What It Takes to Be AI Ready

Imagine a company precisely forecasting the return on an engagement even before a single line of code is written, or evaluating the success of its offerings from the second-order data of its target audience, or maybe building intuitive systems that can assist its senior line managers. Enticing?

With Gartner anticipating 75% growth in the mainstreaming of artificial intelligence in enterprise operations by as early as 2024, such prescriptive insights will be critical to remain competitive. Here a lab-type approach simplifies the task for decision-makers by estimating whether a planned AI project is aligned with institutional goals, turning a page on the earlier practices of committing substantial expenses and leaving the outcomes to chance.

As a global leader in secure and decarbonized digital, the Atos North American AI Lab aims to weed out the inherent flaws in the conventional try and fail approach to AI by harnessing competent hands, data ascendency and worldwide industry partnerships, demonstrating on-demand the value for proposed business cases. As intelligent automation becomes central to mission success, Jonas Bull, Head of Architecture at the Atos North America AI Lab, explains what it takes to discover transformation opportunities, mitigate investment risks, and gear up reasonably for an AI-first future.  

Data Exploration

The value of machine learning models lies in their efficiency to accurately predict trends and interpret patterns of system behaviors. Bull says: “It can only happen when the underlying algorithms have access to clean and trusted data that can guarantee the reliability of the proof of concepts running on them.”

While that is desirable, Bull observes that actual enterprise data landscapes may be highly fragmented and asymmetric owing to poor hygiene or limited information strategies. Besides structured data that are readily pluggable, unstructured data locked in A/V recordings, CRM notes, handwritten memos, or even opinions by process owners and engineers are also indispensable to enable AI outcomes. 

The Atos AI Lab preempts failure by exploring the origin, availability and usability of the existing datasets and probing the feasibility of business cases through workable prototypes before financial commitments, sometimes leading to unexpected findings.

“Our explorations often reveal interesting scope for process improvement or restructuring and even incompatibility of a given use case with AI, saving money for the customer,” says Bull.

Further, the Atos AI Lab makes the journey cohesive by bringing in digital constructs like natural language processing (NLP), computer vision and advanced OCR to extract the unstructured data and engage across the customer’s leadership spectrum. These technologies help unearth key enablers, expectations and pitfalls, and assert measurable impacts on the bottom line.      

Building Strategy Roadmaps

The relevance of customized strategies to steer affirmative shifts are often underappreciated. However, Bull recommends that the findings from the exploration exercises need to be weaved into coherent AI roadmaps that work best for a particular company. It is a collaborative approach triggered at the beginning of the engagement with the Atos AI Lab, closely studying the customer’s data culture, stakeholder mindsets, operational practices and specific AI requirements, constantly sharing feedback on reaching the desired elevated state through AI implementation.

“We want to help companies make more informed and better decisions, from the tactical level of customer engagement touchpoints up to the corporate level business strategy,” Bull says.

Two recent engagements by the Atos AI Lab further clarify the tangible benefits it can bring to companies in a closely contested market. The first detected flaws in the contractual obligations of a CPG manufacturer with one of its retailers that was likely to have an oversized impact on its relationship with other customers, risking the loss of entire business accounts. The second equipped an energy plant maintenance vendor to predict generation outages from second- and third-order data, allowing it to better market its services. In both, Atos AI Lab combined human intelligence, stakeholder insights, historical data, and older models to develop radical insights, empowering the customers to operate with greater confidence.  

The Business Angle

Implementing AI unlocks a new horizon of improved productivity, operational efficiency and cumulative return on investment, and Bull suggests that companies need AI and machine learning investments to stay relevant. The Atos AI Lab consultants can help customers set the right expectations, keeping them from joining the AI bandwagon, while driving results across the performance metrics of their choice.

Also, kickstarting AI engagements via the lab route mitigates the risk of relying on the wrong levers and undertaking disproportionate CAPEX. “It allows organizations to focus on specific outcomes and understand how they sync with their larger business objectives by orchestrating them within controlled environments,” says Bull.

People are essential to AI

While intelligent automation is blazing new trails in all aspects of business, it is not replacing human knowledge, skills and experience. Instead, lab-centric AI engagements are a decisive step toward improved and practical man-machine convergence that is only possible with a solid grasp on the customer’s end-objectives, hopes and cultural inclinations, albeit at a much reasonable price tag.

As Bull says: “We want companies to help us, help them.” 

For more information, visit Atos Artificial Intelligence.