Why many companies think they’re ready: they have data and a budget. Why AI consultants know otherwise? It’s far deeper than data acquisition and allocation. The first weeks of an AI consultant’s engagement don’t even seem like implementation, they’re playing detective to determine whether your company can handle what’s about to happen.
This isn’t a way for them to be mean and dismissive, either. It’s a way to prevent disaster down the road that could require termination of the contract. If an AI consultant tells every potential client that they’re ready to go, then they aren’t ethical. If a consultant wants to push AI on a company, there are financial gains for them down the line, but it means a slow burn of the implementation game for years to come for a company that’s not actually prepared.
Does Data Make You Ready?
Assessments typically start with data. Not whether you have it (everyone has data), but rather if it’s current and useful for the AI purposes at hand.
Consultants want to know if different departments utilize similar systems or if marketing and sales data exists in one place while customer service and shipping has their own set of data points. They want to know about formats; are all customers logged the same way? Or is there variability in how John Doe gets typed in twelve different times because every employee used a different method to type him up.
Yet AI models need structure and needs to be trained, which means there’s an elimination of noise. If customer records include their address, product name, source of acquisition, and feedback, yet the next record only includes the first and last without configuration, that’s noise that AI can’t work through. If product names exist in one spreadsheet but duplicate entries boast formatting errors, that’s information that can’t be vetted through AI either. Consultants need to know how much cleanup needs to happen before training even begins.
They also want to know about tenure and longevity; AI deployments usually require three+ years’ worth of realistic data to extrapolate critical metrics. If you’ve only logged six month’s worth of occurrences, that isn’t adequate either. On occasion, data has been in existence for ten plus years but it’s so differently formatted than the current records that it’s nearly impossible to recreate from scratch without major effort on the consultant’s part.
Does Your Company Have the Technical Capacity?
The next step consultants look for is your infrastructure, are you cloud-based? Are you on-prem? Do you have established systems in place that work but now need processors added or chips taken for better aggregate analysis?
Many companies find out that their infrastructures need major upgrades before they’re even ready for AI. Software that’s worked well for centuries ends up being the bottleneck for business intelligence machines or data pipelining as well as big data analytics. This means time and money that companies typically underestimate, consultants want to know if you’re looking at minor upgrades or a complete overhaul.
They also want to assess your team, do you have personnel who can troubleshoot API integrations or fix pipelines as needed? Will it be able to help when something goes wrong or will everything need an outside line of communication? Getting experienced with the best ai consulting firms reveals where a company has technical capacity and where one does not, meaning that help is needed in team training or educated hiring before implementation can occur.
They also want to know if there’s someone on your side who can continue after implementation without further resources. If models need continued watch and retraining daily, that’s time as money that your enterprise needs to cover on its own.
Many companies decide against external team support after implementation because they’ve run out of money but AI isn’t a learn-it-and-leave-it kind of deal; this means either your team does it or a consultant steps back in which takes more time and costs more money.
Is Your Company Culture Ready?
This is where assessments get touchy feely. There’s no right or wrong answer, but good consultants assess whether your culture can support change.
They want to know how prior adjustments to technology have affected your staff; is there a lack of awareness of changes made after decisions are finalized? Does leadership utilize data-derived assessments in critical thinking or do they act on gut instincts better? Will middle management appreciate support from AI or find it cumbersome and threatening?
They also want to know how well your departments communicate. Is there a culture of silos? AI aims to improve business elements over time; if there’s never cross-departmental communication, your systems may run well together but they’ll end up being awkward teammates come implementation.
Assessing timelines and deliverables is important too; do executives expect instant gratification? AI solutions aren’t put in place with one click. They take time – and if leadership goes AWOL because day two doesn’t bring results, then it’s not worth moving forward.
Have You Documented Your Own Processes?
This is something that companies typically fail; consultants want to see how well you’ve documented current business processes because if no one can explain why something happens the way it does or how certain things have evolved, you can’t expect an AI system to learn, or improve, something subjective.
AI requires a baseline understanding before assessing where enhancements can happen, they want process standards that are amenable for mapping out business awareness metrics based on those learnings. They also want clear expectations; how do you measure success metrics for ongoing opportunities with AI support, if you can’t quantify what’s going on now, how will you measure change?
But without these clear statistics now, weight points, duration logs, successful measurement counts, consultants assess whether you’ve gotten an arbitrary number or if you’ve omitted collecting things unnecessary.
What’s Your Budget Reality?
Finally, there’s budget reality, which goes beyond affording a consultant. Veteran assessment consultants want to know if you’ve anticipated costs associated with data cleanup alone because often this is far more than the average implementation request.
They assess whether you’ve anticipated ongoing costs (cloud resources, software licenses per model used, system allowances) as well as adjusting funds for unforeseen circumstances (which almost always arise).
A good timeline often costs unnecessary amounts; they assess whether expectations are too high as over-friendly suggestions tend to inflate prices before work even begins, and they assess whether commitments are relevant if these changes exceed what you’re accustomed to paying.
The Final Go/No-Go Decision
Deciding whether you are ready provides great consultants with the proper information needed to be honest; sometimes that means waiting six months until work can begin; sometimes it means starting small instead of diving right in, and sometimes it means recommending something else entirely as your solution.
The companies that ultimately succeed with AI findings are the ones that embrace assessments rather than try to convince them why existing gaps don’t matter. They use assessment periods as times to plug holes rather than justify why they’re worthy of implementation regardless. Understanding readiness means not checking boxes but rather reinforcing what’s needed ahead of time so when the weight falls on top down the road, there’s enough of a sturdy foundation beneath all requirements.
There’s nothing worse than getting an honest no during implementation when money has already been spent and momentum gained, assessments may drag on over time but they’re life-savers when it comes down between dealing with AI as your best company experience or biggest loser sunk cost fallacy ever
