By 2026, enterprise AI buying has shifted from a frenzy of pilots and experiments to a strategic, outcome-driven process. CIOs worldwide – in India as well as global enterprises – now treat AI procurement with the same rigour as core software purchases, demanding clear business value, robust governance, and seamless integration. Procurement decisions centre not just on novel features but on long-term capability-building. The focus is on measurable outcomes and strategic partnerships rather than one-off proofs-of-concept. Across industries (BFSI, healthcare, manufacturing, public sector) CIOs are forging new collaborations with hyperscalers, startups and consultancies to expedite deployment and mitigate risk. They face key challenges – data readiness, explainability, sustainability and ethical risks – and have added new procurement criteria (model provenance, data governance, interoperability, vendor lock-in) to their checklists. This report draws on recent research, CIO insights and case studies to analyse how AI buying has matured and what it means for technology suppliers and service providers. It also highlights changing budgets (with shifts from CapEx to OpEx models) and offers strategic implications for vendors in the evolving market.
“By 2026, AI procurement has grown up. What we are witnessing is not a slowdown in ambition, but a sharpening of intent. CIOs are no longer buying promises or pilots, they are investing in outcomes, governance and long term capability. AI is now judged with the same commercial and ethical discipline as any core enterprise platform. The winners in this next phase will be organisations that treat AI not as a shiny experiment, but as a strategic partner embedded into processes, people and purpose. This shift marks the moment when AI truly becomes business critical, not just technically impressive.”
Ankit Kankar, General Manager, AI Spectrum India
The Strategic Shift in AI Procurement
In 2025–26, AI moved beyond the hype cycle into mainstream deployment. Whereas earlier AI spending was pilot-heavy and hype-driven, CIOs now prioritise business outcomes. They insist on clear ROI, tangible metrics and broad integration. A recent global CIO survey noted that AI procurement “now mirrors traditional software buying”, with disciplined evaluation frameworks and price sensitivity. In practice, this means choosing models and tools not just for novelty but for proven reliability and fit. One CIO put it plainly: for most tasks “all the models perform well enough now – so pricing has become a much more important factor”. In effect, security, compliance and TCO have gained ground on pure performance in vendor evaluations.
This trend is not confined to the West. In India, EY and CII report that after a surge of pilot projects, 47% of firms already have multiple GenAI use cases in production (and 23% still in pilot). Indian CIOs recognise they must now “move from building pilots to designing processes where humans and AI collaborate”. In other words, AI must be embedded into day-to-day operations, not left on the lab bench. Similarly, an IDC study found that by 2026 more than one-third of organisations will otherwise remain stuck in point-solution experimentation, delaying ROI.
These shifts mean that CIOs are reallocating budgets from trial-and-error to scaling proven projects. Global AI spending is skyrocketing – Gartner forecasts generative-AI spend reaching $644 billion in 2025 (a 76% year-on-year jump). However, such growth comes with scrutiny. CIOs are now saving up budget (“setting roughly 9% of IT spend aside”) to cover vendor price increases, and cutting fringe projects to fund core AI initiatives. In India too, the vast majority of firms still cap AI investment at a small portion of IT budget: 95% allocate under 20% of their IT budget to AI, and only 4% exceed that threshold. The mandate is clear: any new AI tool must prove its economic value before expansion, a discipline far stricter than in the exploratory “hype” phase.
From Pilot to Production: Maturing Investment Patterns
Initially, AI buying was characterised by fragmented pilots and POCs. For instance, one IDC survey found Asia–Pacific companies ran an average of 24 GenAI pilots in one year but only 3 reached production. Such “pilot paralysis” is now easing. CIOs have started to weed out poorly performing experiments and concentrate on enterprise-grade solutions. Procurement processes have adapted accordingly. Andreessen Horowitz research reports that AI models are now selected through formalised processes – complete with checklists, benchmarks and tight budgets – very much like enterprise software. This ensures only solutions that meet risk, security and performance thresholds make it into production.
