By Katie Mowery,
Director, Content Strategy at Clarity Insights

AI adoption is not enough. Preparing to scale AI is essential to success today.

Editors Note: Clarity Insights is a part of Accenture Applied Intelligence

Artificial intelligence is a frequently discussed topic as part of technology considerations - whether for customer service applications, call center improvements, robotics, or predictive modeling. It covers a wide range of capabilities that address numerous department-specific needs through both general or industry-specialized platforms and services. But with more widespread availability, AI can also be more disjointed and less effective, becoming siloed between business units or as new offerings conflict with or become redundant to those already in use.

Companies no longer can applaud themselves on the acceptance or adoption of AI alone; if an investment in AI is still in the proof of concept (POC) stage, the potential for ROI is still largely untapped. Most likely, that means stakeholders are still dipping their toes in the water, reluctant to dedicate the resources needed to leverage the scalability of an AI initiative in the enterprise. Underestimating the amount of time, talent or executive direction that an AI program requires can short circuit the process needed for the real value of AI to be unleashed. 

Moving from adoption to scaling AI is where you’ll want to set your attention. Accenture Applied Intelligence research shows that only 15-20% of companies have made this leap: therein lies the opportunity for businesses to reap the advantages of being a first mover (or at least ahead of the competition). 

But you can’t sit on it long: three out of four execs understand they need to scale AI across the organization to stay competitive—and in business entirely. This knowledge is motivating, and it also means a large majority are preparing to take AI more seriously than ever. How can business leaders get started, today?

  1. Remember that innovations rely on innovators. AI is a team sport, and the more formalized this insight becomes through organizational structure, the better equipped companies will be to scale AI from an operational standpoint. This means moving from IT-led trials to multi-disciplinary teams led by a C-Suite counterpart: Chief AI, Data or Analytics Officer.  
  2. AI = Growth. Based on the research referenced above, 84% of C-Suite respondents believe they must leverage AI to achieve their growth goals; yet 76% also acknowledge they struggle when it comes to scaling AI across the business. The challenge is ripe with opportunity, while also highlighting the important role AI plays: unlike some technology that exists to “keep the lights on”, AI can be a strong revenue driver. When operationalized properly, AI will not only make things run smoother than before, it has the potential to open entirely new lines of business and ways of working that will change how you view your business. 
  3. Make the CEO an evangelist. If you’re looking to double or even triple your return on AI, the CEO must take an active role in setting the strategy of data teams and ensuring their intel maps to company goals—and, vice versa, that goals are informed by data routinely assessed. Not only does CEO involvement in AI initiatives signal commitment to these investments, it opens the door to even greater AI integration: where the business is industrialized, through thousands of AI-based models, a data-driven cultural ethos and AI-informed product innovation, to build a digital platform mindset as the foundation of enterprises well-adapted for the future and for market differentiation.
  4. Go predictive. A big leap is taken when businesses move from using AI to power discrete applications like chatbots or for personalization to algorithms that power everyday decision-making and future forecasting. Trust in data at this level requires a data foundation within your organization—your data lakes full of financial, consumer, marketing and inventory data, accessible in the cloud—to be sophisticated and up to date. Master data management, data governance, and data quality controls must be rock solid for AI to work like it should.
  5. Structure data to tune out noise. What exactly is your core data? And how do you access it, readily? Is it part of routine analysis, at the departmental level and for strategic planning? The Accenture Applied Research report found that, perhaps surprisingly, lack of budget was a challenge that fell to the bottom of the list of obstacles for AI implementation. AI is now business critical, so it’s on the C-Suite radar. Those who allocate fewer funds to it do so because of little organizational structure in place to support it. Those who are most successful at scaling AI confidently manage their data—integrating both internal and external sources—so that other data becomes secondary: well classified to reduce overwhelm and create accurate data sets that leadership learns to rely on. 

No matter where your AI adoption currently stands, its return won’t just “happen” over time and by purchasing the right technology. AI that is built to scale promises much, asking much in response: molding organizations with data at the center instead of the periphery, with teams formally trained and leadership taking active oversight of what’s hidden in the insights that AI surfaces. 

If you want to know where your company falls in its preparation to scale AI, let’s talk.


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