Nina runs a mid-sized manufacturing company that has been expanding rapidly. Her sales team, however, is struggling to track leads and nurture client relationships. Nina knows she needs a CRM.
A few years ago, her first step would have been to open her laptop and type “best CRM for mid-sized businesses” into Google. Hours of browsing vendor sites, comparison tables, and online reviews would follow. She would then line up demos, speak to sales teams, and finally make her choice.
But this time, Nina approaches the decision differently. Instead of opening a browser, she turns to her AI assistant and asks: “What’s the best CRM for a mid-sized manufacturing company?” That single question sets her on a new kind of buying journey - the AI journey.
Search behaviour has shifted. Traditional search engines present users with endless lists of links, forcing them to do the heavy lifting of research, filtering, and comparison. AI, by contrast, interprets intent, synthesises information, and narrows choices immediately. This changes the rhythm of buying: instead of navigating multiple fragmented steps, users move through a compressed, guided path where AI acts as the first point of trust.
|
Traditional Journey |
AI Buyer Journey |
|
Awareness – User realises a need |
Need Expression – User voices it directly to AI |
|
Research – Browsing sites, reviews, comparisons |
AI-Driven Discovery – AI shortlists or recommends |
|
Consideration – Evaluating multiple options |
Trust & Validation – Cross-check AI results selectively |
|
Decision – Independent choice by user |
AI-Guided Decision – Choice heavily AI-assisted |
|
Purchase & Onboarding – Brand-led |
Personalised Purchase & Experience – AI filters and supports |
|
Post-purchase loyalty – Brand-driven |
Feedback Loop – User-AI interactions shape future |
While the reasons behind this change are clear, the challenge for leaders lies in navigating it without losing focus or direction. The six stages of the AI journey outline this progression, helping leaders chart a realistic path forward.
In the AI journey, users don’t begin with vague awareness and hours of search. They start by directly articulating their needs in plain language, through voice, or even by sharing an image. This compresses the early stages of the buying journey, removing much of the scatter that comes with traditional research. The shift is that intent is now expressed in a single, clear interaction with AI.
For Nina, this meant skipping the usual “why CRMs matter” articles. She simply asked her AI assistant: “What CRM works best for a mid-sized manufacturing company?” That one question replaced days of online exploration.
AI doesn’t just give a list of links. It analyses the request, curates possible answers, and surfaces only the most relevant options. Instead of long lists of search results, users get concise, synthesised recommendations that are easier to act upon. Discovery becomes sharper and faster, but the breadth of exploration narrows.
When Nina posed her question, her AI didn’t dump twenty CRM names at her. Instead, it presented three platforms tailored to manufacturing, with pricing tiers summarised. Without visiting a single vendor site, she already had a clear starting point.
Because AI is now the gatekeeper, the critical question for users becomes: Can I trust this output? Instead of evaluating dozens of sources, people focus on validating whether the AI’s recommendations are reliable. Often this involves cross-checking with peers, scanning a few reviews, or even running the same query across another AI tool. Trust and validation take the place of broad, time-consuming exploration.
Nina, for example, shared the shortlist with her sales head and quickly reran the query on another AI. Seeing overlap between results reassured her. Instead of sifting through endless case studies, she reached confidence in minutes.
Decisions are no longer based only on independent comparison. AI actively guides the process, highlighting the best fit and nudging users towards action. In some cases, AI even integrates directly with vendor systems to allow scheduling, demos, or trials within the same interface. The role of AI here is not passive. It becomes a decision partner.
Nina noticed this shift when her AI recommended a CRM that integrated seamlessly with her existing tools. With a single click from the AI interface, she was able to schedule a demo. What would once have been a long chain of emails now became a streamlined, AI-enabled decision.
Once the decision is made, AI continues to shape the buying experience. It doesn’t just connect users to vendors, it personalises the purchase path. Budgets, features, and onboarding steps are surfaced in ways tailored to the user’s needs. The process feels less like being sold to and more like being guided through a customised experience.
In Nina’s case, after signing up, the AI provided step-by-step onboarding tailored to her business type. It also highlighted FAQs and suggested automation features that other manufacturing companies commonly adopt. Her early experience with the CRM felt intuitive because AI had pre-empted her learning curve.
The journey doesn’t end at purchase. Every interaction feeds back into the AI, improving how it responds in the future. This creates a loop where the user’s preferences, behaviour, and satisfaction shape the AI’s next set of recommendations. Loyalty shifts from being only between user and brand to also being between user and AI.
For Nina, providing feedback after her onboarding experience meant her AI assistant could fine-tune future suggestions. When she later explored HR tools and marketing automation platforms, her AI already had a sense of her preferences. The result was a journey that kept getting smarter over time.
As AI becomes the default interface, the buying path will compress further. Multi-agent interactions may emerge, where one AI consults another before presenting options. Entire purchases may happen within AI environments, bypassing vendor websites altogether. For businesses, this means visibility will depend not only on SEO but on AI discoverability, whether the AI chooses to recommend you in the first place.
For Nina, the CRM decision was never just about software, but about choosing a partner for growth - one that could keep pace with her needs in a world where change is constant and decisions are data-led. Her journey mirrors that of countless businesses today: navigating uncertainty, weighing complexity, and leaning on AI not for quick fixes but for sustained clarity and direction.
The six stages are not a checklist to be ticked off, but a continual cycle of learning and recalibration. As AI continues to reshape the way buyers think and act, the real question is not how companies can sell better, but how they can stand alongside customers like Nina, and help them make choices that feel not only smart today, but right for tomorrow.