By: Krishnan Venkata, Chief Client Officer at digital analytics firm LatentView Analytics.
For more than a decade, businesses ranging from small startups to large corporations have talked about the promise of artificial intelligence (AI) and machine learning (ML). According to these prophecies, AI and ML would transform modern work, automating everyday processes and allowing human employees to focus on higher-level tasks.
Ten years later, for many enterprises, the promise of AI has turned out to be just that—a promise, and nothing more. While many of these organizations have taken steps to accelerating their digital transformation efforts, a few common pitfalls often leave the AI/ML dream unrealized.
What have been some of the biggest factors holding back the transformative potential of AI and ML?
- Lack of organization: The first step to a successful AI strategy is collecting data. But equally important is planning for the organization of that data; businesses that amass a treasure trove of data with no plan for how to organize it, analyze it, and put it to work are left with an unrefined, practically unusable resource. What’s the value in discovering oil if you have no way to get it out of the ground or refine it for use?
- Piecemeal adoption: While digital transformations promise long-term cost savings, the initial price tag to adopt new technology can be steep. This sticker shock leads some enterprises to take a piecemeal approach to integrating AI tools, without considering how that single solution will fit into a larger roadmap.
- Missing processes/discipline: AI and ML solutions will naturally be championed and introduced by specific leaders within the company, but their success depends on institutional buy-in from top to bottom. Early adopters need to prepare the runway for broader adoption, instilling the discipline and routines necessary to make the integration of new tools as smooth as possible.
The past year has demonstrated that there’s no time to waste in terms of digital transformation and automating routines through AI and ML. According to Fortune Business Insights, the global market for artificial intelligence is expected to reach $267 billion by 2027, representing nearly tenfold growth from a value of $27 billion in 2019. A long-term shift to remote work brought on by the COVID-19 pandemic has pushed enterprises to adopt new solutions; Twilio’s COVID-19 Digital Engagement Report found that 97% of executives said that the pandemic accelerated their digital transformation efforts.
So what will it take to move past the hype of AI and ML and actually operationalize these tools? A few technologies and strategies can make the difference between a triumph or a flop:
1. AIOps, MLOps, DataOps
Attaching -Ops to a technology or application is a surefire recipe for a shiny new buzzword, but not all these emerging solutions are vaporware. In fact, strategies like AIOps, MLOps and DataOps can offer the solution to the challenge of organizing all of that data being collected within an enterprise. These tools apply the principles of Agile management to AI, machine learning and data management, respectively, dramatically simplifying the knowledge and effort required to derive value from new solutions. For businesses making their first steps into AI/ML and looking to get up to speed, these strategies are a must.
2. Low Code/No Code
The most complex and nuanced ML models will always require dedicated developers and data scientists to ensure their success. However, the challenges facing many enterprises aren’t nearly as complicated, and can be solved with simpler, one-size-fits-all AI solutions. Low-code and no-code platforms lower the barrier to entry for employees with little to no background in software development. No-code tools enable any employee to build solutions such as recommendation engines through intuitive, drag-and-drop platforms, while low-code platforms can perform complex tasks with only a few lines of code.
3. AutoAI and AutoML
If artificial intelligence and machine learning automate business processes, then why would they themselves need to be automated? A crucial aspect of AI and ML success is the idea of refinement: as these tools learn on the job and integrate more data, they can steadily hone their performance and deliver improved outcomes. AutoAI and AutoML perform this refinement process without requiring any human input, creating an unending virtuous cycle. Humans can check in on the performance of the model to prevent biases and confirm that the tool is serving the enterprise’s needs, but AutoML enables employees to take on other challenges during the day-to-day.
As chipmakers and software companies break new ground with natural language processing, the AI/ML field is reaching a turning point that will see an explosion of new use cases. Enterprises must be prepared to react to these emerging technologies; those who don’t have their house in order now will be left behind by competitors who do.