C‑Suite Tech Radar: May 2026 Snapshot

Theme: Companies are shifting from “AI pilots” to AI‑centric operating models, with capex, M&A, and partnerships increasingly clustered around AI‑enhanced infrastructure, agents, and governance.

1) AI‑vs‑non‑AI deals (2025–early 2026)

Indicative split across major geographies and deal types.

Category	AI‑centric share (%)	Typical deal type / example	Strategic implication for CXOs
Global VC in tech	~60–70%	AI‑driven SaaS, infra, health‑AI, fintech‑AI startups	Allocate more vetting rigor to AI‑value claims over “AI‑washing”.
US/EU enterprise M&A	~40–50%	AI‑and analytics consultancies, cloud‑native AI ISVs	Build AI‑adjacent M&A pipelines (data‑ops, AI‑governance, domain‑specific agents).
Indian IT‑services M&A	~70–80%	AI‑cloud, data‑engineering, Salesforce/Azure‑Centric firms	Expect Indian IT giants to price‑in AI‑readiness via tuck‑in buys.
Indian VC (deep‑tech)	~90%+ of deep‑tech	AI‑led deeptech, health‑tech, mobility‑AI, industrial AI	India‑focused strategy must lean into AI‑have or partner‑with‑AI.

Takeaway: AI‑centric deals now dominate new capital and M&A flows; non‑AI deals are mostly in legacy infrastructure, non‑AI SaaS, or “enabling” tech (e.g., cybersecurity, low‑code).

2) Regional hotspots: where to watch

By region, what’s driving the AI‑centric tech cycle.

Region	Key drivers / trends	Strategic angle for CXOs
US	Big‑tech AI‑capex, hyperscaler alliances, AI‑chip investments, agentic‑SaaS stacks	Partner with or benchmark against hyperscaler‑native AI stacks (Azure, AWS, GCP).
EU	AI‑regulation‑driven compliance layer, AI‑governance platforms, edge‑AI for industry	Build AI‑risk and AI‑governance into your operating model; use EU as a “regulatory probe state”.
India	$70B+ AI‑centric infra commitments, AI‑led VC, IT‑services AI‑M&A, emerging AI‑hubs	View India as AI‑onshoring + AI‑innovation hub; explore JV / captive‑AI‑studio models.
China	Domestic‑only AI ecosystems, AI‑driven hardware, facial‑recognition‑heavy verticals	For global firms, expect dual‑stacks (China vs ROW), with decoupled AI models.
EMEA/ LATAM	AI‑driven fintech, logistics‑AI, agritech, and energy‑AI pilots	Look for AI‑leverage in emerging‑market operating models (e.g., AI‑underwriting, AI‑logistics).

Takeaway: AI‑infrastructure and AI‑M&A are most concentrated in US, India, and EU; EM markets are more “use‑case‑first” with AI‑enhanced vertical SaaS.

3) Sector‑wise risk–opportunity assessment

High‑level risk–opportunity matrix across key industries.

Industry	AI‑centric opportunity	AI‑centric risk (strategic)	Non‑AI opportunity
Healthcare & life‑sci	Faster drug‑discovery, AI‑diagnostics, personalized therapy, clinical‑decision support	Data‑privacy, model bias in clinical decisions, regulatory‑lag	Digital health platforms, EHR modernization, remote‑monitoring IoT
Financial services	AI‑fraud, AI‑risk‑modelling, AI‑wealth‑management, AI‑underwriting (incl. SMB)	Black‑box lending, model‑risk, regulatory‑scrutiny of AI‑decisions	Cloud‑core banking, digital‑payments, core‑banking modernization
Retail & CPG	AI‑personalization, visual search, dynamic pricing, demand‑forecasts	“Surveillance pricing”, algorithmic bias in offers, customer‑trust	Omnichannel retail stacks, supply‑chain visibility, BNPL‑enabled commerce
Enterprise platforms	AI‑agents inside ERP/CRM/HCM, no‑code AI‑workflow builders	Platform lock‑in, AI‑vendor dependence, governance gaps	Low‑code platforms, integration‑as‑service, data‑fabric
Manufacturing	AI‑predictive maintenance, AI‑SOP optimization, robotics + AI	AI‑driven downtime if models fail; cybersecurity‑surface grows	OT‑IT convergence, IIoT‑plus‑edge, MES modernization
Energy & climate	AI‑grid optimization, renewables‑forecasting, carbon‑tracking AI	Energy‑intensive AI‑data‑centers, “green‑AI” credibility	Smart‑grid, clean‑energy SaaS, ESG‑data platforms
Telcos & infrastructure	AI‑network‑optimization, AI‑OSS/BSS, AI‑driven customer‑experience	Vendor‑dependence on AI‑chip/IP stacks, geopolitical‑supply‑chain risk	Private‑5G, edge‑data‑centers, fiber‑to‑enterprise

Color‑coding for CXO use (conceptual):

• Green (high opportunity, manageable risk): Healthcare, FS, enterprise platforms, manufacturing, energy.

• Amber (high opportunity but high regulatory/ethical risk): Retail/CPG, FS, healthcare.

• Red (caution, heavy AI‑risk or regulatory‑overhang): High‑surveillance‑use‑cases, opaque AI‑pricing, unregulated AI‑lending.

  
  

4) One‑page action‑themes for CXOs

1. AI‑centric operating model:

   • Treat AI not as a “tool” but as a core capability layer: embed AI‑agents, governance, and data‑lineage into every major digital initiative.

2. Capital allocation lens:

   • Screen all capex/M&A through an “AI‑multiplier” filter: deals that accelerate AI‑readiness, data‑quality, and governance will yield higher ROI.

3. Geo‑strategy:

   • Use India + US + EU as your AI‑infrastructure triangle; treat EM as AI‑use‑case playground with lighter‑touch pilots.

4. Risk‑mitigation playbook:

   • Build an AI‑risk committee (legal, risk, data, ethics), with clear policies on explainability, human‑in‑the‑loop, and model‑risk‑management.