InMobi's founders have repurchased a stake worth about $250 million from SoftBank, reducing the Japanese investor's holding in the company from around 35% to over 5%, reports stated. The share buyback deal was executed at a valuation of under $1 billion. SoftBank had invested around $200 million in InMobi from 2011 and will receive $250 million from this transaction.

Revenue models differ widely, and selecting the wrong metrics slows growth. SaaS depends on retention and payback periods, ecommerce on contribution margins and repeat cycles, and B2B on pipeline quality. Mapping metrics to how money actually compounds helps operators avoid vanity indicators and align decisions with sustainable performance.

Prescriptive systems analyse creative signals, audience shifts and channel behaviours to recommend actions that raise performance. Instead of reviewing dashboards, teams act directly on insights tied to probable outcomes. This reduces experiment wastage, speeds planning and improves accountability by connecting each suggestion to measurable campaign impact.

Human-in-the-loop systems rely on supervised oversight, ensuring control in high-risk tasks. Agentic AI performs multi-step operations autonomously using reasoning loops. Choosing between the two depends on error tolerance, speed needs and governance demands. Understanding both models helps teams deploy AI that fits operational safety and efficiency requirements.

AI agents range from reactive and model-based agents to goal-driven and learning agents. Each type handles information, memory and decision-making differently. Knowing these distinctions helps teams select the right agent architecture for planning, environment navigation or multi-step tasks, improving reliability and performance across applications.

Legal teams process large document sets and rule-heavy logic. Agentic AI assists by reviewing contracts, summarising clauses and structuring compliance checks through multi-step reasoning. While expert oversight remains essential, these systems reduce repetitive tasks and improve research speed without replacing statutory judgment or human interpretation.

Product teams juggle research, prioritisation and feedback cycles. Agentic AI helps by synthesising data, stress-testing requirements and simulating outcomes before execution. It speeds discovery, reduces manual review burdens and provides scenario-based clarity. This allows managers to focus on strategy, customer value and cross-functional momentum.

Whitespace analysis examines gaps in consumer needs, pricing, formats and category saturation. Brands spotting unmet expectations can design offerings before competitors notice. Identifying these gaps early allows faster product adaptation, clearer targeting and stronger differentiation, especially in categories where incumbents overlook nuanced behavioural shifts.

Achieving market fit requires consistent pull, rising repeat behaviour and strong message–product alignment. Fit becomes evident when users adopt without heavy incentives and feedback converges on the same value drivers. Recognising these signals early enables teams to double down on the strongest use cases and scale responsibly.

Influencer-led referrals work when trust, relevance, and timing converge. Users adopt products more readily when recommendations come from credible voices. Structured referral loops, combined with authentic creator advocacy, increase conversion likelihood and keep acquisition costs controlled, allowing brands to scale through community-led momentum.