In a nutshell:
- Many business leaders believe AI is critical to success in the next five years.
- Implementing AI has historically faced challenges such as poor data quality, high costs, data security concerns, and resistance to change.
- However, AI platforms are evolving, making implementation easier and necessary for various teams within organizations.
- Roadblocks to AI implementation can be overcome by addressing data preparation, cost, security, and embracing change.
- Choosing the right AI implementation strategy can help businesses overcome these roadblocks and optimize their decision-making processes.
There’s a lot of buzz lately about how artificial intelligence will impact virtually every team, including marketing, sales, and supply chain. Yet, implementing AI hasn’t always been a smooth process.
94% of business leaders believe AI is critical to success over the next five years.
— “State of AI in the Enterprise, 5th edition report,” Deloitte
Throughout many AI implementations, business leaders have encountered numerous hiccups on the road that slowed down results — sometimes for months — and discouraged them from continuing on their digital transformation journeys. These roadblocks included things like:
- Poor data quality. Effective results from AI require quality data sources. Transitioning to AI can feel impossible if there isn’t easy access to data or an automated way to clean and prepare it.
- High costs. Historically, AI implementation has required companies to recruit and retain expensive and rare data science and data engineering staff.
- Data security concerns. Any time data is involved, security is something to be aware of. Some leaders can feel too concerned about security to consider transitioning away from on-prem data sources and using cloud-based AI technologies.
- Resistance to change. Change can be hard. Some leaders aren’t interested in big changes to the status quo or find that implementing AI feels too overwhelming, risky, impractical, or all of the above — and, therefore, isn’t worth the effort.
But what if we told you each of these barriers is a thing of the past?
If you’re wondering how AI is implemented today and if it’s gotten any easier to use effectively and securely, read on. We’ll discuss how the challenges of implementing AI aren’t nearly as problematic as they once were, as long as you secure the right solution and partner.
How AI is evolving
Transitioning to AI is a big step in any company’s digital transformation, and early iterations of AI (including generative AI, machine learning, and predictive analytics) can often be clunky and narrow in focus.
The good news is that AI platforms are evolving every day, and it’s getting easier for business leaders to actually move forward with successful AI implementation.
In fact, AI solutions are becoming imperative for respective teams within an organization (think marketing teams, sales teams, etc). For example, marketers have specialized AI solutions specifically for optimizing campaign performance and return on ad spend (ROAS). Similarly, sales teams use AI solutions to help target the right audience with the highest chances of converting. AI solutions can also support supply chain teams, analyzing inventory and providing more accurate forecasting.
Indeed, the roadblocks to implementing AI aren’t as foreboding and overwhelming as they once were. And, with opportunities for cross-departmental benefits, implementing AI is not only easier — but also necessary.
Crushing AI implementation roadblock #1: The data prep hurdle
Most businesses collect a great deal of data. From marketing teams constantly beefing up their CRM databases to analytics teams processing their website analytics, collecting meaningful data is crucial to making effective business decisions.
Since you’re probably already collecting huge amounts of data, implementing AI is the next step to maximize its value. However, manually intensive data prep has traditionally held many organizations back from harnessing the full value of their data.
Modern automated data preparation and cleansing can take data from its raw and messy form into pristine, AI-ready datasets with very little human effort — meaning you can point the AI in the right direction, with the highest quality data, and then watch it do its thing.
Crushing AI implementation roadblock #2: Modern AI doesn’t require expensive experts
High licensing costs are a barrier to implementing any new technology. Add in the additional cost of specialized talent and training, and your TCO (total cost of ownership) climbs to unnerving heights.
Yet, implementing AI no longer requires insanely expensive software or hiring costly experts to build predictive models and dashboards. Instead, new low-code predictive analytics platforms provide AutoML capabilities to level up your existing staff’s skills — such as your data analysts’ SQL ability — to easily build and quickly deploy predictive models.
No need to bring in expensive data engineers or external consultants. AI solutions have everything you need to start maximizing the value of your data.
Crushing AI implementation roadblock #3: Concerns about data security
Concerns over data security are legitimate, no matter what type of data or technology you’re working with. The best AI providers now offer extensive data security assurances and state-of-the-art protections, meaning transitioning away from on-prem data sources and implementing cloud-based AI technologies doesn’t have to be such a scary prospect.
Crushing AI implementation roadblock #4: Be open to change
There’s no doubt that the current AI buzz has increased enthusiasm for every kind of AI, not just generative AI but also automated, predictive solutions. In fact, many new marketing platforms come equipped with AI tools, meaning that AI implementation is becoming the norm rather than a unique advantage. Companies that don’t catch on risk falling behind.
In addition, improved predictions of customer activity can help business leaders make more satisfactory decisions and see more accurate cost-saving and dollar-generating results. Those results — and increasing demand — speak for themselves. In one example, gaming publisher KSG Mobile used Pecan’s predictive AI platform to calculate customer lifetime value. These insights helped KSG’s business leaders make more informed marketing decisions. As a result, their ROAS soared. Similarly, Armor VPN, leveraged Pecan to sharpen its user acquisition strategy for its privacy-focused mobile app. The platform’s daily insights helped the user acquisition team make more informed decisions on campaign adjustments and budget distributions.
Choose the right AI implementation strategy for your business
Ready to implement AI? The easiest way is to choose a partner who will help you every step of the way. Whether you need a provider to help implement AI and handle the nitty-gritty details on an ongoing basis or if you just want the best in predictive analytics platforms for your team, Pecan offers fast, simple integration with your existing technology and your current data storage situation. Its low-code interface means users can start seeing results in minutes — you’ll be able to allocate resources more efficiently and optimize your business decisions without hiring expensive new data scientists. And once you start using AI to generate predictions and guide business decisions, you may find that the road ahead looks clearer than ever.
Want help choosing the right predictive analytics strategy? Grab our guide or request a demo.
