CPG companies turn to AI-driven strategies to navigate price and volume challenges

As the consumer packaged goods sector faces mounting pressures on margins and shifting consumer behaviours, industry leaders are increasingly adopting AI and analytics to refine pricing, promotional strategies, and demand planning for sustainable growth.

The consumer packaged goods sector is navigating a difficult reset: price increases have lifted revenues, yet volumes and margins tell a more complicated story. Recent industry snapshots show that despite notable markâ€...

‘ups, unit sales have slipped in many categories, while consumers gravitate toward lower‑cost options and alternative channels. That tension has made a disciplined, technology‑enabled approach to revenue growth management essential for manufacturers and retailers alike.

Demand forecasting is the foundation. Firms that still rely on manual or coarse forecasting are struggling to keep shelves balanced and promotions effective as buying patterns fragment across online and brick‑and‑mortar channels. According to Gartner, large organisations are moving rapidly toward AI‑based forecasting, with a prediction that 70% will adopt such systems by 2030 to enable near‑touchless demand planning. Industry research also points to tangible gains from smarter sensing: Bain reports that adopters of AI demand‑sensing have seen sales improvements of several percentage points, driven by more precise, SKU‑level visibility and faster reaction to market shifts.

Promotional spend remains a major drain when poorly targeted. Analysts estimate that around one‑fifth of CPG revenue is channelled into trade promotions, yet a significant share of that investment fails to generate net profit. McKinsey data cited in the sector show widespread losses on trade spending, underscoring the need for analytic rigour. Best practice combines rigorous review of historical promos with simulation tools that identify low‑return activities and measure true incremental uplift versus cannibalisation. Vendors are offering platforms that claim to surface hidden leakages, create AI‑driven promotional calendars and allocate budgets to the highest‑return SKUs and customers; such capabilities are becoming table stakes for teams seeking accountable return on trade spend.

Pricing strategy is similarly nuanced. Across the US, shelf prices have risen markedly over the past several years, while input costs have climbed in parallel, squeezing both shoppers and producers. Food industry reporting notes that since 2020 shelf prices are up by roughly 30%, while manufacturers’ costs have increased by about 25%, creating narrow operating room for many brands. At the same time, consumer behaviour is shifting: a 2025 survey found nearly a third of US shoppers switched to cheaper grocery items in the previous month, and a sizeable minority are cutting back on household essentials. Those dynamics mean that indiscriminate price hikes are losing effectiveness; instead, companies need granular elasticity analysis, pack‑architecture optimisation and scenario testing that weighs revenue, margin and share consequences before changes are implemented.

Integrating these capabilities into a single decision environment reduces fragmentation. More than half of senior CPG executives report prioritising upgrades to analytics and decision tools, according to SAP research, reflecting a desire for unified dashboards that tie forecasts, pricing, promotions and performance metrics together. Consolidated platforms enable rapid what‑if modelling, a single source of truth for calendars and plans, and smoother cross‑functional execution, reducing the time between insight and action and limiting costly misalignments between sales, marketing and supply teams.

Collaboration between manufacturers and retailers remains a critical, yet uneven, axis for improvement. Reporting in 2024 highlighted a disconnect: a large proportion of manufacturers intended to pursue price increases while many retailers anticipated fewer rises than in prior periods. That misalignment amplifies friction around promotional funding, assortment decisions and joint innovation. Transparent, customer‑level ROI reporting and shared scenario tools can help bridge those gaps, enabling negotiations grounded in evidence rather than assumption.

The business case for AI across go‑to‑market activities is strengthening. Surveys of sales leaders show widespread expectation of revenue gains from AI integration, with some forecasting double‑digit improvements over several years. When combined with better retailer cooperation and tighter trade governance, these technologies can shift decision‑making from reactive firefighting to proactive, prescriptive planning.

Vendors in the market position their offerings as turnkey paths to these outcomes; for example, some analytics firms present agent‑based architectures that automate price and promotion optimisation and promise rapid visibility into spend effectiveness. Such claims should be treated with editorial distance: the technology can materially improve decision quality, but benefits depend heavily on data governance, cross‑company alignment and disciplined change management.

For CPG organisations seeking to protect margins and retain shoppers, the prescription is clear: invest in granular, AI‑enabled forecasting; apply robust causal measurement to promotional investments; deploy sophisticated, elasticity‑based pricing tools; and consolidate insights into shared platforms that enable faster, evidence‑based collaboration with retail partners. Those moves will not erase external pressures, but they offer the best route to convert data into durable, profitable growth in a market where price alone no longer guarantees volume.

Source: Noah Wire Services