Where AI Programs Fail – Protect your investment in enterprise AI and avoid where AI programs fail most – Bell Integration, your enterprise AI partner for the future

Don’t let your AI programs fail before they get started, look out for the most common points of failure in the implementation and orchestration of your enterprise AI program

Where AI programs fail most is right at the starting block. Get off on the wrong foot, and your AI program is doomed before it gets started, and it will only get worse without help.

When it comes to AI initiatives in organizations of all types and sizes, one thing is clear, those that mature their AI prowess fast enough, and reliably enough, will outperform their peers, in fact, a recent study of some of the world’s leading companies showed that companies with mature and successful AI programs on average can attribute up to 30% of their revenue to AI programs within their organization, and further, those that have been able to successfully conceive, strategize, implement, and orchestrate AI in the enterprise to help solve real operational challenges that were identified in the conceptual stages of the AI program, had revenue growth rates that were as much as 50% higher than their peers who did not. The downside is, not many of the organizations surveyed fell into this category of having a mature, successful AI program, or even successful pilots, or smaller program trials that were mature for that matter, even less in production. With the majority of medium to large enterprises already working on AI initiatives, why such a low rate of successful AI programs? Why is the road littered with AI program failures?

Pitfalls, and many of them. Bell can help.

Where AI Programs Fail – Reason number 1, most AI programs fail due to lack of real direction, objectives, goals and an alignment with operational challenges that are pre-defined

It seems odd, but true, that in a corporate world full of validated processes for everything, that something as critical as an enterprise AI program would lack clarity from the start. So many programs start with the question, how can AI help us, help us compete better in the marketplace, how can it help deepen our engagement and relationships with both internal and external customers and stakeholders, how can it make us more efficient, how can it lower overall operational costs, and help us increase revenue. Many program managers overlook the step of understanding operational challenges in search of a solution, they look at it from the 10K foot elevation instead of building the AI program on the back of a real-world operational inefficiency that’s desperately needed, and currently causing pain within the organization. It’s not good enough to say we think AI can help us do all these great things. Specific operational challenges must be identified, goals must be set that will help evaluate whether the program is a success or failure, and the strategy must align with the business unit impacted by the addressing of the challenge successfully. Most people simply just get started with AI and skip this early, but necessary step. You have failed before you started without clear objectives and operational benchmarks for success. Bell can help.

Where AI Programs Fail – reason number 2, understanding you may not have the right talent in house to properly prove out AI for your enterprise

If you are lucky enough to have avoided AI pitfall number 1, then AI pitfall number 2 may just get you. Perhaps you have a well-defined goal, and a desperately needed challenge solved, and you have the target business unit onboard, with their complete support, and they have confidence in you, and AI’s ability to overcome their challenge, but you still need to find the right talent in the right numbers, across the right disciplines to take on the task to resolution, so you can gain not only user adoption in the business unit, but also continue on the path to an AI First, or Native AI methodology, so you can remain competitive in the future. There is a lot riding on this pilot or first challenge, and based on the statistics, your odds are less than one in three you will pull this off successfully, let alone on schedule and on budget. It may just be the lack of talent that stops your AI program cold in it’s tracks.

You might just have a great IT team in place, but do they have the needed experience, and skillsets that implementing and orchestrating an AI program requires not only in the proof-of-concept stage, but through the program’s entire lifecycle. The answer to that question is probably a resounding no. But even if you can answer this with the affirmative, and demonstrate that you do in fact have the right talent, in the right numbers, across the exact disciplines needed, will your current team have the time necessary to commit and devote the proper time it will take to ensure a successful launch of AI in the enterprise, this is big job, and it demands attention, without it, you are merely dipping your toes in the water, and you might never jump right in, that could spell disaster for your AI program and perhaps your businesses competitive edge because all of your competitors, if not already knee deep in AI, will certainly be so in short order. In fact, recent surveys show that organizations expect dramatic increases over the next few years in the percentage of revenue that is AI influenced. Procrastinators beware.

So, what’s the real challenge here? You don’t know what you don’t know. How would you know if you had the right talent or not? The field of enterprise AI is so new, there are so few people with the broad expertise to determine if you have the right people in place already in your organization. Do you even have the right talent to determine if you have the right talent? Do you have the talent to even get started with aligning organizational challenges, business units, and the AI program? Do you have the right talent to determine if you have the right data, in the right amounts, in the right places to move your AI program along at a reasonable pace? You get the point, traditional IT staff and directors have perhaps dealt with some of these disciplines before, but most likely in silos, not in the open environment it needs to be useful for your AI program in conjunction with any target business unit. Do you have the right talent to bring it all together, to gain the trust and confidence of all the stakeholders so you can get to the hard work of leveraging AI to move your business forward. You see, this is an exercise that requires technical expertise, communication, salesmanship, business strategy and a myriad of other skills to succeed, and you don’t know what to look for, so how would you know you have it already, or not?

Again, even if you somehow can find your way to answering in the affirmative, that you believe you have the right talent in the right amounts and places, you still must ask yourself if those people have the time needed to undertake this critical undertaking? The answer in these demanding times is most likely, no.