A key driver is the realisation that building AI in-house often lags buying off-the-shelf solutions. CIOs have become less inclined to develop models from scratch. As one analyst notes, “CIOs are no longer building generative AI tools, they’re being sold technology” by vendors. This matches the data: enterprises report a marked shift from internal development to third-party AI apps. Over 90% of companies in a recent A16Z CIO survey were testing third-party AI applications (even for tasks they might have coded before).
The move to buy is especially evident outside of highly regulated sectors. The a16z study found that while industries like healthcare (with strict privacy/compliance needs) still caution, many others have embraced off-the-shelf GenAI. One consumer fintech firm, for example, reviewed its own in-house chatbot and decided that commercially available customer-support AI was a better investment than continuing to build. This buying orientation allows organizations to leverage specialized AI expertise without heavy R&D. In fact, Gartner and others report that AI procurement now often focuses on outcome-based partnerships: it’s not just about software licences, but contracts tied to business metrics.
An outcome of this trend is a shift in how vendors price and package their AI offerings. Most CIOs still prefer straightforward usage-based pricing to opaque outcome-based models. In practice, many vendors continue to bill by API calls or compute usage, since customers remain uncomfortable with profit-sharing or contingent-fee models. Even so, both buyers and suppliers are experimenting: some contracts now include clauses on model updates, support levels or performance guarantees tied to key performance indicators.
Key Challenges in Enterprise AI Procurement
Despite enthusiasm, CIOs face substantial hurdles in scaling AI. Key challenges include:
- Data Readiness: Gartner warns that 65% of organizations lack “AI-ready” data and will struggle with AI projects unless data is systematically prepared. Without clean, accessible data, many AI initiatives flounder. In practice, CIOs must fund data engineering, create shared data lakes, and enforce governance standards before expecting high AI ROI.
- Integration with Legacy Systems: Many enterprises have an “overburdened legacy operating model” that blocks innovation. Outdated infrastructure and siloed applications make it hard to deploy modern AI tools. IDC notes that 37% of CIOs cite IT modernisation (reducing technical debt) as a top priority, because legacy systems drag down performance and agility.
- Explainability and Trust: As AI is used for critical decisions, businesses demand transparency. CIOs must be able to explain model behaviour to regulators, boards and customers. This is still difficult: language models are often “black boxes”, and tools for interpretability are immature. Without built-in explainability, organisations risk regulatory scrutiny or loss of confidence.
- Ethics and Compliance: Closely related is the need for ethical AI use. Only about 23% of Indian firms currently have formal AI ethics safeguards. CIOs worry about algorithmic bias, fairness and data privacy. This has prompted many to carve AI governance policies (for instance, IDC expects 70% of firms to formalise AI risk oversight by 2025) and insist on features like bias detection, audit logging and privacy controls in vendor products.
- Sustainability and Cost: AI can be resource-hungry. CIOs increasingly factor in the environmental impact and total cost of ownership. By 2027, IDC predicts half of CIOs will demand sustainability metrics for all tech projects, including AI. In practical terms, this might mean choosing energy-efficient cloud data centres or carbon-offset programmes for training large models. Rising hardware (GPU) costs and power use also push firms to careful capacity planning.
- Security and Risk: AI introduces new security attack surfaces. Enterprises must guard against data poisoning or model hijacking. Procurement processes are adapting: for example, U.S. federal procurement now requires proof that AI models are “truthful, neutral and nonpartisan”. CIOs similarly insist on security certifications, independent audits and contractual terms that address AI-specific risks (model drift, adversarial data, etc.).
- Vendor Lock-in and Portability: AI systems can create lock-in. If a company fine-tunes a particular model deeply into its workflow, switching providers is costly. CIOs combat this by adopting multiple models (37% of firms now use five or more models) and favouring open standards. In fact, many organisations emphasise prompt engineering over proprietary fine-tuning, since prompts can be ported between models. As one interviewee observed, tuning prompts (rather than models) helps avoid lock-in when new LLM versions emerge.
Each of these challenges – data, integration, ethics, etc. – is now routinely evaluated during procurement. Vendor evaluations often score heavily on these non-functional factors. For example, an IDC survey of Asia–Pacific healthcare CIOs shows that 42% say “AI security capabilities” are extremely important when choosing a partner, and 40% flag “data governance capabilities” as essential. Security, cloud/partner ecosystem alignment and compliance often rank higher than raw model performance.
