Why Are Businesses Struggling to Implement AI Strategies?
Cisco research finds that only 14% of businesses are ready to implement AI strategy, citing siloed data as a major hurdle.
A recent survey conducted by Cisco revealed insights into business leaders’ AI “readiness,” based on their perspectives and actions taken regarding 6 metrics of AI strategy implementation: Strategy, infrastructure, data, governance, talent, and culture. Despite 97% of over 8,000 business leaders acknowledging the urgency to incorporate AI technology in the last six months, only 14% of respondents feel fully prepared to make the transition, with 57% of companies categorized as having limited or no preparedness for the needs of AI.
Why are businesses struggling with AI implementation? Three metrics in particular stood out as significant hurdles: Culture, governance, and data. Just 9% of organization leaders reported having a fully AI-prepared corporate culture, and 30% have comprehensive AI policies. Perhaps most notably, “Some 81% of respondents said their data is currently siloed within their company, inhibiting the extent to which it can be ingested by AI technology in the first place,” per FastCompany.
The challenge with data
Most businesses recognize that data is the bedrock of AI models. So why are they struggling to use it for AI models that boost competitive advantage? The solution seems simple – organize and consolidate your data so it can be used to train AI models of choice. But 80% of enterprise data is unstructured, meaning it’s not properly structured or readable by AI models. Unstructured data exists in varied, disparate formats – documents, emails, PDFs, notes, call logs, etc. These data sources can be some of the most insight-rich data in your organization, if they can be unlocked for AI. Most businesses do not have the time, manpower, or budget to devote to structuring this data on their own, let alone uniting it with already structured data.
Seek AI’s support
What can businesses do instead? One solution is to turn to AI tools to structure and prepare your data for generative AI models. Conveyer’s AI engine unites unstructured and structured data by ingesting and organizing them via topics, generating metatags, and storing the freshly curated data in our TopicLake™ repository. Teams are fighting for resources, and many are understaffed. Conveyer provides scale to your teams’ AI efforts through Conveyer’s AI engine and makes your data usable for AI models.
You can see the TopicLake™ repository at work in an interactive BI dashboard created by Conveyer’s co-Founder and Chief Product Officer, Max Riggsbee, which utilizes the research from Cisco’s AI Readiness Index. With our AI engine, you can merge your siloed data, unstructured and structured, and interface with that data to gain insights via auto-generated question and answer pairs, summaries, classifications, and keywords.
A dashboard such as this one is just one way to interface with your unstructured data, to quickly access relevant information and gain valuable insights. Using this technology not only allows you to take advantage of the massive amounts of valuable, often untapped data locked in your disparate file sources, but it also alleviates the massive costs and time investments associated with attempting to do it on your own.
Get ahead of the 81%
With most businesses struggling to implement AI strategy, taking advantage of technology like Conveyer’s TopicLake™ repository can put you ahead of the 81% of businesses inhibited by siloed data. Your data is your competitive edge, and failing to unlock the value within it could rob your business of its potential growth and impact promised by AI models trained on that data.
What will your company do? Leverage the technology that enables seamless AI strategy implementation for trusted, robust, cost-effective AI that serves your business across departments? Or will you fall behind as your competitors do take advantage?
Reach out to a Conveyer implementation specialist to learn how to get ahead.