7 Steps to Scaling Enterprise AI for Maximum Value

7 Steps to Scaling Enterprise AI for Maximum Value

A few years ago, a browser recommendation based on a person’s previous search history constituted AI.

Today, we are able to harness deep learning, neural networking, speech recognition, natural language programming, cloud computing, big data, and more, to create complex, cognitive systems that not only analyze massive data sets and make predictions, but can act on behalf of humans, and even “think” as humans do. All this is happening now in an enterprise near you. Hopefully, in yours too.

AI adoption within enterprises tripled in 2019, and, as Gartner’s Chris Howard put it, “If you are a CIO and your organization doesn’t use AI, the chances are high that your competitors do and this should be a concern.” While all the stats point to AI happening here and now, that does not necessarily help enterprises decide which AI-driven projects to start, scale up or scrap altogether.

Use our seven-step guide to help you navigate how to get the most business value from your enterprise AI investment.

1. Making the biggest impact in the shortest space of time

“AI” encompasses a vast range of technologies. We apply this overall term for a customer service chatbot in a retail context, to a self-driving car, and to cognitive computing, like the researchers who are using cognitive AI to detect brain tumors. Furthermore, there is a lot of choice about how you might want your AI served up. Off the shelf? Bespoke? Sector-specific? Something more generic? Open source? Customizable? Last year, R&D departments were the fastest adopters of AI. So why do so many struggles to get these projects into production? Should you keep investing R&D dollars or look to buy something ready-made? Which business functions should you target first? There’s an AI solution for almost every business function, so how should you choose what to improve first?

If the thought of scaling AI leaves you with more questions than answers, it’s time to cut through the noise. There is only one question you really need to answer: what is your primary source of business value? Answer that, and then look for solutions that will help support that goal. With so much great AI out there, it will require laser focus to keep your primary strategic business goals in front of mind.

2. Encourage the C-suite

AI has risen up the board room agenda in recent years. No longer a topic that the CIO brings to the attention of the rest of the C-suite, AI is becoming the hot trend that finance, legal, marketing, and operations all want supporting their systems. This interest is very welcome and should be harnessed to demonstrate how AI can deliver greater business value. Ultimately, AI is a means to achieving a business goal, which means the leaders of that business need to be right behind its adoption.

3. What’s your enterprise’s vision for AI?

As with any new implementation, short-term gains are crucial. The all-important ROI calculation will be the factor that sways your organization’s decision to invest and ultimately decide its success.  However, just like any other significant investment, short-term wins should also map onto the future direction of travel for AI in your business. Perhaps even more so than other investments given that the future of work will be inexorably linked with artificial intelligence. A good place to start is to align your AI goals with your business roadmap. You can then work on your AI roadmap, in which you can afford to get more granular in terms of the processes, milestones, key investment areas such as technologies, talent, and training.

4. Human + AI

According to Gartner, by 2021, augmented intelligence will create a $2.9 trillion business value. If your enterprise has not done so yet, it is time to consider how AI will change your workplace. What mix of skills will you need in one, three, or five years? Even more pressingly, do you have the skills right now to realize your AI ambitions? For enterprises looking for external help integrating AI into the business, options exist to suit all budgets and meet all needs from individual freelancers to digital engineering services specialists (like Infostretch), to the global services firms.

5. Putting data to work

As important as it is to align AI goals with the overall business strategy, it is equally important to anchor it to a solid data strategy. An enterprise data strategy is closely bound up with how the enterprises see themselves and the world around them. The decisions made on how to collect, store, manage, interpret, share and utilize data are what enable the enterprise to listen, see, think and act. It is no surprise that the role of data has become increasingly pivotal in conferring competitive edge. The AI-based technologies most enterprises are setting their sights on all demand a coherent and efficient approach to data. With so much data generated almost constantly, enterprises need to carefully consider the data points that will really make a difference to them.

6. Getting started

Many enterprises struggle to move out of the POC stage. An effective “production line” combines strategy, tools, people, and processes to get projects up and running. Since enterprises will need to scale AI multiple times in multiple forms, it really is important to iron out any teething problems.  If an enterprise is struggling to move out of the POC stage, especially if it happens repeatedly, it is worth examining the causes. If the problem is technical, there are some great AI tools available that require relatively little customization, so these could be a good place to start. A skills gap could be another cause, in which case it may be time to consider external help to help accelerate the value of the AI investment.  Another factor to consider is team structure: does everyone in the team have a defined role in evolving the product from ideation to maturity?

7. Winning people over

If we want AI systems to take on increasingly “intelligent” decision-making, we need to be comfortable with the choices they are going to make, whether we are employees, customers, patients, partners, or prospective employees. Remember when Amazon had to scrap its AI-enabled recruiting tool because it was discovered to be biased towards women? We all have a stake in ensuring AI is responsible, ethical, legal, and fair. AI and data strategies that comply with industry regulation should be seen as a minimum, and enterprises should expect to take an active role in ensuring they use data and AI fairly and transparently. If a candidate loses out on a job, or has their loan request denied, or is offered a seemingly high insurance premium, they will want to understand why that particular decision was made. Enterprises will need to be able to justify and show how their systems work.

Harnessing AI to deliver business value is one of the biggest digital business challenges facing businesses today. Whether your organization is looking to automate critical business processes, support decision-making, or identify new opportunities, Infostretch can help you harness the power of AI.

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