New Criteria in AI Vendor Selection
Given these challenges, CIOs have expanded the checklist for AI procurements. In addition to functionality, new criteria now rule the day:
- Model Provenance and Transparency: Buyers demand to know where an AI model comes from and how it was trained. Agencies now require vendors to provide documentation on model architecture, training data sources and audit trails (so-called “model provenance”). CIOs push for open disclosure: Which datasets were used? Were any copyrighted or sensitive materials involved? This level of scrutiny is unprecedented in IT procurement and reflects concerns over copyright, bias and legal risk.
- Data Governance: Robust data governance is non-negotiable. CIOs check that both input and output data pipelines comply with corporate policy and regulations. They expect vendors to maintain certifications (e.g. ISO/IEC standards), demonstrate strong access controls and commit to data privacy frameworks. This focus aligns with survey findings: 80% of organisations count “data privacy & compliance standards” as a top criterion for AI tools. In practice, CIOs now often require vendors to outline their data management practices in proposals.
- Interoperability and Integration: Since AI must sit atop broader IT systems, seamless integration matters. A system that can plug into existing ERPs, workflows and databases is more attractive. In a survey of AI adopters, 79% rated “system integration capability” as extremely important for partners. Many buyers insist on open APIs, common data formats (e.g. ONNX compatibility), and minimal custom coding. This also ties into multi-cloud strategies: CIOs want flexibility to run AI on any major cloud or on-premise hardware.
- Explainability and Auditability: CIOs expect AI solutions to include tools for interpretability. Providers must explain how key decisions are made (for example, which features weighed most in a prediction) or at least log detailed decision paths. Proposals are scrutinised for explainability features, compliance with guidelines (such as the EU’s forthcoming AI Act), and evidence of bias tests. This was clear in recent US guidelines telling agencies to ensure LLM outputs remain “truthful in responding to factual prompts”.
- Ethical Controls: Alongside technical checks, companies look for ethical safeguards. Features like bias detection routines, demographic parity audits, or even internal ethics review boards at the vendor are now considered advantages. According to IDC, many CIOs now expect an AI vendor to show a “responsible AI code of practice” (over 40% said this was extremely important).
- Vendor Lock-in Risk: Procurement teams formally assess the risk of lock-in. They ask about portability of models, continuity of support, and the feasibility of migrating data if they switch providers. One tactic is to work with consultancies or integrators that pledge neutrality. For example, some organisations require multiple vendors or open-source back-stops in their AI roadmap. A16Z reports 37% of firms now use five or more different models in production – partly to avoid dependence on a single provider.
- Financial Metrics: Cost structures receive fresh scrutiny. Beyond licence fees, CIOs ask how pricing scales with usage spikes, whether training compute is included, and how much support costs. Because generative models can rack up API charges rapidly, buyers often cap usage or seek fixed-cost platforms. Many now prefer “consumption-based” pricing to large upfront CapEx. (Indeed, a CIO Dive report found over 60% of firms were already shifting more spend to Opex subscriptions instead of one-time purchases.) Vendors are being pushed to offer transparent rate cards or outcome-based service terms.
In summary, AI procurement checklists now read more like RFPs for highly regulated systems than typical software. Vendors who fail to address these new criteria – for example, by treating AI as just another SaaS rollout – risk being sidelined.
Cross-Industry Examples
The strategic approach to AI buying is evident across verticals:
- BFSI (Banking and Financial Services): Indian banks are at the forefront of strategic AI buying. For example, Mumbai-based Yes Bank has aggressively boosted technology spend (a 33% CAGR to ₹963 crore by FY2025) as part of a digital strategy. Its CIO describes a “comprehensive customer onboarding programme” underpinned by AI and APIs. The bank collaborates with both major tech vendors and fintech startups to roll out AI/ML innovations: they have partnered with Microsoft on a virtual assistant (‘Yes Robot’) and explored “agentic AI” with Razorpay to speed up partner onboarding and financial intelligence. These initiatives tie into a broader cloud strategy: Yes Bank emphasises multi-cloud deployments to gain elasticity and cost efficiency. In other Indian banks, similar trends are visible. HDFC Bank, for instance, has invested in startups (like chatbot firm CoRover) and is deploying GenAI chatbots in customer service. Globally, major banks (e.g. JPMorgan, HSBC) likewise shifted from proofs-of-concept to broad platform buys for fraud detection and customer analytics, although concrete public case studies are still emerging.
