Artificial intelligence is already part of everyday business. Companies are testing generative AI tools, introducing AI assistants and exploring agents capable of automating increasingly complex tasks.<\/p>\n\n\n\n
But there is a significant difference between experimenting with AI and creating real business value from it<\/strong>.<\/p>\n\n\n\n McKinsey’s 2025 research found that nearly nine in ten respondents said their organizations were regularly using AI, yet almost two-thirds had not begun scaling AI across the enterprise. Only 39% reported an EBIT impact at enterprise level.<\/p>\n\n\n\n The challenge is therefore changing. For many companies, the question is no longer “How can we use AI?”<\/em> but rather:<\/p>\n\n\n\n How do we turn AI experiments into solutions that actually improve the way our business works?<\/strong><\/p>\n\n\n\n Starting an AI experiment has never been easier.<\/p>\n\n\n\n A team can use ChatGPT, Claude or Copilot, build a chatbot or test an automated process relatively quickly. But a successful demonstration is very different from a system employees can rely on every day.<\/p>\n\n\n\n A production-ready AI solution needs to work with the company’s data, tools, workflows, security requirements and people<\/strong>.<\/p>\n\n\n\n IBM research illustrates this gap: nearly 40% of AI projects initiated by surveyed organizations during the previous two years did not progress beyond the pilot phase.<\/p>\n\n\n\n So why is moving beyond experimentation so difficult?<\/p>\n\n\n\n One of the most common mistakes is starting with:<\/p>\n\n\n\n “We need to use AI.”<\/em><\/p>\n\n\n\n Technology should not be the starting point. The business problem should.<\/p>\n\n\n\n Companies should first identify where teams are losing time, where information is difficult to access and which repetitive tasks create unnecessary friction.<\/p>\n\n\n\n Instead of asking:<\/p>\n\n\n\n “Where can we use AI?”<\/strong><\/p>\n\n\n\n A better question is:<\/p>\n\n\n\n “Where are we losing time or doing repetitive work?”<\/strong><\/p>\n\n\n\n Once the problem is clear, companies can determine whether AI, traditional automation or another solution is the best way to solve it.<\/p>\n\n\n\n The objective isn’t more AI. It’s better operations.<\/strong><\/p>\n\n\n\n Imagine a customer support employee receives a request.<\/p>\n\n\n\n They copy it into an AI tool, generate a response, search another system for customer information, copy the answer back into the support platform and manually update the CRM.<\/p>\n\n\n\n AI has helped with one task, but the workflow itself has barely changed.<\/p>\n\n\n\n Now imagine:<\/p>\n\n\n\n Customer request \u2192 AI classification \u2192 relevant information retrieved \u2192 suggested response \u2192 human approval \u2192 CRM updated<\/strong><\/p>\n\n\n\n That’s a very different implementation.<\/p>\n\n\n\n McKinsey found that workflow redesign had the strongest relationship with an organization’s ability to generate EBIT impact from generative AI<\/strong> among the organizational practices it examined. Yet only 21% of respondents using generative AI said their organizations had fundamentally redesigned at least some workflows.<\/p>\n\n\n\n This is where much of AI’s potential lies: not simply adding another tool, but reconsidering how work gets done.<\/p>\n\n\n\n Not every process needs AI.<\/p>\n\n\n\n The strongest opportunities usually combine a clear business problem, repetitive work, accessible data and a measurable outcome<\/strong>.<\/p>\n\n\n\n For example:<\/p>\n\n\n\n Customer support:<\/strong> classify requests, retrieve relevant information and prepare responses.<\/p>\n\n\n\n Sales:<\/strong> summarize meetings, update CRM records and prepare follow-ups.<\/p>\n\n\n\n Internal knowledge:<\/strong> retrieve information from company documentation and provide contextual answers.<\/p>\n\n\n\n Operations:<\/strong> analyze information from different systems and generate reports or recommended actions.<\/p>\n\n\n\n Starting with a focused workflow also makes it easier to determine whether the implementation actually works before expanding it.<\/p>\n\n\n\n Even the best AI model will struggle if the information behind it is incomplete, inaccessible or poorly organized.<\/p>\n\n\n\n Deloitte found that 55% of surveyed organizations had avoided certain generative AI use cases because of data-related issues<\/strong>, highlighting the importance of data quality, security and governance when moving from experimentation to implementation.