Enterprise Technology: 6 Steps for Negotiating Winning AI Deals


While emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduce exciting growth opportunities, they also present challenges and additional layers of complexity when sourcing, negotiating, and enabling these technologies.

While emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduce exciting growth opportunities, they also present challenges and additional layers of complexity when sourcing, negotiating, and enabling these technologies.

The hype surrounding this rapidly evolving space can make it seem as if AI providers hold the most power at the negotiation table. After all, the market is ripe with commentary from analysts stating that companies failing to embrace and implement such emerging and disruptive technologies run the risk of losing their competitiveness. But with a mindful approach and acknowledgment of concerns and potential risks, it is possible to negotiate mutually beneficial contracts that are flexible, agile and most importantly, scalable.

These 6 steps will help you lock in winning AI deals.

1. Understand your potential roadmap and use cases

It can be difficult to predict exactly where and how AI technology can be used in the future as it is constantly being developed, but creating a roadmap and identifying your catalog of potential use cases prior to talking to suppliers is a must. Your roadmap will help guide your sourcing efforts, so you can find the provider best suited to your needs and able to scale with your business use cases.

You must also clearly frame your targeted objectives both in your discussions with vendors as well as in the contract. This includes not only a stated performance objective for the AI system but also a definition of what would constitute failure and the legal consequences thereof. For example, in a contract for the use of AI in production management, is the objective to improve performance or reduce specific problems? And what happens if the desired results are not achieved?

2. Understand your vendor’s roadmap and how they will be evolving their product

Once you begin discussions with vendors, be sure to ask questions about how evolved their product currently is and how they got there. What data was used to train their system? What are their plans for growth and how will they achieve that growth?

Asking these types of questions can uncover potential business and security risks and help shape the questions the procurement and legal teams should address in the sourcing process. Understanding the vendor’s roadmap will also help you decide whether they will be able to grow and scale with you.

Gaining insight into the vendor’s growth plans can uncover how they will benefit from your company’s business and where they stand against their competitors. The cutthroat competition among AI rivals means that early adopter companies that want to pilot or deploy ML at scale will see more and more capabilities available at ever-lower prices over time. Remember, the AI providers are benefiting significantly from the use cases you bring forward for trial as well as the vast amounts of data being processed in their platforms. These points should be leveraged to negotiate a better deal.

3. Identify business & security risks

As with any technology implementation, it is important to assess the various risks involved. As technologies become increasingly interconnected, entry points for potential data breaches and risk of potential compliance claims from indirect use also increase. What security measures are in place to protect your data and prevent breaches? How will indirect use be measured and enforced from a compliance standpoint?

Another risk AI is subject to is unintentional bias from developers and the data being used to train the technology. Unlike traditional systems built on specific logic rules, AI systems deal with statistical truths rather than literal truths. That can make it extremely difficult to prove with complete certainty that the system will work in all cases as expected. Lack of verifiability can be a concern in mission-critical applications, such as controlling a nuclear power plant, or when life-or-death decisions are involved.

Who is accountable or liable for incorrect outputs in situations where your business depends on the accuracy of AI and how will those situations be handled?

4. Develop a sourcing and negotiation plan

Using what you gained in the first three steps, develop a sourcing and negotiation plan that focuses on transparency and clearly defined accountability. You should seek to build an agreement that aligns both your company’s and vendor’s roadmaps and addresses data ownership and overall business and security-related risks.

For the development of AI technology, the transparency of the algorithm used for AI purposes is essential so that unintended bias can be addressed. Moreover, it is appropriate that these systems are subjected to extensive testing based on appropriate data sets as such systems need to be “trained” to gain equivalence to human decision making.

Gaining upfront and ongoing visibility into how the systems will be trained and tested will help you hold the AI provider accountable for potential mishaps resulting from their own erroneous data and help ensure the technology is working as planned.

5. Develop a deep understanding of your IP, data, innovation and commercial protections

Another major issue with AI is the intellectual property of the data integrated and generated by an AI product. For an artificial intelligence system to become effective, companies would likely have to supply an enormous quantity of data and invest considerable human and financial resources to guide its learning. Does the supplier of the artificial intelligence system acquire any rights to such data? Can it use what its artificial intelligence system learned in one company’s use case to benefit its other customers? In extreme cases, this could mean that the experience acquired by a system in one company could benefit its competitors.

On the other hand, companies may change their business model based on AI insights they gain from the partnership. This is the case for Rolls Royce who began selling engine insights to airlines, changing its traditional engine manufacturing and distributing business to a service model that uses predictive analytics. If AI is powering your business and product, or if you start to sell a product using AI insights, what commercial protections should you have in place?

6. Lifecycle – Transition, renewal or end of life

Lastly, set milestones for evaluating progress and seek flexible terms that allow for changes to pricing metrics should use cases change. Set guidelines for when and how changes may be communicated, which could prevent AI from accessing or using your data without your knowledge.

You should also obtain a clear protocol for ending the relationship or switching to a new provider. Specifically, what will happen with the data and learnings you and the provider gained over the course of the relationship? What insights are yours to keep and can you easily migrate to another AI vendor? What data is theirs?

Overall, AI will empower companies to grow in very exciting ways. While you don’t want to squash the AI provider’s ability to grow and innovate, you do want to address potential risks and negotiate a well-balanced and mutually beneficial contract. Do not underestimate the value of your data, participation in design thinking sessions, and your contribution to the identification of new and practical AI use cases. All of this is hugely beneficial to the vendor’s success as well, so it should certainly be leveraged in your discussions.

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