- Healthcare: Hospitals and insurers are buying AI to improve patient care and operational efficiency. Generative AI is a growing example: tools now help draft clinical notes, patient instructions and even perform preliminary diagnoses. In Japan, Fujita Health University Hospital worked with AWS to deploy GenAI assistants that relieve clinicians from administrative tasks. IDC notes that Asia–Pacific healthcare CIOs rank “medical accuracy” and integration with patient data as top priorities – reflecting the need for reliable outputs. In India, EY forecasts GenAI will boost healthcare productivity by ~30–32% by 2030, driven largely by automation of non-clinical tasks. Indian government hospitals and telemedicine platforms, influenced by global trends, are moving to procure conversational AI assistants and image-analysis tools from vetted vendors (both multinationals and domestic startups). Vendors now routinely need to demonstrate HIPAA-equivalent data handling (or, in India’s case, compliance with draft rules) as part of any procurement pitch.
- Manufacturing: Industrial CIOs are adopting AI for predictive maintenance, quality control and design automation – and buying solutions accordingly. A recent TechCircle report finds 65% of Indian manufacturers had adopted AI by 2024 (up from 45% in 2022). Leading firms report significant results: Tata Steel uses AI-based monitoring to cut unscheduled downtime by ~20%, Maruti Suzuki’s AI-driven production scheduling has cut costs by ~14% and downtime by 30%. AI-driven visual inspections in textiles, electronics and automotive (including robotics and machine-vision solutions) are now purchased at scale. Procurers in manufacturing focus on industry-specific expertise and proven ROI: for example, over 86% of adopters rate “vendor proven success in similar use cases” as extremely important. This has spurred many industrial suppliers (and even governments via “Make in India” initiatives) to co-develop solutions. Global OEMs like Bosch and Siemens promote their IoT/AI platforms (MindSphere, Siemens Xcelerator) as complete stacks, reflecting a shift to bundled offers. In procurement meetings, CIOs in this sector often seek hybrid offers: cloud analytics for central oversight, plus edge AI appliances for factories.
- Public Sector: Governments and agencies are also retooling procurement. A prominent Indian example is the Government e-Marketplace (GeM). By embedding AI analytics into the procurement portal, GeM achieved a 9.75% median price saving across ₹13.6 lakh crore of transactions by May 2025. This success was recognised with national awards and is now being replicated in other e-gov initiatives. In the UK and US, public CIOs are similarly enforcing new AI procurement guardrails: U.S. federal guidelines now require evidence of “LLM truthfulness” and even explicitly mention requesting model documentation as part of any AI contract. In India, the Centre has mooted using AI to scrutinise tenders for fraud and to prioritise projects. As a result, public-sector IT acquisitions increasingly include clauses on data privacy, model accountability and local-language support. These efforts reflect a broader theme: governments expect AI to cut costs and fraud, so AI solutions must demonstrate fairness and transparency out of the box.
Each of these industry cases illustrates a common theme: long-term capability-building. CIOs are not buying one-off chatbots or analytics tricks; they are investing in platforms, partnerships and processes that can support AI at scale. This is a far cry from the early-2020s “pilot phase”, and indicates that AI procurement has matured into a core strategic activity.
Strategic Collaborations and Ecosystems
CIOs no longer go it alone when implementing AI. Instead, they build ecosystems of partners to cover gaps in capability and speed time-to-value. Recent data shows about 80% of organisations now rely on external AI partners for development and deployment. Key forms of collaboration include:
- Hyperscalers and Cloud Providers: Major cloud vendors (AWS, Microsoft Azure, Google Cloud) are natural allies. They offer not only infrastructure but increasingly native AI tools and marketplaces. For example, many enterprises procure AI models through marketplace channels (e.g. Azure OpenAI Service or AWS Bedrock) to benefit from enterprise SLAs and integration with existing systems. Yes Bank’s partnerships with Microsoft illustrate this: using Azure’s AI offerings, they enhanced their virtual assistant and risk-analytics capabilities. CIOs also negotiate enterprise agreements (EA) that bundle AI usage discounts. Because hyperscalers are continually updating their model catalogues, CIOs see this as a way to keep pace without repeatedly renegotiating new deals.
