The AI market continues to swell in what detractors claim is a bubble and proponents insist is the future of several industries. As AI adoption continues, we now have a number of key takeaways for businesses looking to integrate AI into their products, services and workflows.
As per an MIT report on the topic, an extremely large majority of AI pilot projects promising generative AI are failing to take off. As Fortune magazine claims, this is largely due to poor targeting; AI is being used for tasks it is not particularly suited for, and not integrated into existing business needs and workflows well.
It doesn’t help that many such AI models are incapable of using primarily internal data as their data set, leading to some problems of relevance or accuracy. This in combination with a tendency of companies to try and shoehorn AI into marketing or sales department use, where it tends to do poorly, has led to the above mentioned failure rate.
How can we tell if a tool has potential?
There’s no easy way to know if a tool will work right for you; there are, however, some clear indicators that the tool is going to have problems:
Unrealistic timelines
There is a tendency to assume AI tools will slot easily into your organization’s needs – but AI isn’t plug and play. If an AI vendor promises production-ready solutions in weeks, leaders should proceed with caution.
The ‘we’ll replace humans’ narrative
The big sales pitch of AI has consistently been a simplified org chart and cut costs; the reality is largely different. AI can simplify some processes, especially those that are prone to automation in any case, but most successful AI deployments rely on human-in-the-loop systems — whether for exception handling, oversight or ethical review.
Lack of integration with existing tech stacks
This is possibly the biggest one – too many AI pitches will lack a plan for how to integrate into your structures. It’s one thing to show off a tool in isolation as a tech demo or proof-of-concept, but another entirely to have it function in tandem with all surrounding systems. A company will already have sets of ERP systems, CRMs or cloud data platforms; the AI has to integrate into these systems and their workflows, or instead of creating value it creates a separated data silo and eventually a headache.
It’s also key to remember the potential security issues inherent with integrating any new tool into your structures, AI or not; the tool will need access to your systems, and will inevitably run the risk of opening new backdoors where none previously existed.
What’s the path to AI adoption?
Set up a checklist, and run down the following questions:
- Timelines: What are the milestones and how long will each phase realistically take?
- Human roles: How will our teams interact with the system? Where will workflows hand off from AI to team members and vice versa? What’s the escalation path?
- Integration: How does this fit with our existing tech stack? What APIs or connectors are supported?
- Readiness: What processes, governance and data structures need to be in place first?
- ROI: How will success be measured? Is there a clear path to value, not just experimentation?
The answers will likely shape your path for adoption – or rejection – of the tool in question, as well as open new avenues of thought and discussion as the AI revolution rolls on.
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