How to Find and Select AI Technology

Artificial intelligence has moved from a buzzword to a boardroom priority in the space of just a few years. Organisations of every size and in every industry are now actively evaluating AI technology - whether that means deploying a standalone AI tool, embedding AI capabilities into an existing platform, or investing in a more strategic AI infrastructure. The market opportunity is real. But so is the complexity. Indeed, it's the fastest growth area for Viewpoint Analysis - helping businesses to find and then select new technology.

The challenge is that the AI technology landscape is unlike any other. Vendors appear and disappear rapidly. Capabilities that seem transformative one quarter become commoditised the next. The boundary between genuine innovation and well-packaged hype can be frustratingly thin. For business leaders trying to make sound decisions - decisions that affect operations, data, budget, and competitive position - the pressure to move quickly must be balanced with the need to move wisely.

This guide is designed to help you do exactly that. Viewpoint Analysis acts as a neutral Technology Matchmaker, helping organisations find, evaluate, and select the right AI technology for their specific needs. Whether you are just beginning to explore the AI landscape or are already deep in a selection process, the factors outlined here will help you make a more confident and better-informed decision.

For a broader read on the AI market, vendors, and trends, take a look at our AI Technology area - it's regularly updated and packed with practical insight.

What Do We Mean by AI Technology?

AI technology is a broad umbrella that covers a wide range of products and platforms. For the purposes of this guide, we are focusing on commercial AI solutions that organisations can procure and deploy - not on building AI from scratch. This includes:

•       Generative AI tools for content creation, code generation, and summarisation

•       AI-powered analytics and business intelligence platforms

•       Intelligent process automation and workflow tools

•       AI assistants embedded in enterprise software (CRM, ERP, HR, and more)

•       Specialist AI applications for functions such as finance, customer service, HR etc

•       AI infrastructure and platform services (LLM access, vector databases, orchestration layers)

The common thread is that these tools use machine learning, large language models, or other AI techniques to automate, augment, or accelerate work that was previously done manually or with traditional software. The question is not whether AI technology can help your organisation - for most, it can. The question is which solution, from which vendor, on which terms, is the right fit for you.

Starting the AI Technology Selection Process

Before diving into vendor evaluation, it pays to be clear on your starting point. AI selection processes tend to fall into one of two situations: organisations that know broadly what they want to achieve but need help identifying the right tools, and those who have already identified a category of AI technology and need support running a structured selection process.

If you are still in the discovery phase like most of our customers, our Innovation Series and AI Matchmaker services can help you quickly map the market, shortlist credible vendors, and build a working brief before any formal process begins. For those ready to move into a structured selection, we run Rapid RFIs, Rapid RFPs, and 30-Day Selection Processes that move quickly without sacrificing rigour.

Critical Factors in Selecting an AI Technology Vendor

Below are the key factors we believe every organisation should assess carefully when selecting an AI solution. These are drawn from our direct experience working with business and technology leaders across a wide range of industries and use cases. They are presented not as a rigid scorecard, but as a framework for structured, thoughtful evaluation.

1 – Financial Stability and Business Viability

The AI technology market is extraordinarily dynamic. Vendors appear at speed, attract significant venture funding, and just as quickly run out of runway, get acquired, or pivot to a different focus. Selecting an AI vendor that ceases to operate - or is absorbed into a larger platform that deprioritises your use case - is a very real risk, and one that is often underweighted during evaluation.

Before committing to any AI vendor, review what you can of their financial position. For publicly listed companies, this is straightforward. For private or venture-backed vendors, it requires more digging - but it is worth doing. Key questions include: How long does the company have before it needs its next funding round? Who are its investors, and is continued investment likely? Does the company have a credible path to profitability, or is it entirely dependent on growth funding?

Also consider how dependent the vendor is on a small number of large customers. A provider with a highly concentrated customer base carries more risk than one with a diverse portfolio. And look at their track record - have they delivered on previous roadmap commitments, or have they consistently promised capabilities that have yet to materialise?

Financial stability is not glamorous, but it is foundational. The best AI solution in the world delivers no value if the company behind it is not around in eighteen months.

2 – Market Saturation: How Many Others Do the Same Thing?

One of the defining characteristics of the current AI market is how quickly new entrants appear claiming to solve the same problem. When you evaluate an AI vendor, it is worth asking: how many other companies offer a materially similar solution, and what happens to this market in the next two to three years?