- Consulting and Systems Integrators: Top consulting firms (BCG, McKinsey, Deloitte, EY) and boutique AI specialists play a big role in shaping procurement strategies. They often run AI Centres of Excellence (CoEs) or help craft RFPs. For instance, many large organisations engage consultancies to conduct readiness assessments and vendor evaluations before buying. These partners bring proven methodologies for use-case prioritisation, governance frameworks and risk assessment templates. According to industry reports, CIOs heavily consult analysts and SIs to filter the sprawling AI market into shortlists that match organisational needs. This trend also extends to post-purchase: integration firms frequently embed their own “AI success accelerators” or even “AI-at-CX” platforms to glue solutions together.
- Startups and Niche Innovators: Many AI vendors in RFPs are startup-born. CIOs recognise that innovative capabilities often come from newer companies (e.g. domain-specific LLM providers, data-labeling services, or AI-infused SaaS). To tap this, procurement teams are now more open to engaging smaller vendors – provided they meet enterprise criteria. Some organisations even create incubator or accelerator programmes to nurture promising startups (for example, Indian tech giants and banks have opened AI innovation labs with startup cohorts). These collaborations give CIOs access to cutting-edge ideas and allow for co-innovation. For instance, startups are helping customise LLMs to local languages or regulatory contexts, an important factor in non-English markets.
- Industry Consortia: In highly regulated sectors, CIOs often pool risk by joining consortia. For example, several hospital groups or banks may co-invest in a shared AI platform or data lake (under strict governance). This approach is gaining traction in India’s fintech space, where lenders are exploring a common fraud detection AI system. While still nascent, such consortium-led procurements could reshape how AI is purchased in the public interest.
What all these collaborations have in common is a shared focus on outcomes and trust. When selecting partners, CIOs screen for proven track records and alignment with their ecosystem. In fact, a survey of AI buyers found the top vendor attributes sought were technical expertise (91% of respondents) and “proven success in similar use cases” (88%). Also important are transparent pricing (82%) and cultural compatibility (71%). Integration capability (79%) – the ability to interoperate with existing systems – is another recurring criterion across sectors. The message is clear: partners must complement in-house strengths, not just sell new gadgets.
Budgeting Trends: From CapEx to OpEx
The financial model of AI investments is shifting. Traditionally, major IT projects were budgeted as CapEx – one-time purchases of hardware and software. In contrast, modern AI initiatives are favouring OpEx models. CIOs increasingly prefer cloud-based or subscription offers where costs scale with usage. This trend predates AI but is accelerating. A CIO Dive survey found that 60% of companies accelerated their shift from CapEx to OpEx to free up capital for innovation. In practice, buying AI often means entering long-term service agreements (for cloud GPUs, LLM APIs or data-label services) rather than buying a server.
This move to OpEx is aligned with AI’s unpredictability. Early pilot projects in 2023–25 often encountered unpredictable costs (e.g. massive GPU bills for training). To tame this, procurement now negotiates flexible deals: fixed monthly subscriptions, reserved compute pools, or consumption caps. For example, an enterprise might reserve a block of Azure AI credits at a fixed fee, rather than scaling on-demand. In India, cloud providers have also started offering “AI as a Service” bundles with transparent pricing to woo cautious CIOs.
Reflecting this trend, CIOs have begun carving out budget lines for AI initiatives as a continuous expense. According to research, by 2025 more than 20% of IT spend will be driven by AI and cloud transformations – up from single digits a few years prior. Instead of large up-front capital outlays on on-premise AI appliances, firms are more likely to pay for models and data-bytes as operational expenditures. This aids cash flow management and aligns investment with delivered value.
