Your Enterprise Needs an AI Strategy
Businesses worldwide are all taking different approaches to artificial intelligence (AI). Many are embracing the technology and deploying ambitious AI projects, while some are still experimenting and investing in R&D, and others have yet to determine the business value of adopting AI technology for their organization. Regardless of what stage your firm is at, there is one thing that’s certain: AI cannot be ignored and everyone needs to have a strategy.
Recent research has shown that 80% of enterprises have already invested in some form of AI. Additionally, 30% of enterprises intend on expanding their investments in the next three years. This is not exactly news - the adoption of AI has been covered incessantly in the news media for the last few years. What is fascinating are the following statistics:
Further, 91% of business leaders expect to face challenges with their implementation of AI, the top barrier being a lack of IT infrastructure. It is clear that the problem isn’t that enterprise executives don’t understand the need to implement AI in their organizations to stay ahead, it’s a question of “how.”
A Scalable Digital Solution
Scalability essential for firms to handle complex AI workloads, in fact it is the number one roadblock for most firms interested in developing the technology. Due to the nature of deep learning, a small AI pilot project can turn into a massive undertaking in a short amount of time, requiring significant IT resources to sustain.
For smaller AI initiatives, on-premise solutions are appropriate. However, larger, more ambitious AI initiatives will require external assistance to implement servers and store large data sets. This is necessary to mitigate the increased risk that comes with developing AI, which is often a trial-and-error process.
AI can be leveraged in multiple ways for a company’s web presence. Functionality such as language processing, categorizing and interpreting images, content moderation and text summarization can all be deployed to improve site performance with little risk. However, like all AI technologies, it requires a robust, highly scalable platform, like the open source Drupal CMS. Known to be the world’s top CMS for complex enterprise needs, Drupal’s powerful categorization and taxonomies can successfully manage large data sets for AI solutions.
AI development is expensive, and while the right IT infrastructure is essential, costs will only continue to climb unless an efficient method is adopted as well. Thousands of companies worldwide, including OPIN, have committed to the Agile development methodology.
At OPIN, all of our digital solutions have been deployed using Agile development. As opposed to the more traditional “waterfall” approach which involves an enormous amount of planning and focuses on total project completion prior to launch, the Agile approach breaks down a project into components that can be quickly launched and tested. This leaves room for subsequent launches to continuously improve based on results garnered from the constant feedback loop.
Data science teams employing the Agile approach are able to make constant tweaks to gradually improve their product, re-prioritizing when necessary. Ultimately, Agile development results in more accurate solutions with a faster turnaround at a lower cost.
Although common in software development, it remains rare for AI development. While unfortunate for the progress of AI as a whole, this is a great opportunity for enterprises who do successfully leverage Agile development in their organizations.
Open Source to Support AI initiative
AI development is almost cost-prohibitively expensive. From hiring specialized talent to constructing a more robust IT infrastructure, the costs keep rising. This has led many organizations to either stall or abandon altogether their ambitious AI projects.
Using proprietary software as the backbone for AI initiatives is problematic due to the costly lock-in situation that is created as licensing and development costs continue to rise as the AI becomes more complex. On the other hand, building AI solutions with open source technology provides a low-cost development environment that is sustained by a global community of coders. Further, your organization will be able to tap into the wide pool of global talent and innovation that naturally arises from open source communities, keeping you at the cutting edge of AI development.
Another issue of concern regarding proprietary software in AI is transparency. With digital privacy and scepticism towards AI now at the top of many consumers’ minds, opaque proprietary solutions managing and categorizing massive data sets autonomously can produce unintended consequences. While there is a moral issue here, there is a strong business case for adopting open source technologies for AI.
As an open source CMS, Drupal integrates exceptionally well with emerging AI technology. In the last year alone, an abundance of contributed modules has been developed by the community, providing features such as chatbots, automatic text generation, image recognition and categorization, auto-tagging and multi-lingual services.
Leaning on open source communities to develop AI solutions provides a low-cost, transparent backdrop for your firm’s innovations in the space. It’s an opportunity that should not be missed by any company looking to stay ahead in the midst of widespread AI adoption.
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