How AI Is Transforming Businesses in 2026

The AI market is growing fast & that's not news. What's worth saying is that most companies still don't get value out of it, and the gap between "we use AI" and "AI changed how we work" is where the money goes missing.

Two ways companies approach this

There are two kinds of AI projects. The struggling ones start with "what's the coolest AI technology we can buy?" and go chasing whatever looks innovative. The ones that work start with different questions. What specific problems are costing us the most? Where are teams losing hours to repetitive work? What would free people up to do the work that matters?

Buy the platform first and figure out the use case later, and the platform ends up collecting dust. Start with one modest pilot on one problem (predicting which customers are about to leave is a common first one) and you have something you can scale from. Same market, same timeframe, very different outcomes.

Also worth being honest about what "we use AI" means in most companies. Often it's that someone has a ChatGPT subscription. There's a big difference between playing with AI tools and building them into how the work gets done. The companies getting this right treat AI as a core capability with an owner, put money into data infrastructure before fancy models, and bring their teams along with honest conversations about what changes, not just technical training.

Where business intelligence meets AI

The interesting part is what happens when AI sits on top of the business intelligence you already have.

Old-school BI is driving while looking in the rearview mirror. You can see where you've been, not where you're going. AI-powered systems are the windshield, what's happening now & what's likely next.

The practical version: inventory decisions made on sales data that's weeks old means you're always reacting. A system that processes customer behaviour as it happens, market trends from social media, supply chain updates, competitor pricing changes, even weather patterns that affect demand, feeds predictive models that let you act before the problem shows up. Better forecasts mean less stock you can't move and fewer stockouts of what people want.

The data foundation nobody wants to talk about

None of this works without clean data, and this is the lesson most companies learn the expensive way. A churn model that looks great in testing and does barely better than a coin flip in production is almost never an AI problem. It's customer information scattered across systems, inconsistent formatting, duplicates, errors nobody cleaned up.

The fix is months of cleaning and organising data before trying again. Frustrating, but the second attempt is the one that works. Invest in data quality before you invest in algorithms - clean, well-organised data makes every AI application after it faster, cheaper & more effective.

On picking a platform, with Google Cloud AI as the example

The shortlist is usually Microsoft Azure, AWS and Google Cloud, and Google Cloud AI is a reasonable place to start if you don't want to hire a team of PhDs before you get anything running. Machine learning, natural language processing, computer vision and data analytics sit in one place, which matters because juggling multiple vendors turns into an integration project that eats the time you were trying to save.

The multimodal side is the part that's new. It processes text, images, audio and video together, so a quality control process can look at product images, written specs, customer feedback and support call audio at the same time. And a support team (the actual support team, not developers) can build something that categorises tickets by urgency, spots trending issues early, routes complex queries to specialists and handles the routine ones on its own, then plug it into Salesforce or Zendesk.

Where it makes a difference

The question is where to start without wasting money.

Customer service. Modern chatbots have moved well past the "I didn't understand your request" systems everyone hates. The good ones handle genuinely complex interactions and, this is the important bit, know when to hand off to a human. The reason satisfaction tends to go up rather than down is that customers get instant answers round the clock on the simple stuff, and the support team only sees the problems that need empathy and creative thinking. If that's where you're starting, WhatsApp CRM is the practical version of it for a lot of businesses.

Finance. Invoice processing, compliance checks, payment reconciliation - days of manual work that automation does with fewer errors. The less obvious benefit is what the finance team does with the time: analysing spending for cost savings, building better financial models, finding process improvements across the company instead of doing data entry.

Predictive maintenance. For companies with physical operations this is often the highest ROI use of the lot. Computer vision catches product defects more consistently than human inspectors, and sensor data from equipment predicts failures before they happen - less unplanned downtime, longer equipment life.

Understanding customers. Sales forecasting gets a lot better when the models consider historical sales, customer behaviour, market trends, competitor actions and macroeconomic indicators together. Better forecasts flow straight into inventory, production planning & cash flow.

What the companies that get it right do differently

Start with problems, not technology. Every failed AI project starts with "let's implement AI" instead of "how do we solve this specific thing". A sane approach: find the most painful operational bottleneck, put a cost on it (time, money, customer impact), ask honestly whether AI is the best fix, compare it against redesigning the process, training or hiring, and only then pick technology. Sometimes AI is the answer. Sometimes better processes or one more hire works better. AI is one tool in the kit.

Data infrastructure first. The best algorithm is worthless on garbage data. Companies that get real value usually spend most of their initial AI budget on data infrastructure and governance, which sounds backwards until you've watched a model fail on bad inputs.

Bring the team along. The technology is rarely the hardest part. People are. Your team might fear for their jobs, resist changing workflows they know, or not understand how to work with the tools. The way through is involving them from day one - support reps designing the chatbot escalation rules, QC inspectors helping train the vision system. People who help build the thing become its advocates. Be clear that AI takes the repetitive work so humans get the interesting problems, show the career paths for people who lean in, and train properly on how the work changes.

What's next

The term going round is "agentic AI". Instead of tools that need constant direction, agents take a high-level goal, plan the steps and execute with minimal oversight, while a human stays in control of the critical decisions. Google Cloud's own predictions point the same way, teams moving from doing every task by hand to directing agents on what needs doing.

Multimodal AI is going from novelty to expected. Early adopters report big productivity gains in specific functions, and the companies that put that saved time into higher-value work are the ones that pull ahead.

AI stopped being something to worry about later. The companies without it in their core operations are going to struggle to compete, & the barrier is no longer technology or budget, it's strategic thinking and disciplined execution. So, what problem are you tackling first?

Questions we get asked

What is an AI solution business, and how is it different from traditional business intelligence?

An AI solution business builds artificial intelligence into its operations to automate tasks, predict outcomes and improve decisions. Traditional business intelligence shows you historical data. AI adds predictive insight and, increasingly, autonomous action on top of it.

How much does implementing Google Cloud AI platform cost?

A typical mid-sized business should expect a modest investment for an initial pilot over a few months, scaling to a more significant implementation over a year or two. Most companies see ROI within the first couple of years, with returns growing after that.

What are the biggest challenges in implementing AI systems?

Three, and they're the same three every time: data quality and infrastructure, change management, and realistic expectations. AI needs clean, centralised data. Teams have to be brought round to new workflows. And meaningful results take time.

How long does it take to see ROI from AI investments?

Quick wins like customer service chatbots show ROI in a few months. Complex implementations like predictive maintenance typically take over a year.