<\/p>\n\n\n\n But technology and data are only part of the equation.<\/p>\n\n\n\n Employees need to understand when to use an AI system, what its limitations are and where human judgment remains necessary.<\/p>\n\n\n\n IBM research found that 64% of CEOs believed successful generative AI adoption would depend more on people’s adoption than on the technology itself<\/strong>.<\/p>\n\n\n\n A technically impressive AI solution that nobody trusts or uses creates very little value.<\/p>\n\n\n\n This challenge becomes even more relevant with the rise of AI agents<\/strong>.<\/p>\n\n\n\n While traditional AI assistants primarily respond to user requests, agents can potentially perform multiple steps toward an objective: retrieving information, interacting with software and executing actions within a workflow.<\/p>\n\n\n\n For example:<\/p>\n\n\n\n Customer request \u2192 classify issue \u2192 retrieve context \u2192 search knowledge base \u2192 prepare response \u2192 update CRM \u2192 escalate if necessary<\/strong><\/p>\n\n\n\n Interest is growing quickly. McKinsey found that 62% of surveyed organizations were already experimenting with AI agents<\/strong> in 2025.<\/p>\n\n\n\n But greater autonomy also means greater responsibility.<\/p>\n\n\n\n Companies need to think carefully about permissions, cybersecurity, data access, monitoring and human oversight<\/strong>.<\/p>\n\n\n\n The important question isn’t necessarily:<\/p>\n\n\n\n “How autonomous can we make our AI agent?”<\/em><\/p>\n\n\n\n It’s:<\/p>\n\n\n\n “How much autonomy does this workflow actually need?”<\/strong><\/p>\n\n\n\n Moving beyond experimentation does not necessarily require a massive AI transformation.<\/p>\n\n\n\n A practical approach can start with five steps:<\/p>\n\n\n\n 1. Identify a real problem.<\/strong> 2. Map the current process.<\/strong> 3. Define a measurable outcome.<\/strong> 4. Build and integrate a focused solution.<\/strong> 5. Test, measure and scale.<\/strong> The important part is measurement.<\/p>\n\n\n\n If an AI implementation saves time, reduces repetitive work or improves a business process, that value should be visible.<\/p>\n\n\n\n If you can’t explain what the AI solution is improving, you probably shouldn’t scale it yet.<\/strong><\/p>\n\n\n\n AI models will continue to become more capable. Agents will become more sophisticated and new tools will continue to appear.<\/p>\n\n\n\n But access to technology alone will not determine which companies benefit most.<\/p>\n\n\n\n The difference will increasingly come from how effectively businesses integrate AI into their data, workflows and teams<\/strong>.<\/p>\n\n\n\n Experimentation remains important, but pilots should be a means to an end: identify what works, integrate it into real processes, measure the impact and scale where it creates value.<\/p>\n\n\n\n Because ultimately:<\/p>\n\n\n\n The goal isn’t to use more AI. It’s to make AI useful.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence is already part of everyday business. Companies are testing generative AI tools, introducing AI assistants and exploring agents capable of automating increasingly complex tasks. But there is a significant difference between experimenting with AI and creating real business value from it. McKinsey’s 2025 research found that nearly nine in ten respondents said their…<\/p>\n","protected":false},"author":5,"featured_media":4842,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[662,78],"tags":[367,373,388,386],"class_list":["post-4841","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-2","category-uncategorized-en","tag-ai","tag-cybersecurity","tag-fintech","tag-innovation"],"yoast_head":"\nFrom AI experiments to real implementation<\/h2>\n\n\n\n
1. Starting with AI instead of the business problem<\/h2>\n\n\n\n
2. Keeping AI disconnected from real workflows<\/h2>\n\n\n\n
3. Choosing the right use cases<\/h2>\n\n\n\n
4. Data and people matter as much as the technology<\/h2>\n\n\n\n
From AI assistants to AI agents<\/h2>\n\n\n\n
How to move from pilot to production<\/h2>\n\n\n\n
Find a bottleneck, repetitive task or inefficient workflow.<\/p>\n\n\n\n
Understand the people, tools, information and decisions involved.<\/p>\n\n\n\n
Decide what should improve: time saved, fewer manual tasks, faster responses, lower error rates or another business KPI.<\/p>\n\n\n\n
Connect AI with the right data and tools rather than keeping it isolated.<\/p>\n\n\n\n
Use feedback from real employees and expand only once the solution demonstrates value.<\/p>\n\n\n\nMaking AI useful<\/h2>\n\n\n\n