Chances are you don’t know what you really need, you thus don’t know where to find it, and hence you can’t really get started until you figure this out. Bell can help.

Where AI Programs Fail – Reason Number 3, The Right Software Platform, The Right Infrastructure, And The Right Data And Access

Knowing that a properly conceived, strategized, implemented, and orchestrated AI program will be bountiful, many organizations just jump right in without painstakingly validating the right AI platform for the job. Many organizations get enamored with feature sets and stop digging before they get to understanding the connection between the business, it’s challenges, ways AI can solve them, their data, access to it, etc. There are however big differences between AI platforms, many of them big. Further complicating the choice is the fact that lot’s of marketing gets in the way of understanding what the true definition of AI is with regards to their platforms. Do you need true conversational AI? Some platforms don’t offer superior conversational AI with NLP, or Natural Language Processing, if you simply want a chatbot for website interaction, perhaps this isn’t required, but I bet you want to stay in lockstep with the competition and that means multi-channel AI so that the business can scale efficiently across, the web, apps, call centers, SMS, text, email, and more. It’s almost the standard these days and that requires conversational AI with Natural Language Processing and multi-language capabilities. You simply can’t live with a chatbot, or even a broadly defined “AI chatbot”, you need so much more.

So why do lots of organizations fail to choose the right platform at the onset? Because they fail to realize that the pilot program is critical to the eventual implementation of AI across the enterprise, they simply choose to prove out a simple task in development and ignore what it will take to roll it out in production across the enterprise. This leads to a scrapping of the AI program as decision makers lose interest, faith, and confidence when they are told a new platform will be needed for production. You need to get this right from the start, in many cases there are no second chances. Bell can help.

Where AI Programs Fail – reason number 4, lack of true data science in the organization

We noted this as one of the talents in the right talent section, but because of its importance, and the fact that in most organizations’ talent, or expertise around data science doesn’t exist, we felt the need to identify it as a single point of failure worthy of its own noting.

Data Science is a critical part of driving AI in the enterprise, and without it you lack the ability to tie organizational challenges to a solution driven by data and powered by AI. Data Science is the glue that holds it all together. Data Science helps companies gain insights from their data that in turn aids in understanding where AI can help in the predictive process enroute to solving both simple and complex challenges. Data Science is the connection between the business unit, data, AI, and the engineers selected to orchestrate the automations, verify them, and continue to monitor for accuracy as time goes on.

One of the perennial top reasons for AI program failure is the lack of organizational data science experience. Bell can help.

Where AI Programs Fail – Reason Number 5, Ignoring Ongoing AI Orchestration, Monitoring, As Well As Data and Process Validation

Enterprise AI programs require ongoing optimization, orchestration, monitoring, and validation of both the automations, and the data sets, to ensure consistently high-quality results. AI isn’t a set it and forget it process, and many programs fall flat once they are in production because ongoing care and feeding hasn’t been accounted for. Data is always changing, user habits are always evolving with regards to usage and expectations as technology evolves, the goal posts are ever changing, this step is critical in maintaining user adoption, as well as increasing the value of AI to the enterprise.

Once again, even if your IT staff and other AI program stakeholders can make room in their busy schedules to deploy AI in the enterprise, have they cleared time in their already full schedules to address this ongoing requirement. Far too many AI pilot, and proof of concept programs fail at the finish line because early planning failed to make allowances for this final step in your journey to AI enablement for your enterprise. Bell can help.

Where AI Programs Fail – reason number 6, realizing at the start you could really benefit from an AI implementation and orchestration partner

There is always a tendency to want to do more in house when it comes to implementing change, after all having the knowledge of your complete AI program residing in house seems like a great idea, until it isn’t. That’s when it all goes wrong, and it’s often too late when this realization occurs, starting over, although necessary, comes with it some loss of credibility, time, budget, and perhaps even the loss of more critical resources such as enterprise confidence, loss of control, etc. These outcomes are simply not acceptable, especially when recent predictions show that AI transition across the world’s leading organizations will take much less time than did their digital transformation. Not surprising.

Why Let Your AI Program Fail Or Flounder – Engage Bell Today for AI Managed Services Across the Enterprise

So, what’s the answer? AI Managed Services from Bell Integration. Whether you are looking to implement AIOps, an AI help desk, AI IT service desk or other AI enabled programs across the enterprise such as AI for HR, AI for customer service, AI for finance, AI for sales and marketing, or others, having an AI implementation and orchestration partner with the right talent, in the right disciplines, combined with great communication and collaboration skills to bring everything together within the enterprise, is invaluable. It very well could be the difference between a successful or failing AI program. It’s no wonder why AI managed services are becoming more sought after by organizations looking for early wins in corporate AI initiatives.

What makes Bell Integration’s AI Managed Services portfolio different from others? Success you can count on in the form of flexible SLA driven outcomes in terms of both costs, and results. Every organization has a different appetite for how they want to consume their investment in AI, and Bell has the flexibility to let your organization consume it’s AI managed service costs in a way that makes sense for you. Mitigate the risk in your AI program implementation and orchestration and avoid the myriad of pitfalls present along the road to an AI First organization by lessening the load with Bell.

Bell Can Certainly Help.