Markets with many near-identical competitors often consolidate rapidly - sometimes through acquisitions, sometimes through the exit of weaker players, and sometimes through a large platform vendor absorbing the functionality into their existing suite. In each case, your chosen vendor may be affected. Understanding where your chosen solution sits in the competitive landscape helps you assess its long-term viability and the likelihood that it will remain relevant and independent.

This is also relevant to negotiating position. In a crowded market, vendors will typically be more flexible on price and commercial terms. In a market with fewer credible players, leverage shifts to the vendor. Being aware of how saturated the market is helps you go into commercial discussions with clear eyes.

Finally, consider what it means if a large incumbent - Microsoft, Salesforce, SAP, or Google - decides to build or buy into this space. For some AI categories, the risk of platform commoditisation is significant. Choosing a specialist vendor whose core differentiation could be replicated by a hyperscaler within a product cycle is a genuine strategic risk.

3 – Platform Lock-In and Exit Flexibility

One of the most consequential questions in AI technology selection is how easy it would be to leave. The AI market is evolving quickly, and a solution that is excellent today may be superseded or rendered obsolete within a few years. The ability to migrate to a different platform without catastrophic cost or disruption is not a sign of lack of commitment - it is good governance.

Lock-in in AI technology takes several forms. Data lock-in occurs when your data is stored in a proprietary format or location that makes extraction and migration difficult. Workflow lock-in arises when AI processes have been deeply embedded into operations in ways that are hard to unpick. Model lock-in happens when the vendor's proprietary AI model has been trained on your data in ways that cannot be replicated elsewhere. And contractual lock-in is exactly what it sounds like - multi-year terms, high exit penalties, and auto-renewal clauses.

When evaluating vendors, ask directly: what does data portability look like? Can our data be exported in a standard format? What happens to any fine-tuned or custom models if we end the contract? Is there a clear exit process? How long is the notice period?

Choosing a solution with open standards, portable data, and transparent exit terms gives you optionality. Optionality in a fast-moving market is valuable.

4 – Integration with Your Existing Technology Stack

AI technology rarely operates in isolation. For it to deliver genuine value, it almost always needs to connect with the systems and data sources your organisation already uses - your CRM, ERP, data warehouse, communication tools, and core operational platforms. Weak or non-existent integration means manual workarounds, duplicated data, and a solution that quickly becomes peripheral rather than embedded.

When evaluating vendors, look carefully at the quality and depth of their integration capability. Pre-built connectors with your key platforms are a strong positive indicator. An open API is important for custom integration, but should not be seen as a substitute for proper connectors - open APIs mean someone still has to build and maintain the integration, and that costs time and money.

Also consider the direction of data flow. Does the AI tool need to read from your systems, write back to them, or both? Does it require real-time data access, or can it work with batch updates? What latency is acceptable? These questions shape not just the technical architecture but the practical usefulness of the solution.

Organisations with complex, heterogeneous technology estates should pay particular attention to integration. The best AI solution for a greenfield environment may not be the best choice for an organisation with legacy infrastructure. Understanding the integration landscape before you select - not after - saves significant pain later.

5 – Data Privacy, Security, and Governance

AI technology involves data - often sensitive data. Whether it is customer records, financial information, employee data, or proprietary business processes, the AI tools you deploy will ingest, process, and in many cases learn from information that carries significant privacy and security obligations. How a vendor handles this data should be a primary consideration, not an afterthought.

Key questions include: Where is data processed and stored? Is data used to train or improve the vendor's underlying models, and if so, can you opt out? What certifications does the vendor hold - ISO 27001, SOC 2, Cyber Essentials, or sector-specific frameworks? How does the solution handle access controls, audit trails, and data residency requirements?

For organisations operating in regulated industries - financial services, healthcare, legal, public sector - the bar is higher still. AI tools that cannot demonstrate compliance with relevant regulatory frameworks are simply not viable options, regardless of how impressive their capabilities appear.

Governance is equally important. Many organisations are now developing internal AI governance frameworks that define which AI tools can be used, for what purpose, and with what data. Selecting a vendor whose approach to data and AI aligns with your governance framework - and who can evidence that alignment - avoids difficult conversations down the line.

6 – Transparency and Explainability

One of the defining challenges of AI technology is the 'black box' problem. Many AI systems - particularly those based on large language models or complex neural networks - produce outputs without being able to clearly explain how they arrived at them. For some use cases, this is tolerable. For others, it is unacceptable.

