Business leaders overwhelmingly understand that AI is important. They’ve read the newspaper articles, heard the success stories, likely even sat through a few informational sessions about machine learning and robotic process automation. Yet there is a significant chasm between acknowledgment of the importance of AI and learning how to practically apply it to one’s own business.
AI is rarely in question, it’s simply a matter of cutting through the clutter, identifying genuine opportunities and implementing solutions that yield results instead of useless pilot projects that waste time and budget. Here we explore what it really takes to make AI work in your favor.
Start with Problems, Not Solutions
When businesses fail to approach AI in the proper context, they immediately set themselves up for failure. They see it as something they need to have to remain competitive, then start sourcing AI solutions without even understanding what problems need solving.
Those companies that are successfully implementing AI solutions are doing just the opposite – they’re starting with their biggest problems. Whether it’s customer service being overwhelmed with requests seasonally so no one gets the attention and support they need, or sales reps spending 50% of their time performing administrative chores instead of converting leads, or inventory management consistently at a 200% threshold or out-of-stock and losing sales on key items – they start at their headaches and then find a solution.
Only then can companies determine whether AI is a potential solution (sometimes yes, sometimes not), but at least they’re making choices based on business performance and not trends.
Get Real About AI Capabilities
AI is something that’s continuously advancing, yet many don’t realize what’s realistically possible today. Right now, AI works best on specific types of tasks – including but not limited to – analyzing patterns or recommendations based on extensive data sets, automating repetitive action, predicting using historical data and understanding rudimentary customer questions/inquiries.
Where AI fails is a dependence on creativity, appropriate judgment in ambiguous scenarios, and a nuanced perspective relative to other humans. In short, humans struggle to keep up with time-consuming tasks but also have no issue with creative endeavors, which means that if AI learns patterns for tasks that take time away from humans, but AI can accomplish in easily translatable ways, that’s the nexus.
For example, customer service chatbots are a good source – basic questions can help save time; human agents can focus on more nuanced inquiries that require compassion and creativity to solve appropriate concerns. This is realistic application instead of unrealistic potential.
Clean Your Data House
The most challenging part about getting ready for AI is an honest assessment of your data process – most companies learn that their data flow is far messier than they thought. Information lives in disparate systems that don’t talk to each other, inconsistent data entry, different access levels over time, and historical gaps for what may have once been desired.
You don’t need to have perfect systems in place, but you do need usable information. This means take the time to do the dirty work required beforehand – cleanup, organization, standards development and autonomy needs to be achieved first before worthwhile application can take place. Otherwise, it’s like trying to make a cake with mystery ingredients.
The good news about all this diligence is that even if you don’t implement AI systems, your data will be improved for your business outcomes as well as other information-based initiatives; better quality means better decisions across the board. But even more so; when it comes to AI, garbage in equals garbage out relative to results.
Identify Good Opportunities for Pilot Projects
Unless you’re prepared to overhaul your entire operation immediately – and usually that’s a bad idea – you need to find a good pilot project to implement AI within your organization. One that’s important enough yet contained enough to manage.
Processes that are repetitive – meaning they’re overtly time-consuming and data-dependent as they currently are – can bring AI solutions. Whether it’s invoice processing in accounting, lead scoring in sales or predictive maintenance in inventory when equipment is too expensive for when it breaks unexpectedly – these represent good opportunities.
A successful pilot project achieves two goals: measurable value and team efficacy. First, it shows that the investment is worth it. Second, it helps people figure out how to implement AI effectively, so they’re taught how for larger projects down the line. It’s not just about value, it’s about know-how.
Gain Internal Buy In
AI will not operate without people; therefore, it’s crucial to manage expectations around who will be impacted by AI solutions once they’re implemented. If they see it as a threat to their jobs and adds additional stressors, you’ll get pushback at every corner; if they see it as a means of making their job easier and more interesting – and ultimately newer – you’ll get champions for implementation.
Part of this process is communication – honesty about what’s going on and why must be relayed. Show people who are overloaded with mundane tasks that AI can take care of those chores so they can spend their time with more relevant and valuable work. Include perspectives in the planning process so it doesn’t feel like change was thrust upon them.
Training is crucial – people not only need to know how to work new programs but also why certain decisions were made so they can ultimately implement judgment about when to implement AI’s findings or override them.
Partner With Legit Experts
Few companies have good AI expertise just floating around internally – and that’s okay. What’s not okay is being too proud to know when you need outside help or learn when to source legitimate partners who genuinely understand your industry (and not just the technology).
When vetting partners, use discretion – technical acumen is great but they’re looking for more than that; do they ask good questions? Can they explain themselves without excessive jargon? Have they worked with similar companies? If you’re someone who wants good implementation with AI systems, the best ai consulting firms will make your success worlds apart from costly mistakes.
The right partner isn’t going to hand you a solution and leave you be; they’re going to hold your hand through implementation while teaching your team how to fish themselves so you can ultimately internalize down the line without relying upon someone else.
Measure What Matters – and a Little Extra
Setting application metrics from day one ensures success as some vague idea like “increase efficiencies.” These should be quantifiable; reduce customer service response time by 20 percent; increase leads generated by 15 percent; reduce monthly spending on inventory carrying costs by 50 percent.
Two goals emerge from this: ensure project focus on business value and objectively determine if the cost was worth it. If you can point out value, then it’s exponentially easier to expand other elements.
Moreover, don’t forget about qualitative measures; things you didn’t expect but still warrant value – an empowered customer service team may have found alternative products/interventions their customers kept asking about; while that’s not necessarily poor it’s good to document these if only for third-party assessment down the line.
Plan for the Long Haul
AI is not a project – it’s a capability. As such, as the company grows, interconnectedness occurs and technology advances further you’ll want to monitor successively – but you can’t do that if you treat it like a project instead of ongoing concern.
There must be processes in place for maintenance and updates for consistent value over time. Companies that think they’re getting away from implementation again will fail – they were just getting started!
Accept the Inevitable Progression
AI isn’t easy – but it’s worth it if you approach thoughtfully from intervention types of real problems, manageable projects, internalized buy-in and measurement with honesty along the way. You don’t need to become an expert overnight; you just need practical application that pays off – and you’ll learn along the way.
The companies winning with AI right now aren’t necessarily large enterprises or those with a strong technical background; they’re the ones asking really good questions and making smart decisions about where implementation makes the most sense right out of the gate – and executing successfully on a narrower scale before moving bigger.
That’s something any leader can afford to do – from CEO to CMO – and regardless of the industry vertical.
