Investment in AI is on the rise and buzz is at an all-time high. But AI adoption by sectors outside the digital frontier is still in its early days. In general, few companies have incorporated AI into their value chains at scale; a majority of companies are still in experimental or pilot phases. According to a McKinsey State of AI discussion paper , only 20 percent of companies had adopted one or more AI-related technology at scale or in a core part of their business. Only 9 percent reportedly adopted machine learning.
Commercial considerations can explain why some companies may be reluctant to act. Regulation issues may also limit the actual utilization of AI tech, as well as limited support from current workforce. When a company sets to deploy AI technology, there are many business-specific challenges, and making the right choices can be tricky. Gauging AI success in one field can be meaningless for another. Moreover, a technical evaluation of the solution – for example, which machine learning algorithms are utilized or what are the number of layers in its deep neural network – might also be pointless as it does not directly reflect the solution’s ‘success’ implications.
Nevertheless, it seems that the market ignores this reality and continues to evaluate AI-based products by buzzword checklists using familiar and related AI terminology (e.g., Supervised, Unsupervised, Deep Learning etc.). While checklists are an effective tool for comparative analysis they still require the ‘right’ items to be included. Unfortunately, what’s typically absent are the items which are important to the customer from a problem-solution perspective.
Given all of this, there is a need to change the narrative around AI technology and solutions in a way that reflects the real-life challenges and opportunities that businesses are facing. This is the time to get acquainted with the principles of Authentic AI.
The Merriam-Webster dictionary defines ‘Authentic’ as both ‘worthy of acceptance or belief as conforming to or based on fact’ and ‘conforming to an original so as to reproduce essential features’. This is not about ‘Fake’ to be contrasted with ‘Real’. It’s about the essential features of AI which need to be acknowledged, and hence, redefine the ‘checklist’. Often, these essential ‘authentic’ features are hidden and only surface when a CIO/CDO is faced with a new problem to be solved.
This is seen especially when the AI aspects of a proposed product or solution are fully explored by asking questions such as:
- Is the AI technology utilized by the product aimed specifically for my problem, optimally (e.g., performance, cost, etc.)?
- Is it capable of addressing the complete problem or only a part of it?
- Can it be assimilated into the existing ecosystem without imposing new demands?
- Can it address the compelling environmental conditions of the problem space?
These issues can be grouped into three different ‘classes’ – ‘Original’, ‘Holistic’ and ‘Pragmatic’:
How innovative is the solution? This can be quantified by assessing the following:
- the invention of new algorithms or even new models and
- the use of complex orchestration techniques or
- through the capability to handle complex data formats and structures.
While there is no need to re-invent the wheel repetitively for any problem, there are distinctive characteristics which require optimizing.
How complete is the proposed AI technology? It takes into account the capability of handling the end-to-end aspects of the solution, the competence of harmonizing the operation of the various AI components of the solution and the ability to adapt to ever changing conditions of the AI application.
Can the technology solve real world problems in their actual and natural space in a commercially viable way? This means that for example the data sources can be processed in their most native format (both unstructured or structured) as well as provide insights or results matching the pragmatic needs of the specific market expectations. In addition, the ability to be quickly deployed and rapid to act are assessed. The AI also needs to be explainable, in the sense that it provides users some transparency into its internal logic.
All of these elements should be used to systematically assess and evaluate AI-based products and solutions and their effectiveness in specific use cases.
For example, many marketing technologies utilize some form of machine learning, usually limited to the predictive area of the solution – which is one of many other processes included within the system. These technologies cannot be considered Authentic AI. They score low on the ‘Original’ aspect as they aren’t innovative enough (from an AI sense), as well as on the ‘Holistic’ aspect, as their AI component is siloed within the system and does not cover on its own the end-to-end aspects of the solution (hence affecting the overall performance and precision). It could be considered to be ‘Pragmatic’ to some level if it can handle the required data sources of customer data and if the solution output are the explicit results required as a specific recommendation (e.g., segmented target audiences). However, the deployment timeline (time-to-market) and commercial aspects need to be evaluated as well. This is just one example of many others, covering all kinds of variations.
AI’s potential to transform business processes, create new sources of value and increase profits is great, as demonstrated by early adopters and early case studies. There are many industry- and sector-specific use cases to inform companies when they define a focused strategy, but while machine learning and deep learning underpin most opportunities, industries need to identify the AI technologies that will bring the most benefits to them. Going beyond the buzzwords and opting for Authentic AI solutions is a necessary first step.