In India specifically, the availability of cloud services has enabled even public sector procurement to move to an OpEx model. For instance, GeM’s AI investments are effectively as a service (using vendor analytics engines), and the new AI Centres of Excellence financed by the government follow an Opex grant model. The overall effect: CFOs are more comfortable approving recurring expenditures on AI if tied to measurable outcomes, whereas large CapEx spends (like building private datacentres) are viewed with more scrutiny.
Challenges and Barriers
Even as procurement becomes more strategic, obstacles remain. CIOs cite several persistent barriers:
- Data Silos and Quality: Perhaps the single biggest bottleneck is messy data. Information needed for AI often lies scattered across legacy systems, spreadsheets or even paper. Bringing this data together – and cleansing it – takes time and money. As Gartner warns, without an “AI-ready data” foundation, well over half of AI projects will stall. Some firms are now investing more in data engineering and governance (often at the insistence of senior leadership), but misaligned incentives and organisational silos slow progress.
- Organisational Readiness: Relatedly, many companies are still adapting their operating models. A Gartner poll found 62% of strategy leaders acknowledge their current IT setup cannot support future innovation demands. Effectively, some CIOs have to restructure teams and processes (for example, by creating an AI Centre of Excellence) before major procurement makes sense. This soft-change is harder to budget for and plan, yet it is crucial. IDC notes that CIOs who tackle technical debt and rigid processes (37% are already doing so) will accelerate AI adoption.
- Talent Shortage: Advanced AI skills are scarce. Even if procurement brings the right technology, firms struggle to staff data scientists and AI engineers. IDC predicts that by the end of the decade, half of top enterprises will rely on automated tools (low-code/no-code, agents) to partly bridge this gap. In the meantime, some CIOs mitigate risk by using managed service providers or focusing on explainability so that less-expert IT staff can maintain models.
- Trust and Explainability: Building trust in AI results is non-trivial. Pilots that appeared promising may fail to generalise, or reveal biases. CIOs now allocate budget to model monitoring, bias audits and fallback procedures. For example, a bank might require that any credit-decision AI routes ambiguous cases back to human review. Such governance adds cost and complexity but is increasingly treated as integral. As one industry survey noted, “simply buying AI software won’t cut it” – teams must redesign processes and train users to manage AI outputs responsibly.
- High Upfront and Ongoing Costs: Despite the OpEx trend, there remain significant costs in licences, cloud infrastructure and specialised hardware (GPUs, FPGAs). CIOs report that negotiating contracts to cap these costs is a key hurdle. In some sectors, the need for private, compliant clouds (e.g. for patient data or banking secrets) drives expensive custom solutions. Even on OpEx models, budgeting for cloud spending growth strains IT finance. Gartner data shows many CIOs setting aside ~9% additional budget for AI-related price increases. CFOs can be wary of such open-ended commitments unless clearly justified.
- Regulatory Uncertainty: Finally, the regulatory landscape is in flux. In the EU, draft AI regulations impose stringent requirements on high-risk AI (such as financial scoring or healthcare diagnosis). In India, the government is drafting its own AI rules. CIOs must therefore vet compliance risk when buying. They often lack in-house legal expertise on AI, so this task is usually driven by procurement or compliance teams. The uncertainty means contracts now often include detailed clauses on legal liability and IP ownership – lengthening negotiations.
Each of these challenges creates a drag on procurement cycles and ROI realisation. However, they are no longer tolerated as optional issues. Boards and audit committees now regularly query AI projects on these very points, forcing CIOs to front-load risk mitigation into RFPs and vendor evaluations.
Strategic Implications for Vendors and Service Providers
For vendors and service providers, these shifts in buying behaviour carry important lessons:
- Demonstrate Enterprise Readiness: Products must come enterprise-hardened. This means providing thorough security documentation, data governance compliance and integration SDKs. Vendors should be ready to share whitepapers or demo environments that show how their AI meets the buyer’s standards (for example, ISO certifications, SOC reports, or third-party algorithmic bias audits).
- Partner Ecosystems: Vendors should be open to collaborating with consultancies and integrators that CIOs trust. Instead of resisting third-party integration, AI suppliers ought to build joint offerings with cloud partners or SIs (e.g. pre-certified on AWS Marketplace, or solutions co-developed with Accenture/Capgemini). Many CIOs will penalise tools that lock them out of known partner channels.
