13 May Adopt AI Technologies Now, Do Not Wait
Blog by John Song
Times are changing fast, don’t get left behind.
If your competitors are personalizing their interactions with customers, can you really afford not to? Furthermore, if other companies are implementing AI to automate much of their back office tasks that you are doing manually, how will you compete long term? The practical business use cases for AI are growing rapidly from fraud detection to smarter recommendation engines to text analytics that deliver metrics to predicting successes or failures, and more.
One of today’s biggest challenges for the corporate boardrooms, during this the fourth industrial revolution, is the need to keep up with all the rapidly emerging capabilities of AI technologies. Companies that wait to adopt AI may never catch up, according to Harvard Business Review. First, there is substantial effort required to build AI systems. So, by waiting to start the AI adoption, your organization likely will always be behind. In addition, integrating a new AI system with current business applications and processes also requires considerable amount of time and effort that will keep you far behind early adopters. Lastly, most AI systems rarely run without some augmented support by humans. Putting this type of workforce transformation in place is time consuming as well.
It is no wonder that there is a real sense of urgency to adopt AI technologies by executives. According to an Accenture survey, three out of four C-suite executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely. For these executives, scaling AI means going beyond just pilot projects, and actually putting into production intelligent applications across the organization that predict and prescribe better business outcomes.
Yet, the path to successful scaling of AI within an organization has many daunting barriers. Seventy-six percent of the executives surveyed by the above Accenture survey reported that they struggle with how to scale AI. Another way to explain the problem is that 87% of data science projects never make it into production according to an article in Venture Beat.
There are many reasons for the high failure rate, but I think these three are most notable.
- The fast rate of AI innovation causes misunderstanding about what is required to be successful. This leads to a high rate of failure in implementing AI systems into production.
- The difficulties in data access and preparation. It’s an old problem, but now it’s even more complicated by all the different types of data, including unstructured data, that need to be analyzed. Without good data, you cannot have accurate predictive results that is the promise of AI applications. Essentially, organizations are unable to get to the anticipated return on investment.
- The lack of collaborative teams working with data scientists to build full-stack solutions. Data science has been around since the 1950’s, but most data scientists have not been properly integrated into the greater application development teams, or the functional and change management teams. Consequently, many AI applications are not successfully adopted within organizations and fail.
Naturally, new solutions are being developed to help companies adopt AI into their business. Cloud computing and the advent of AutoML, such as AWS (Amazon), Azure, (Microsoft) and Google Cloud (Google) makes applying data science easier; that is, if you already have capable data scientists, data engineers and software engineers working together on your team. While the one percent of the rich global companies can hire and build such teams (this is known as the 1% problem), it is not so easy for the other 99%.
There are other more niche data science platforms such as Oracle’s datascience.com that provide data science tools to be more efficient and productive. These are good for teams that again already have a capable data science team.
More and more, we will start seeing machine learning application development platforms that can manage the data and deliver on targeted business use cases, such as consumer-specific or supplier management-specific machine learning applications. The most successful, I believe, will be the platforms that provide not just the machine learning intelligence but a fully-integrated environment for the rapid development of full-stack intelligent applications.
For example, this platform will have to incorporate concepts of augmented analytics where machine learning and AI technology is utilized to make data management categories including data quality, master data management, metadata management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning. According to Gartner, this is a big deal because it automates many of the manual tasks opening up opportunities for less technically skilled users to use data.
In addition, this new-breed platform will help integrate the software engineering, data engineering efforts with the data science efforts to facilitate a full-stack approach that quickly delivers business-ready intelligent applications. Businesses normally are not interested in just a library of machine learning algorithms. They want a fully functioning business solution, they want outcomes. In order to make this possible for businesses, there needs to be platform that elevates much of the hidden technical debt currently inherent in each new AI system.
We, at TenPoint7have built such a machine learning application development platform. Our mission is to help business organizations adopt AI technology now by focusing on three key objectives:
- Scale your data science and machine learning capabilities
- Mitigate the risk of AI projects and initiatives
- Accelerate AI innovation
Without question this the fourth industrial revolution will disrupt the business landscape as we know it. Some late adopters will be displaced by a new-breed of AI-native companies and by those who had the vision to start this digital transformation early.
Therefore, companies shouldn’t wait with a strategy to be “fast followers” in adopting AI technologies. Emerging companies like TenPoint7, among others, already have solutions that make AI adoption possible today before it is too late.
TenPoint7’s mission is to help scale an organization’s data science capabilities by focusing on three key objectives:
- Scale your data science and machine learning capabilities
- Mitigate the risk of AI Projects and initiatives.
- Accelerate AI Innovation
Scale your data science and machine learning capabilities
We help clients unlock the power of their data through deploying proven machine learning as a service (MLAAS) technologies that change the way they operationalize their business and deliver customer experience. We do this by:
- Making AI, Machine Learning, and IoT work together to create superior and personalized digital end customer experiences
- Unlock the hidden knowledge in unstructured data to drive productivity and business value
- Augment existing data science & ML skills and capabilities to achieve business outcomes faster
Mitigate the risk of AI Projects and initiatives.
Scaling an organization’s data science capability can often result in increased risk if not managed. Our platform can help mitigate the huge risks of AI initiatives and realize the possibilities of AI to maximize the value of data and business processes securely and cost-effectively. TenPoint7 clients quickly achieve business outcomes through AI transformation utilizing proven technologies and approaches. We’ve done the heavy lifting in terms of understanding the security issues associated with AI and protecting data. This enables us to identify quick wins to test the value and outcomes of AI initiatives by generating Proof of Concepts (POCs) quickly and securely. We work with clients to determine the business processes that would most benefit from AI, and experience efficiencies and productivity quickly. These applications and the underlying methodology have been tried and tested by some of the world’s biggest brands and are built with security and ethics in mind.
Accelerate AI Innovation
We are also able to incorporate new AI innovations into the platform that can be leveraged across our customer base. TenPoint7’s culture is based on a hunger to learn and continuous evolution that results in solutions that have been specifically tailored, architected and proven to scale to service the needs of complex and mature global organizations. Our team stays at the forefront of technology trends and changes. These solutions future proof client’s AI initiatives as they have been built with innovation in mind. This empowers us to get from idea to POC quickly, securely and accelerate business outcomes.