ARTIFICIAL INTELLIGENCE, ROBOTICS AND AUTOMATION THE BEST OR THE WORST THING EVER TO HAPPEN TO HUMANITY? Contents 1 THE EDGE OF THE PRECIPICE ................................................................. 03 2 WHAT IS AI, RPA AND ROBOTICS? ......................................................... 04 3 TIME FOR A CONTRACT REFRESH.......................................................... 05 02 | Artificial Intelligence, Robotics and Automation THE EDGE OF THE PRECIPICE As Professor Stephen Hawking said[1], we do not yet fully understand and cannot predict the true impact of AI, and yet the race to business and operational transformation via the implementation of digital technologies, such as artificial intelligence (AI) and robotic process automation (RPA), is on an inexorable rise. And whilst there may be some debate as to the socio-economic impact of the rise of the machines and whether they will in time decimate the human race in a form of science fiction disaster movie, for the time being their use is slightly more prosaic. There is no doubt that AI and RPA are here to stay, and businesses, academic institutions and governments are being encouraged to develop their intelligence further, and so it is essential to look to the intelligent future and work to both facilitate innovation, allowing businesses to embrace technology and at the same time mitigate any associated risks. We examine some of the business opportunities and challenges faced, as well as providing our insight on how to manage these issues both in strategic sourcing programmes and in transformative, technology-enabled projects. [1] http://www.cam.ac.uk/research/news/the-best-or-worst-thing-to-happen-to-humanity-stephen-hawking-launches-centre-for-the-future-of www.dlapiper.com | 03 WHAT IS AI, RPA AND ROBOTICS? There is much talk of AI, robotics and RPA, almost on an interchangeable basis. In this paper, these terms are defined as having the following meanings: Artificial Intelligence – technically a field of computer Neural networks – an example of machine science and a phrase coined by John McCarthy in the late learning; a neural network is a connected 1950s, AI is the simulation of human intelligence by machines, network of many simple processors, modelled often sub-divided into ‘strong’ and ‘weak’ AI (strong or hard AI on the human brain. is true human mimicry, often the focus of Hollywood, whereas weak or soft AI is more often focussed on a narrow task). Deep learning – a form of machine learning concerned with the human brain’s function and Machine learning – is the Robotic process structure. ability of a machine to automation (RPA) – improve its performance the use of software to Heuristics – a ‘rule of thumb’, more akin to in the future by analysing perform repeatable or gut feeling (as opposed to algorithms which will previous results. Machine clerical operations, previously guarantee an outcome), used in AI to problem learning is an application performed by a human. solve quickly. of AI. One thing it is important to note is that, in spite of the hype surrounding RPA, it won’t do much by itself “out of the box”. It needs to be taught and it will continue to learn before and after deployment, as indicated in the diagram below. This means that the use of RPA comes with an investment cost and a time requirement that is important to bear in mind when seeking to understand when the issues set out here are likely to manifest. It also goes some way to underlining that the use of RPA requires a relatively long-term investment in order to obtain and maintain the full potential benefits. Training phase “Does this scan indicate cancerous growth?” Training with Testing on Results of testing Testing on ntton no t initial data new data fed into system new data Accuracy 50% 63% 74% Level: Human trained via medical school, on-the-job experience Live use phase after 10+ years of practice has seen several thousand scans Machine combines results from potentially hundreds of thousands of scans, including input from Patient presents CT scan carried out edge cases from the best with symptoms diagnosticians 04 | Artificial Intelligence, Robotics and Automation TIME FOR A CONTRACT REFRESH The potential benefits of implementing AI or RPA within and management of the solution will enable these errors a business can be significant and even transformative for to be recognised, unless the RPA itself can recognise its the commercial well-being of that company – so long as it own errors. is set up to succeed. The use of AI and RPA, particularly in outsourcing deals, can give rise to a number of novel CONFIDENTIALITY AND IP and differently nuanced issues that, if not addressed at the outset, could create some significant issues for the future. Software has been writing software without human intervention for some time. Who owns the resulting new code? Similarly, valuable, derived data from huge raw data SERVICE LEVELS AND FAILURE sets may be sold in much the same way as market data. Broadly speaking, current service level models are Likewise, who owns a new derived data set which has devised to incentivise suppliers to avoid ‘low grade’ issues been created by the machine? that might arise if staff do not follow proper processes. Clearly, any applicable agreement will need to include This is because human beings are by definition fallible terms that deal with the relevant issues. The key here and will be more or less efficient depending upon a large is understanding the likely different outputs that might number of factors. be created as a consequence of the deployment of the This is not the case with AI-based services, which do not AI or RPA technology. It will be important to rethink (or should not) suffer from the same challenges as those the provisions insofar as they relate to matters such as that will likely give rise to human error. Accordingly, configurations, outputs that reflect or are a manifestation of it is not unreasonable to expect improved service levels business rules, and templates generated by the AI or RPA. for processes supported by RPA and in fact, this will There are two particular issues that may require different often be perceived to be one of the key reasons for the treatment – background IP and know-how provisions. implementation of RPA. It is not unusual for customers to agree that The flip side is that if RPA failures occur, there is a far modifications or enhancements to the supplier’s greater risk that the incidents will be catastrophic rather background IP are owned by the supplier (often on the than minor. This is because AI-based systems tend to basis that they are worthless without the underlying work at a demonstrable accuracy level or will, if this product). However, in AI or RPA deployments, this accuracy level cannot be achieved, fail in a significant way category of IP can instead have its own intrinsic value far below the relevant standard. It is far less likely that that the customer ought to consider before letting such systems will degrade by small margins as human- go, because, for example, if used by the supplier or a provided services might. When a defect or error occurs third party it would allow that entity to replicate the its likelihood of repetition and going unseen is increased customer’s business practices (potentially even more beyond that of a human error, as it will likely have been efficiently than the customer) or because it is something “programmed” within the RPA solution and accordingly that the customer will continue to need to own because will become part of the norm. Only continued oversight of the value to the company itself. www.dlapiper.com | 05 Similarly, most customers will agree a know-how clause, HR, REDUNDANCIES AND KNOWLEDGE permitting the supplier to use the knowledge gained TRANSFER by the supplier in the course of providing the services. Those implementing AI and RPA clearly need to understand But this ought to be reconsidered on the basis that it the HR consequences. Transformational programmes will might aquire knowledge, not of humans, but of machines need to address process risks such as collective consultation and software opening up the possibility for the supplier requirements, where failure could potentially delay progress to re-use material and knowledge that the customer or give rise to significant financial penalties. Equally, believes to have been protected. potential redundancies will undoubtedly be a sensitive issue, as well as potentially triggering severance payments. AUDIT AND TECHNOLOGY Newly created roles on the back of change may give rise to Customers often ask for audit rights – especially in redeployment and retraining obligations for those displaced. particular sectors such as financial services where a Both remuneration design and representation structures regulated entity is required to ensure appropriate audit will potentially be impacted and come into play. rights and may incur substantial sanctions from its A particular challenge will be understanding the impact regulators if it cannot audit and monitor the work of its of AI and RPA on the workforce sufficiently to identify service providers. legal obligations and not fall foul of timing issues by failing Such monitoring is easier within the traditional sourcing to comply with any obligations in the required timescales, environment, when a supplier can be audited mainly for example collective consultation processes or filing through a review of documents, reports and procedures. notification of redundancies with competent authorities. Any work done by a human can be checked by another Another difficulty where there is a proposed outsourcing human relatively easily. In the new context of AI and and transformation will be understanding whether or not RPA, it is more difficult to work out how the AI system automatic transfer rules apply such as those under is working (and evolves) through the service.