January 22 by Kingman Tang

As I alluded to in my last blog, 2018 will be the year that Artificial Intelligence (AI) becomes mainstream in the enterprise. You may have made a new year’s resolution or you were mandated by your management to introduce AI into the company, but don’t know how to get started.

Here are 5 ways to take AI from the academic ivory tower and get started applying it to your organization in 2018:

1: Determine Your Outcome Gaps

We strategize, plan, and execute our business plans, but market conditions evolve and we need to stay ahead. Performing a gap analysis between your desired outcome and your actual business outcome is strategically critical for the success of your Artificial Intelligence journey. AI is not a panacea for companies struggling with fundamental structural problems, but done properly, AI will provide insights to propel the enterprise ahead of the competition and delight its customers. I suggest bringing in a trusted advisor to conduct a workshop with your company’s key leaders to determine the gaps, define the destination, and develop a plan to get there.

2: Automate Your Business Processes

It’s the same familiar story in too many enterprises (even the largest ones): they use Microsoft Excel to manage their product catalog, price sheet and quoting, while Microsoft Word is used to redline and negotiate contracts. Order management and billing are done in a separate system (or systems) and are disconnected from the product catalog and pricing in the front end. You get the picture, Quote-to-Cash remains one of the most poorly implemented business processes, riddled with manual steps and sprawled across departments that rely on disparate tools. To change this trajectory, enterprises must take the step to automate its processes, end-to-end, thus laying the foundation for Quote-to-Cash to be one coordinated, efficient process. Through this comprehensive automation, productivity gains are realized from better management of processes, better communication and collaboration among stakeholders, and elimination of manual steps in processes. This clearly sets the stage for a unified data model which AI can draw from.

3: Data Readiness

We’ve all heard the saying, “garbage in, garbage out”, but when it comes to AI insights via machine learning, the quality of your data is paramount. If your data is not structured properly, is not precise, and is not relevant, it doesn’t matter how great your AI technology is – you’ll simply get garbage out for insights. As mentioned in the previous point, automating your processes and hence establishing a common data model, freed from data silos, ensures that you have established a firm foundation.

With the groundwork set, we need to start collecting data for the machine to begin “learning”. The natural question to ask is: How long does it take to start harnessing insights? The answer: That depends on the volume and velocity in which your business generates data. As a rule of thumb, 6-12 months. You can’t rewrite the laws of physics, but you can “bend” them (like the lift force over a winged aircraft overcoming gravity). Likewise, you can overcome this machine learning cold start problem by injecting business rules and/or integrating historical data from other systems like your ERP, to jump start the learning.

Artificial Intelligence

4: Deploy an intelligent agent

As I stated previously, intelligent agents provide a conversational interface to our business applications and are rewriting the rules for how we access our applications. If you have automated your processes, you can immediately deploy an intelligent agent and bypass the “physics problem” mentioned in my prior point. Mundane tasks such as quote look up, quote creation, CRM data entry, contract creation, and more, do not require a vast cache of historical data to begin to “assist” and “guide” your users. By taking this simple step, you’ll increase adoption of your apps, free up time for your sellers to sell, eliminate or curtail the need for product and process training for your users.

5: Take an Agile Approach

I recently spoke with a CIO for a very large windows manufacturer about deploying AI at his company. The advice I gave him was to think of AI like software development and take an agile approach to deployment. That totally resonated and he said that he had this very same conversation with his executive leadership only days before.

Like many transformative initiatives, deploying AI into the enterprise is not a one-time event. Thriving with AI in your organization is a journey. Like most journeys, you have milestones to achieve. Starting small and getting quick wins with Artificial Intelligence is a sure way to build institutional confidence. Measure your results, adjust as necessary, and continue your journey. Deploying an intelligent agent is a quick way to get started with AI, followed by machine learning for your critical Quote-to-Cash processes like for product recommendations, cross-sell/up-sell, or contract risk score prediction.

Having conducted a few enterprise AI workshops, I have seen disparate teams come together, surface challenges that the other team was not aware of, but ended up rallying around one common goal. Contact Apttus to get started with an AI workshop today.

 

Download the Harvard Business Review, Analytic Services report: Artificial Intelligence for Maximizing Revenue to learn more on how AI can impact revenue generation, the Quote-to-Cash process and maximize business outcomes.

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