Consider what your use case requires. If AI is being used to generate first drafts of marketing content, the requirement for explainability is relatively low - a human will review and edit the output. But if AI is being used to inform credit decisions, flag HR performance issues, or prioritise customer service cases, the ability to understand and audit how a decision was reached is both a legal and ethical requirement.

When evaluating vendors, ask how they approach transparency. Can the system provide reasoning or confidence scores alongside its outputs? Can decisions be audited? Is the model's behaviour consistent and predictable, or does it vary significantly across similar inputs? Does the vendor have a published responsible AI framework?

Explainability is also increasingly a regulatory requirement. The EU AI Act, for example, imposes obligations on transparency and human oversight for certain categories of AI system. Understanding how a vendor's solution aligns with emerging regulation protects your organisation and builds stakeholder confidence.

7 – Proven Outcomes and References

The AI market is rich with impressive demonstrations, compelling case studies, and bold claims about productivity gains and cost savings. What is scarcer is independently verifiable evidence of real-world outcomes. When evaluating vendors, push past the marketing and look for proof.

Ask vendors to provide customer references that are genuinely comparable to your situation - similar industry, similar scale, similar use case. Speak to those customers directly, not through a vendor-facilitated introduction if you can avoid it. Ask what the solution actually delivered versus what was promised. Ask what challenges arose during implementation and how they were resolved. Ask whether the ROI has been achieved, and over what timeframe.

Also look at independent evidence: G2 and Gartner Peer Insights reviews, analyst commentary, and case studies published by customers rather than vendors. The most credible AI vendors are the ones whose customers are willing to speak publicly and in detail about the outcomes they have achieved.

In a market where proof-of-concept demos can be optimised to look impressive regardless of real-world performance, customer references are one of the most valuable tools available. Use them thoroughly.

8 – AI Model Quality and the Underlying Technology

Not all AI is created equal. Behind every AI product is a model or set of models - and the quality, recency, and fitness for purpose of those models directly shapes the quality of the outputs your organisation will receive. Understanding what powers a vendor's product is not just a technical question; it is a strategic one.

Key questions include: Does the vendor build their own models, or do they wrap an existing foundation model (such as GPT-4, Claude, or Gemini)? If they wrap a foundation model, how much customisation and fine-tuning have they applied? How frequently is the model updated, and what is the process for managing model changes that could affect outputs?

Vendors that build on top of foundation models are not inherently inferior - many excellent AI products are built on third-party models with significant domain-specific customisation. But it does mean their long-term differentiation depends on their ability to add value above and beyond the base model. As foundation models improve, the question of what the vendor adds becomes more important, not less.

Also consider accuracy and hallucination risk. For use cases where factual accuracy matters - legal, finance, compliance, technical documentation - ask how the vendor measures and manages the risk of AI-generated errors. A vendor that cannot provide a clear and credible answer to this question is a vendor to approach with caution.

9 – Implementation Complexity and Time to Value

Even the best AI solution delivers nothing if it cannot be successfully implemented within a realistic timeframe and budget. The implementation landscape for AI technology varies enormously - from tools that can be deployed in days through simple configuration, to complex AI infrastructure projects that require specialist technical resource and months of integration work.

When evaluating vendors, be explicit about what implementation involves. Who is responsible for deployment - the vendor, a partner, or your internal team? What does the implementation project plan typically look like, and what are the most common causes of delay? Are there case studies of implementations of similar complexity to yours?

Also ask about the availability of implementation partners. In the AI space, specialist implementation expertise can be scarce. If a vendor has only a small number of certified implementation partners, this creates capacity risk - particularly if all available partners are already engaged on other projects.

Finally, consider what 'live' actually means. Going live with an AI solution is rarely the end of the journey - it often marks the beginning of a period of tuning, optimisation, and embedding into workflows. Understanding the path from go-live to genuine adoption is as important as understanding the implementation project itself.

10 – Scalability and Future Capability

Your AI requirements today are almost certainly not what they will be in three years. The organisations extracting the most value from AI are not those that deployed a single tool - they are those that developed the capability to identify, adopt, and integrate AI progressively across their operations. The platform you choose now should be able to grow with you.

Assess whether the solution can scale in the ways that matter to your organisation. Can it handle increasing data volumes without degradation in performance? Can it be extended to new use cases without requiring a new procurement cycle? Does the vendor have a credible and transparent product roadmap that shows continued investment in capability? Are they adding functionality in response to customer feedback, or is the roadmap driven primarily by the vendor's own strategic agenda?

