouldn't confine it to IT or a "data team". Because AI is revolutionary and has plenty of use cases. Marketing, sales, customer support, compliance, and senior leadership can all benefit from AI-driven workflows. That's why assigning clear ownership can help align AI initiatives with business goals and drive better adoption.
Begin with a pilot batch consisting of people from different departments/functions. Equip them with the understanding they need, like what AI can and cannot do. Scenarios where AI is more effective and where human intervention is necessary. This can set up the foundation for a workforce that understands AI's potential, trusts it, and knows when and how to leverage it, increasing operational efficiency and scalability.
Identify where AI can deliver real value, such as cost savings and efficiency gains. For example, using AI in a customer care chatbot can help businesses deliver 24/7 support, especially on generic queries like "Where is my order?" "Return/replace an order", "Download order receipt". It can fetch updated knowledge base/self-service articles that answer most product and business-related queries so that customers can get resolutions faster. It also reduces customer support team burnout, increasing productivity, and CSAT/NPS scores.
Similarly, leaders can define AI use cases and metrics/KPIs upfront and carefully measure the impact before and after AI adoption. This eliminates "pilot purgatory" when AI transformation projects get stuck in endless testing) and helps businesses see real ROI from their AI investments.
Building an AI-ready organization isn't a project. It's a journey. Although it's been a while since Generative AI solutions and related technologies have made their mark, many have been slow to adopt them or see visible ROI gains. But it's important to note that AI isn't a plug-and-play tool like CRM. It needs efficient training, accurate data, continuous assessment, and iteration. As business needs evolve, so should your AI infrastructure, governance, and workflows.
By focusing on these levers, you move beyond mere integration of AI. Instead, you become an enterprise that's fully AI-ready (AI embedded in systems and workflows, not an extension), unlocking its full potential while augmenting employees, driving innovation, and boosting profitability. Discover how you can facilitate safe and trustworthy AI implementation.
Jeetu Patel, Chief Product Officer at Cisco, recently said:
“Eventually there will be only two kinds of companies: those that are AI companies, and those that are irrelevant. AI is making us rethink power requirements, compute needs, high-performance connectivity inside and between data centers, data requirements, security and more.”
So, organizations will surely need to invest in tools and high-performing devices. But that doesn't automatically make your organization AI-ready. To fully capture AI's potential, businesses need to lay a solid data foundation, empower workforce, and build a supportive culture with robust governance. Because AI is as good as the dataset it's trained on, how skillfully people can use its unmatched features, and how governance is embedded. These elements work behind the scenes, round the clock, delivering more effective results that help maximize ROI and set you apart in the market while maintaining security and compliance.