- Flexible Commercial Models: Vendors must align pricing with buyer preferences. That often means usage-based SaaS models or outcome-linked terms (though as noted, many buyers remain cautious about opaque outcome-based fees). In response, some vendors offer trial tiers or proof-of-value pilots to reduce perceived risk. Given the trend away from CapEx, offering cloud credits or consumption bundles can entice CIOs. For large enterprise deals, vendors may need to agree to multi-year frameworks or guarantees of performance.
- Focus on Interoperability and Portability: Building on open standards will win CIO goodwill. Vendors can support export/import of models or data, comply with container standards (e.g. ONNX), and avoid proprietary lock-in mechanisms. The goal is to fit into clients’ multi-cloud or hybrid strategies. Even if the core IP is locked, offering clear migration paths or interim redundancy (such as cross-training on open models) can ease procurement concerns about single-sourcing.
- Vertical and Localisation Strategies: Many enterprises prefer domain-tailored solutions. Vendors should consider specialized offerings (e.g. healthcare language models, BFSI-compliant analytics) or partnerships that customize generic AI to local contexts (languages, regulations). In India, for instance, AI offerings that handle regional languages or local regulatory nuance stand out. Localization is a factor – global generic tools may be passed over in favour of augmented, region-specific solutions.
- Transparency and Governance Support: Being proactive about ethics, bias and auditability can differentiate a vendor. Providers can offer governance dashboards, user-training modules, or even “ethics licenses” as part of their packages. Given CIOs’ interest in vendor provenance, companies should clarify their supply chain: Where was the model trained? What data protection is in place? This could be done via standard ‘vendor risk scorecards’ or compliance certifications. In some cases, vendors may even volunteer to comply with emerging standards (e.g. support independent certifications or “trust marks”).
- Building Long-term Relationships: Finally, vendors must shift from transactional sales to becoming strategic partners. CIOs now expect vendors to invest in knowledge transfer and co-development. This might mean on-site enablement teams, embedded project squads (“pods”) or even revenue-sharing on joint successes. Companies like Salesforce or Google Cloud routinely set up co-innovation labs with customers. For smaller vendors, offering an outcome-aligned pilot or ROI guarantee (for example, “we’ll refund if X improvement is not met”) can signal commitment beyond the initial sale.
In summary, AI procurement’s maturation means selling AI is no longer just a product pitch. It requires demonstrating trust, compatibility and continuous innovation. Vendors who respond by being more consultative and flexible will thrive; those clinging to old sales models risk being sidelined.
By 2026, AI procurement is evolving into a core strategic function for CIOs. The days of ad-hoc pilot spending are over. Instead, enterprises demand that AI tools integrate seamlessly, deliver measurable business value, and align with robust governance frameworks. CIOs are now buying AI with enterprise discipline: negotiating commercial terms, vetting ethical safeguards, and forming partnerships to fill skill and technology gaps.
This strategic approach is reflected in new priorities (from explainability to interoperability) and in budgeting shifts (more OpEx, sustained investment). It spans sectors – banks, hospitals, manufacturers and governments all share this more rigorous stance. For vendors and integrators, the message is clear: adapt your offerings to fit the buyer’s new checklist, and support clients in realising long-term capability, not just a quick proof-of-concept.
The procurement phase sets the foundation for AI success. Those organisations that treat it as a strategic enabler – aligning AI investments with core business outcomes and treating technology selection as a multi-faceted decision – will likely pull ahead in the AI race. Conversely, companies that underestimate the rigour now demanded in AI buying risk sunk costs in abandoned pilots or even compliance failures. As one CIO put it: “In 2026, it’s not about how many AI tools we have, it’s about how effectively we can use them to meet our strategic goals”.
Disclaimer:
This analysis is intended for informational and strategic insight purposes only. It is based on secondary research, industry surveys and expert perspectives and does not constitute financial, legal, regulatory or procurement advice. Organisations should evaluate AI investments in line with their own operational, regulatory and commercial requirements.