Also consider whether the vendor's approach to AI is genuinely forward-looking. The organisations selling AI point solutions that solve a specific, narrow problem may find their market eroded quickly as platform vendors build equivalent functionality into their broader suites. A vendor with a clear vision for where AI is going - and a credible plan for how their product evolves alongside that - offers more long-term security than one focused purely on the current moment.

11 – Commercial Model and Total Cost of Ownership

AI technology commercial models are more varied - and often more complex - than traditional software licensing. Pricing may be based on usage (tokens consumed, queries processed, documents analysed), on seats, on modules, or some combination of all three. Understanding what you are actually committing to - and what the cost trajectory looks like as usage scales - is essential before signing anything.

Look carefully at what is included in the base pricing and what attracts additional charges. Implementation, training, premium support, data storage, API access, and advanced features are all common sources of unexpected cost. A solution that appears affordable at the point of sale can become significantly more expensive as the deployment scales and the true cost structure becomes apparent.

Total Cost of Ownership should be calculated across a three to five year horizon. Include not just licence and implementation costs, but also the internal resource required to manage and maintain the solution, the cost of training and change management, and any integration or infrastructure investment. Set against this the expected return - productivity gains, cost reduction, revenue impact - to form a realistic business case.

Do not let cost be the primary driver of the selection decision, but do ensure you understand it fully before you commit. Surprises in AI technology costs are common, and they are rarely pleasant.

12 – Vendor Support, Customer Success, and Community

AI technology is still maturing, which means that even well-designed solutions require ongoing guidance and support to extract full value. The quality of a vendor's post-sale support and customer success capability is often the difference between an AI deployment that transforms a business function and one that quietly fades into underuse.

When evaluating vendors, look beyond the standard support SLAs. Does the vendor provide a dedicated Customer Success Manager for accounts of your size, or are you reliant on a shared support pool? How frequently are customer health reviews conducted? Does the vendor proactively share best practice, new feature guidance, and use case recommendations, or is communication primarily reactive?

Community is increasingly important in the AI space. Vendors with strong user communities, active knowledge bases, and regular customer events provide a richer support ecosystem than those relying solely on formal support channels. The ability to learn from other customers - particularly those solving similar problems - is a genuine and often underestimated source of value.

13 – Ethical AI and Responsible Use

AI technology raises ethical questions that do not arise with traditional software. Bias in AI outputs, the potential for AI to be used for surveillance or discrimination, the environmental cost of large-scale AI computation, and the impact of AI on employment are all considerations that responsible organisations are increasingly being expected to address.

When evaluating vendors, ask how they approach responsible AI. Do they have a published ethical AI framework or responsible use policy? How do they test their models for bias, and what action do they take when bias is identified? What controls exist to prevent the technology from being used in ways that could cause harm? How do they approach the environmental footprint of their AI systems?

These questions are not merely reputational. Regulators are paying increasing attention to AI ethics, and organisations that deploy AI without adequate governance are exposing themselves to legal and reputational risk. Choosing a vendor that takes responsible AI seriously - and can evidence that commitment - is both the right thing to do and the smart commercial decision.

Conclusion

Selecting AI technology is one of the most significant and complex procurement decisions that organisations face today. The market is large, fast-moving, and full of vendors making bold claims. The stakes are high - both for getting it right and for getting it wrong. The factors covered in this guide will not make the decision for you, but they provide a framework for making it with greater confidence and clarity.

The most important principle is this: do not be rushed. The urgency around AI is real, but the best decisions are made by organisations that take the time to understand their own needs clearly, evaluate the market systematically, and choose a vendor they genuinely trust. Choosing quickly is not the same as being bold. Being methodical is not the same as being slow.

At Viewpoint Analysis, we work with organisations at every stage of this journey - from initial market exploration through to final vendor selection and beyond. Whatever stage you are at, we can help.

How Viewpoint Analysis Can Help

If you are beginning to explore the AI technology market and want to understand the landscape before committing to a selection process, our AI Technology Matchmaker pages are a great starting point - regularly updated with vendor information, market insight, and practical guidance.

For a faster market scan, our Innovation Series and Matchmaker services provide rapid structured assessments of the AI market relative to your specific challenge - ideal for senior leaders who need a clear view quickly without running a full procurement process.

When you are ready to select, our Rapid Selection Services cover everything from requirements gathering and vendor shortlisting to running demonstrations, scoring, and supporting the final decision. We handle the process. You make the call.

And if you already have an AI solution in place but are not sure it is working as well as it should, our Technology Review services can help you assess whether to improve what you have or start fresh.