Managed AI – Overcoming the Barriers to Successful AI May Just Require the Assistance of an Outsourced Managed AI Team
When it comes to your Enterprise AI program you are most likely experiencing significant barriers to success. Today, most Enterprise AI programs fail due to a series of challenges organisations are ill prepared to handle internally but can readily be addressed with the utilisation of professional Managed AI services from Bell Integration.
Managed AI can stop failure 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 organisations 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 organisation, and further, those that have been able to successfully conceive, strategise, 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 organisations 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. Managed AI from Bell can help.
Managed AI can Stop Most AI Programs From Failing Due To Lack Of Real Direction, Objectives, Goals And An Alignment With Operational Challenges
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 organisation. 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. Managed AI From Bell can help.
Managed AI Addresses the Talent Gap Within the Organisation, Removing it as a Barrier to a Successful AI Program Across the Enterprise
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. Managed AI from Bell can help.
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 organisations expect dramatic increases over the next few years in the percentage of revenue that is AI influenced. Procrastinators beware. Managed AI from Bell can help.
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 organisation. 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 organisational 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 or units. 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? Managed AI from Bell can help.
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. But with Managed AI from Bell this won’t ever be an issue.
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. With Managed AI from Bell, these are no longer your concern, we handle everything for you.
Managed AI Provides The Right Software Platform, The Right Infrastructure, And The Right Data And Access Where and When you Need it, and it’s no Longer Your Issues
Knowing that a properly conceived, strategised, implemented, and orchestrated AI program will be bountiful, many organisations just jump right in without painstakingly validating the right AI platform for the job. Many organisations get enamoured 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 lots 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-channel, 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 organisations fail to choose the right platform at the onset? Because they fail to realise 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. With Managed AI the Platform of Choice is Heavily Vetted as Fixed Outcomes, Fixed Costs, and SLA Governed Engagements Drive the Right Choice.
Managed AI Addresses the Data and Application Gaps
We noted this as one of the talents in the right talent section, but because of its importance, and the fact that in most organisations’ 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 organisational 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 organisational data science experience. Bell can Help With Managed AI Services
Managed AI Addresses Ongoing AI Orchestration, Monitoring, As Well As Data and Model Drift
Enterprise AI programs require ongoing optimisation, 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.
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. Managed AI keeps the ball rolling, and rolling, and rolling so you can focus on more important strategic tasks across the business.
Realising At The Start You Could Really Benefit From An AI Implementation And Orchestration Partner – That’s Managed AI
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 internally seems like a great idea, until it isn’t. That’s when it all goes wrong, and it’s often too late when this realisation 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 organisations will take much less time than did their digital transformation. Not surprising. Avoid Failure, Adopt a Managed AI Service Program and Ensure Success at Every Turn.