AI-automation Market Predicts:
AI sunk costs: AI is failing to scale, in 90%+ enterprises
Gartner said in 2020 AI hype cycle- AI governance is fast emerging as the biggest headache.
All net new digital businesses are AI-native, e.g. digital banks, blockchains, cryptocurrency companies, new-age insurance companies like Lemonade, digital advertising, legal processing, and post-COVID, digital healthcare services spectrum- remote monitoring to drastically faster clinical trials in pharma.
But, AI ground realities faced by traditional enterprises are quite grim. Even the early adopters are facing challenges in adoption@scale. Without adoption, RoI is low, and payback periods for these expensive tech-stacks are beyond 3-5 years. Consequently, business sponsorship and budgets for these technologies are drying up under duress, especially when decisions are taken in a short to medium-term outlook.
WHY DOES AI FAIL?
Industry research on AI is shady and murky, with unverifiable and doubtful survey-sourced data, and pseudo-experts camouflaging as AI tech/ practice SMEs, e.g. a global newspapers' misleading marketing claim of a fully GPT3-generated article! Given the vastness, depth, complexity, change dynamics and costs of AI-automation tech-stacks, it is absolutely imperative to have evidence-based, actionable frameworks, guidance & benchmark data on cost, accuracy, performance. AISWITCH fills these gaps, with 2 key solutions:
To make simple changes in decision variable-values/ thresholds, in a bot's decision-tree/ logic layer/ automated workflow, some vendors and service partners demand months of efforts and charge a bomb, inflating the timelines and hiking costs, lock-in & tech debts.
Multiply these mini-projects' cost-spikes with 1000's of RPA bots & AI-powered agents running in integrated/ cascading workflows, to enable end-to-end process automation. You end up hitting a steep cost-wall!