In Part-1 of this blog series, I discussed how electricity (a form of electrical energy) evolved to mass adoption, similarly, AI is the new form of energy in the business world. Businesses are finding ways to adapt and infuse this energy to enhance growth and market capital.
AI is a noticeably new aspect for production, precise utilization of human capital, and overall business innovation. It has already gained significant credibility through the disproportionate business impact it has created within certain industry verticals (BFSI, Healthcare, Retail, CPG, etc.).
In order to realize the economic & economical potential within the AI Solutions space, the need of the hour is to carefully model it with the help of tangible strategic acumen & contextual research. I believe, AI is nothing but 'Hyperactive Energy' which can be generated and suitably utilized for the betterment and development of society at large.
First, let’s acknowledge the fact that there is a lack of the right talent to produce economical AI solutions that can drive a large impact. The idea is to help enterprises bring scale and processes for their AI Solutions, whether it is in the form of Products, Enterprise Systems, or Services.
In my experience of building transformational solutions I feel that around 50% of the time needs to be spent up-front on building AI Strategies, around 35% on Design and Build, and the remaining 15% on contextual assessment of AI Solutions with stakeholders. However, in reality, due to market pressures, the time spent is almost reverse. Enterprises tend to spend way more time on technology, product design, and implementation and the strategy aspect is almost forgotten; leading to lower adoption (if not complete failure) of the product. In my opinion, the major reasons for the AI products to fail or be unable to scale up are lack of:
AI Strategy – for the enterprise, product, or solution
AI Business Model and Value Propositions
Human(User) Friendliness across AI based Product/Service journey
Accurate insights on targeted customers and context
Combination of right team and work
Given the social impact, any AI product or solution will have, problems need to be solved with a human-centric approach than the traditional system development style.
Let’s see the bird's eye view of producing scalable AI Solutions by optimally utilizing time, effort & money. The diagram below is a general guideline with structured steps i.e. Budget, Cost, Time, and efforts for modeling AI Solutions at a high level. The parameters are expected to vary based on the context and AI Scope of work.
Most business and technology executives, value the outside, independent consulting viewpoint on what parts of their business might essentially relate and employ AI, Machine Learning (ML) in solving their business problems. The business problem needs to be qualified, does it really require AI? A problem-solver approach is highly recommended in the context of the AI space and thus requires the involvement of AI Researchers, Scientists & Strategists to qualify the business opportunity for AI at the first stage. If the business problem is exclusively handled by just structuring a set of “Conditional” coding rules there is no need to implement AI/ML forcefully.
Secondly, if there is no availability of accurate practices/patterns to achieve intended business goals through a specific problem statement, then AI/ML may not be the best option. Moreover, the right approach is to determine the Human Performances for intended task/s, which are to be replaced/performed by machines.
With this approach, one can easily regulate the underlying economics & economical values of fitting AI/ML in the specific business context w.r.t Cost, Time, Efforts Saved, and Tangible Value Gained in terms of ROI.
To broaden the spectrum of applications where AI can proliferate, one needs to see smart, self-driven, or high-performance Machines that are able to function in uncertain/unclear and frequently fluctuating scenarios & contexts. This would help unlock the real potential of AI and thus lead to wider adoption and the common good of society.
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